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Author SHA1 Message Date
Alex Cheema f10e6ab1b7 Route generated chunks over transient events 2026-05-03 02:24:36 +01:00
Alex Cheema 7f229425d6 Wire transient router through node lifecycle 2026-05-03 02:19:13 +01:00
Alex Cheema 62fd3ae36c feat: add transient event transport 2026-05-03 02:03:58 +01:00
Alex Cheema c0d4fd7fa7 feat: reconcile worker backoff from state 2026-05-03 02:01:08 +01:00
Alex Cheema 7a25b4186d feat: reconcile api streams from state 2026-05-03 01:58:47 +01:00
Alex Cheema dbc6286066 feat: reconcile custom model cards from state 2026-05-03 01:44:49 +01:00
Alex Cheema e1a79a0918 feat: store custom model cards in state 2026-05-03 01:42:27 +01:00
Alex Cheema ea04549692 feat: bootstrap api state from snapshot 2026-05-03 01:39:54 +01:00
Alex Cheema a2c2a0fc92 feat: bootstrap worker state from snapshot 2026-05-03 01:35:43 +01:00
Alex Cheema 24c56138e3 feat: serve state snapshots from master 2026-05-03 01:18:29 +01:00
Alex Cheema c89caaf87a feat: add snapshot routing types 2026-05-03 01:15:46 +01:00
Alex Cheema 343d5bc6d4 feat: add snapshot receiver 2026-05-03 01:13:48 +01:00
Alex Cheema 0e6a56baee feat: version state snapshots 2026-05-03 01:12:31 +01:00
Alex Cheema f7bdef9f08 feat: allow event router buffer fast-forward 2026-05-03 01:09:09 +01:00
Alex Cheema f792bd5d52 feat: store input chunks in state 2026-05-03 01:06:39 +01:00
Sam BradburyandSam Bradbury 9c6ff4ce95 feat: update rdma_ctl instructions (#1977)
## Motivation

The RDMA setup instructions were missing a step: after booting to
Recovery mode, users need to open Terminal from the Utilities menu
before they can run the `rdma_ctl` command. Without this step, users
following the instructions wouldn't know how to access a terminal in
Recovery mode. This step was already in the README just not in the UI
notifications.

## Changes

Added a missing instruction step — "Open Terminal from the Utilities
menu" — to three instances of the RDMA setup flow in
`dashboard/src/routes/+page.svelte`.

## Why It Works

N/A copy change only. 

## Test Plan

### Manual Testing
Hardware: MacBook Pro M4 Max 48GB

### Automated Testing
No automated tests affected; this is a UI copy change only.

Co-authored-by: Sam Bradbury <sam@consultbradbury.com>
2026-05-01 11:18:57 +00:00
ecohash-coandJordan Miller b26268dfaf fix(macos-app): disable URL response caching for cluster-state polling (#2005)
Fixes #2004.

`ClusterStateService` polls `/state` at 2 Hz via `URLSession.shared`,
which keeps an on-disk `URLCache` attached by default. Every polled
response body gets persisted under `~/Library/Caches/exolabs.EXO/`,
sustaining ~500–620 KB/sec of file-backed memory dirtied — far above
macOS's ~25 KB/sec per-process daily-average baseline. Six
microstackshot reports observed on a single Mac Studio M3 Ultra over
eight days, with one 15-hour run accumulating 34.36 GB of cache writes.

Heaviest stack on every diagnostic report (96–98% of samples):

```
_dispatch_workloop_worker_thread → _dispatch_block_async_invoke2 →
  __CFURLCache::CreateAndStoreCacheNode → write
```

Full diagnostic data and analysis in #2004.

## What changed

`ClusterStateService` now defaults to an ephemeral, non-caching
`URLSession` instead of `URLSession.shared`. Cluster-state responses are
time-sensitive and small; nothing benefits from being cached on disk.

```swift
private static func makeNonCachingSession() -> URLSession {
    let config = URLSessionConfiguration.ephemeral
    config.urlCache = nil
    config.requestCachePolicy = .reloadIgnoringLocalCacheData
    return URLSession(configuration: config)
}
```

The existing per-request `request.cachePolicy =
.reloadIgnoringLocalCacheData` calls are kept as defense in depth — they
only affect read behavior, but harmless to leave alongside the
session-level config.

## Scope

- **Behavioral**: none. Polled requests still go out at the same
cadence; responses still parse the same; no semantic change to any API
surface.
- **Test injection**: the `session:` parameter remains in `init`, so
tests can still inject a custom mock session unchanged.
- **`BugReportService` and other `URLSession.shared` callers**:
untouched. If maintainers prefer an app-wide URLCache disable instead,
happy to switch the approach (issue body has the alternative spelled
out).

## Verification

Verified locally that compiling EXO with this change produces a working
menubar app and `ClusterStateService` continues to fetch state
correctly. After ~30 min of idle polling, no new entries in
`/Library/Logs/DiagnosticReports/EXO_*.diag` and no growth in
`~/Library/Caches/exolabs.EXO/`.

## Test plan
- [ ] Build EXO from this branch on macOS 26.4
- [ ] Launch, let cluster state polling run for 30+ min
- [ ] Confirm no new microstackshot diagnostic reports
- [ ] Confirm `~/Library/Caches/exolabs.EXO/Cache.db*` does not grow

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Jordan Miller <jordan.d.miller@gmail.com>
2026-05-01 10:41:10 +00:00
ciaranbor 8dae3ecb9a A few targeted tweaks to address HF rate limits (#2009)
## Motivation

- exo bursts ~200 HF Hub-API requests on every cold start, blowing past
the anonymous 500-req/5-min budget.
- The existing retry loop catches 429 generically and gives up in ~3s —
well before HF's reset window.
- `file_meta` and `_download_file` had no 429 handling at all (became
`AssertionError`).
- Disk file-list cache was bypassed on every process restart.

## Changes

All in `src/exo/download/download_utils.py` + tests.

- Parse `t=` from HF's `RateLimit` header on 429; sleep `min(t, 300s) +
jitter`.
- Handle 429 at all three call sites (`_fetch_file_list`, `file_meta`,
`_download_file`).
- `n_attempts`: 3 → 5.
- Disk cache now primary across restarts (24h mtime TTL).
- `?recursive=true` instead of N+1 subdir walks.

## Why It Works

`t=<seconds>` is HF's "wait this long and you'll be unblocked" —
sleeping that long lets the window reset. Disk-cache-as-primary plus
recursive listing cuts cold-start Hub-API traffic by ~10×.

## Test Plan

### Manual Testing

MacBook Pro M1 Max. Tripped the real HF 429. Pre-fix: failed in 3.4s.
Post-fix: slept (HF returned `t=158`) and recovered.

### Automated Testing

- New `test_rate_limit_handling.py` (19 tests) — header parsing,
retry-loop behaviour, plus HTTP-level coverage that mocks aiohttp to
return a 429 and asserts each call site raises
`HuggingFaceRateLimitError(retry_after=52.0)`.
- New `TestFileListCacheTTL` in `test_offline_mode.py` — fresh cache
hits, stale cache refetches.
- 421 tests pass; basedpyright / ruff / nix fmt clean.
2026-04-30 18:06:15 +00:00
Alex CheemaandClaude Opus 4.7 fb12b403ea fix(app): tighten Share Bug Report prompt layout (#2008)
## Summary

Follow-ups to #2003 based on feedback that the Share Bug Report window
felt visually weighty: too much padding above and below, and a
description editor that invited an essay rather than a one-liner.

## Changes (one file)

`app/EXO/EXO/Views/BugReportWindowController.swift`:

- **Auto-size the window to its content.** Switched from `NSHostingView`
+ fixed `contentRect: 480x380` + SwiftUI `frame(minHeight: 320)` to
`NSHostingController` with `sizingOptions = [.preferredContentSize,
.minSize]`. The fixed-min combo was centering the form in dead vertical
space.
- **Smaller, lower-pressure editor.** Field is now labeled `Description
(optional)` with a placeholder hint (`What were you doing when it
broke?`) inside the editor. Editor height fixed at 72pt (was 120pt min).
Replaced the long lead-in paragraph and headline with a single one-line
caption between field and buttons: `Diagnostic logs will be uploaded
with your report.`
- **Tighter spacing.** Outer padding 20 -> 16, root spacing 16 -> 12,
prompting-section spacing 12 -> 8.
- **Remove em dash from copy.**

`BugReportService` and the menu wiring are unchanged.

## Test plan

- [ ] Click `Share Bug Report...` from the menu bar.
- [ ] The window opens centered and sized to its content (no big empty
bands top/bottom).
- [ ] Description editor is visibly compact, with the placeholder hint
showing when empty.
- [ ] The optional-ness is conveyed by the field label (no separate help
paragraph).
- [ ] Caption `Diagnostic logs will be uploaded with your report.`
appears in `.caption` style under the editor, above the buttons.
- [ ] Resize the window: persists across re-opens (frame autosave still
works).
- [ ] Send/Cancel/Try Again/Done flows behave the same as before.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 15:10:26 +01:00
Alex CheemaandClaude Opus 4.7 1606e63816 feat(app): open Share Bug Report in a dedicated window (#2003)
## Summary

- Adds a top-level **Share Bug Report…** menu item to the macOS popover
(between *Check for Updates* and *Quit*) with SF Symbol `ladybug`.
- Clicking it opens a dedicated resizable `NSWindow` ("Send a Bug
Report") that hosts the prompting / sending / success / failure flow.
- Removes the description-less duplicate from Settings → Debug Info, and
the dead `debugSection` it nominally lived behind.

## Why

PR #1959 added a user-description prompt to the bug-report flow, but its
trigger lived inside `ContentView.debugSection` — a view that's defined
but never rendered in the body. The path users actually hit was
`SettingsView.sendBugReportButton`, which called
`BugReportService.sendReport(isManual: true)` without ever passing
`userDescription`. So the description prompt was unreachable in the
built app.

## Approach

Per Apple HIG, an action that requires further input before completing
should open a dialog, not transform the menu inline. So:

- Add a top-level menu entry that ends in `…` (HIG: ellipsis indicates
"further input required").
- Move the prompting/sending/success/failure state machine into a
standalone `BugReportWindowController` modeled after the existing
`SettingsWindowController`.
- Single-instance window with frame-autosave name, sensible
`contentMinSize`, resizable, native button layout (`.cancelAction` /
`.defaultAction` keyboard shortcuts), light/dark-mode-correct
`.textBackgroundColor` and `.separatorColor`.
- Auto-focus the description field on open. `Try Again` from failure,
`Open GitHub Issue` + `Done` from success.

## Files

- `app/EXO/EXO/Views/BugReportWindowController.swift` (new) — controller
+ view.
- `app/EXO/EXO/EXOApp.swift` — wire `BugReportWindowController` as a
`@StateObject` and inject as environment object.
- `app/EXO/EXO/ContentView.swift` — replace inline state machine with
menu item that calls `bugReportWindowController.open()`. Remove
now-unused state, helpers, and dead `debugSection`.
- `app/EXO/EXO/Views/SettingsView.swift` — remove duplicate
`sendBugReportButton`, `sendBugReport()`, and related `@State`. Section
"Debug Info" keeps Thunderbolt / interface / RDMA info.

`BugReportService` is unchanged.

## Test plan

- [ ] Open the menu-bar popover → confirm **Share Bug Report…** appears
between *Check for Updates* and *Quit*, with a ladybug icon.
- [ ] Click it → a window titled "Send a Bug Report" appears, centered,
with the description editor focused.
- [ ] Resize the window → size persists across re-opens (frame
autosave).
- [ ] Type a description, press Return → upload succeeds, success card
with **Open GitHub Issue** + **Done** appears.
- [ ] Click **Open GitHub Issue** → browser opens with the description
pre-filled into the issue template.
- [ ] Send with empty description → upload still succeeds.
- [ ] Press Esc from the prompting state → window closes.
- [ ] On failure (e.g., offline) → error card with **Try Again** +
**Close** appears; Try Again returns to the editor with the description
preserved.
- [ ] Open the Settings window → Debug Info section is unchanged except
the Send Bug Report button is gone.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 13:05:07 +01:00
667a3bb0e5 feat: keep-models option when uninstalling EXO (#1997)
## Summary

- Adds a **Keep downloaded models (~/.exo/models)** checkbox to the
macOS uninstall confirmation dialog (Settings → Advanced → Danger Zone).
The full `~/.exo` directory is now removed on uninstall by default; if
the checkbox is checked, `~/.exo/models` is preserved.
- The standalone `app/EXO/uninstall-exo.sh` gains a matching
`--keep-models` flag and the same `~/.exo` cleanup so GUI and CLI flows
stay in sync. Resolves the user home via `$SUDO_USER` since the script
runs under `sudo`.

Previously, "Uninstall EXO" only cleaned up system-level components
(LaunchDaemon, network location, logs, app bundle) and left the entire
`~/.exo` directory behind. Now uninstalling actually removes EXO's user
data, with a one-click opt-out for the (potentially many GB) of
downloaded models.

![Uninstall dialog with new
checkbox](https://raw.githubusercontent.com/exo-explore/exo/703b7fbbf13441217ad2903bb199f07e92af4490/uninstall-dialog.png)

> Note: the rendered icon in the screenshot above is the generic system
folder icon because it was captured from a small standalone Swift binary
(no app bundle / icon resource). When triggered from the actual EXO.app,
the EXO app icon is shown.

## Test plan

- [ ] Build EXO.app locally; open Settings → Advanced → Danger Zone →
Uninstall EXO; confirm the new "Keep downloaded models (~/.exo/models)"
checkbox is present and unchecked by default.
- [ ] Uninstall with the checkbox **checked** → `~/.exo/models/`
survives, all other entries under `~/.exo` are gone, system components
removed, app moved to Trash.
- [ ] Uninstall with the checkbox **unchecked** → `~/.exo` is fully
removed.
- [ ] `sudo app/EXO/uninstall-exo.sh --keep-models` → `~/.exo/models/`
is preserved, the rest of `~/.exo` is removed.
- [ ] `sudo app/EXO/uninstall-exo.sh` (no flag) → `~/.exo` is fully
removed.
- [ ] `app/EXO/uninstall-exo.sh --help` prints usage and exits 0;
unknown args exit 2 with a usage hint.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Evan <evanev7@gmail.com>
2026-04-28 01:06:02 +00:00
Evan Quiney c80b10c013 implement engine abstraction for mlx and mflux (#2000)
refactor for future versions.
2026-04-28 00:58:17 +00:00
Alex CheemaandClaude Opus 4.7 18ffe1df23 fix: uninstall-exo.sh removes both current and legacy bridge scripts (#1998)
## Summary

The standalone `app/EXO/uninstall-exo.sh` only knew about the legacy
filename `disable_bridge_enable_dhcp.sh`. On machines installed with
newer EXO versions, the current `/Library/Application
Support/EXO/disable_bridge.sh` was left behind, and the script then
reported `EXO support directory not empty, leaving in place`.

This PR makes the script try both filenames, removing whichever ones
exist. Tolerates **either**, **both**, or **neither** being present
without erroring.

The Swift `NetworkSetupHelper.makeUninstallScript()` already handles
both paths correctly, so the GUI uninstall flow is unaffected — this is
a script-only fix.

Caught while running an end-to-end uninstall on a real machine for
#1997.

## Test plan

Verified the new block in isolation against all four states:

- [x] both `disable_bridge.sh` and `disable_bridge_enable_dhcp.sh`
present → both removed
- [x] only `disable_bridge.sh` present → removed cleanly
- [x] only `disable_bridge_enable_dhcp.sh` present → removed cleanly
(legacy install)
- [x] neither present → prints the existing "already removed?" warning,
exits 0

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 00:28:12 +00:00
rltakashige f0d1371d89 MLX P/D (#1993)
## Motivation

MLX only prefill server for Apple Silicon
2026-04-28 00:12:42 +00:00
5d10188d3a fix: route by in-flight tasks only — completed tasks were skewing load balance (#1989)
The load balancer counted ALL tasks (Complete, Cancelled, TimedOut,
Failed) instead of only Pending/Running ones. With 138 accumulated tasks
and only 7 active, routing decisions were based on historical
distribution, causing one node to appear permanently 'busier' and
starving the other of work.

Co-authored-by: Adam Durham <adam@example.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-27 16:03:12 +00:00
ciaranbor f2a0db4e23 Extend bench/eval tooling (#1905)
## Motivation

Extend bench/eval tooling with robustness features, streaming support,
and align model configs with vllm eval for reproducible comparisons.

## Changes

- **exo_eval**: Checkpoint/resume (JSONL), instance health monitoring +
early abort, `top_k`/`min_p`/`enable_thinking` params, LCB
`--release-version`/`--offset`
- **exo_bench**: Streaming SSE (`--stream`), Kimi tokenizer fix for
transformers 5.x
- **Both tools**: Auto-detect running instances instead of requiring
`--skip-instance-setup`; `--fresh-instance` to override
- **harness**: SSE streaming client, `find_existing_instance()` shared
helper, removed download timeout, settle-timeout default 0→7200s
- **models.toml**: Added `enable_thinking`, aligned `max_tokens`/temps
with vllm, added new models
- **API**: Streaming SSE for `/bench/chat/completions`

## Why It Works

- Checkpoint/resume uses append-only JSONL + skip-on-load so interrupted
evals resume without re-running completed questions
- Health monitoring races an `asyncio.Event` against API calls for fast
abort when the instance dies
- Auto-detection queries `/state` for existing instances matching the
model ID before attempting placement
- Streaming reuses the existing `generate_chat_stream` infrastructure
from the regular chat endpoint
2026-04-27 16:53:43 +01:00
rltakashigeandEvan 37f6f4f6c2 Add DeepSeek V4 Flash/Pro (#1978)
Wait for upstream merge.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-27 15:20:50 +01:00
Adam DurhamandAdam Durham 48a922fd5c fix: map presence_penalty and frequency_penalty from ChatCompletionRequest (#1991)
Upstream PR #1947 added `presence_penalty` and `frequency_penalty` to
`TextGenerationTaskParams` and the mlx-lm generator call sites, but
missed wiring them up in the API adapter so they were silently dropped
from incoming requests. This fixes the API mapping.

Co-authored-by: Adam Durham <adam@example.com>
2026-04-27 08:58:59 +00:00
rltakashige fd707de30b Add more model cards (#1970) 2026-04-23 15:28:40 +01:00
Alex CheemaandClaude Opus 4.7 45248c5c85 chore(app): hardcode bug report presigned-URL endpoint (#1971)
## Motivation

The bug-report presigned-URL endpoint
(`https://reports.exolabs.net/presigned-urls`) was injected at build
time from the `EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT` GitHub Actions
secret into `Info.plist`, then read at runtime by `BugReportService`. It
isn't actually a secret — the POST body is just `{"keys":[...]}` with no
credential (see `app/EXO/EXO/Services/BugReportService.swift:136-142`),
abuse prevention lives server-side on the lambda, and the URL is already
visible in every publicly-distributed DMG's `Info.plist`. Treating it as
a repo secret added plumbing with no security benefit and broke local
dev builds — hitting **Send Bug Report** on an uncustomised `just
build-app` raised "Bug report endpoint is invalid".

## Changes

- `app/EXO/EXO/Info.plist`: replace
`$(EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT)` with the literal URL.
- `.github/workflows/build-app.yml`: drop the
`EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT` job-level env var and the
xcodebuild build-setting passthrough. No other workflow changes.

Swift code is unchanged — `BugReportService` still reads from
`Info.plist`, which leaves an escape hatch if anyone ever needs to
override via `xcodebuild EXOBugReportPresignedUrlEndpoint=...` without
recompiling.

Follow-up: the `EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT` repo secret can
now be deleted in the GitHub Actions settings UI.

## Why It Works

`Info.plist` variable substitution turns `$(FOO)` into whatever build
setting `FOO` resolves to. CI was setting `FOO` via xcodebuild; local
dev wasn't, so the key resolved to an empty string, which
`BugReportService.fetchPresignedUploadUrls` rejects via the
`!trimmedEndpointString.isEmpty` guard at `BugReportService.swift:131`.
Hardcoding the literal string removes the substitution entirely, so
every build — local or CI — gets the right value.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro (macOS app build via Xcode) -->
- `just build-app` with no extra env vars (reproduces the failure path
on `main`).
- `/usr/libexec/PlistBuddy -c "Print :EXOBugReportPresignedUrlEndpoint"
app/EXO/build/Build/Products/Debug/EXO.app/Contents/Info.plist` →
returns `https://reports.exolabs.net/presigned-urls` (was empty before
this change).
- `open app/EXO/build/Build/Products/Debug/EXO.app` → menubar → **Debug
Info** → **Send Bug Report** → type a description → **Send** → upload
succeeds and the **Create GitHub Issue** button appears (was failing
with "Bug report endpoint is invalid" before).
- Cross-check on the Slack side that the uploaded `report.json` lands
under `reports/YYYY/MM/DD/<ts>/` as before.

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
- No new tests. This is a single-string change to `Info.plist` plus a
workflow cleanup. `nix flake check` in CI verifies formatting/lint for
the rest of the tree.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-23 14:08:16 +00:00
rltakashige 290e3fd927 Keep image cache fresh (temporary fix) (#1961)
## Motivation

When a new node joins, it might not have the cache.



Caveat: 
This is potentially fallible if a new node joins and updates real
topology, but the API topology hasn't caught up with this fact and the
user queues up a new text generation. In practice, there is only a split
second where this is the case, and this is only for users of the
dashboard interface. We should fix this properly after the release.
2026-04-23 11:39:36 +01:00
rltakashige 3894cf134e Fix Gemma 4 E2B TP + DeepSeek V32 thinking parsing (#1967) 2026-04-23 01:50:39 +00:00
Alex CheemaandClaude Opus 4.7 8993ccaf09 feat(app): add friendly context message to bug report prompt (#1959)
## Motivation

When a user clicks **Send Bug Report** in the macOS app, we already give
them the option to add more context via an optional text field. But the
current prompt is just a terse label — `"What's the issue? (optional)"`
— which doesn't tell the user why bothering to fill it in matters. A
friendly one-line explanation increases the chance they'll describe what
went wrong, which is the single most useful signal when we triage the
resulting diagnostic bundle.

## Changes

- `app/EXO/EXO/ContentView.swift`: In the `.prompting` phase of
`sendBugReportButton`, replace the single label with a two-line
hierarchy:
  - Primary: `Tell us what went wrong (optional)`
- Helper: `A quick description of what you were doing and what happened
helps us track down the bug for you.`
- The helper uses `.caption2` + `.secondary` + `.opacity(0.8)` +
`.fixedSize(horizontal: false, vertical: true)` so it stays visually
subordinate and wraps cleanly inside the 340pt popover.

No changes to `BugReportService`, the `user_description` payload, or any
other flow.

## Why It Works

The optional description is already plumbed end-to-end (text editor →
`bugReportUserDescription` state → `BugReportService.sendReport(...,
userDescription:)` → `report.json`'s `user_description` field → GitHub
issue pre-fill). The only gap was user-facing motivation, so this is
purely a copy/layout tweak inside the existing `.prompting` case — no
new state, bindings, or service changes.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro (macOS app build via Xcode) -->
- Build the macOS app in Xcode (`app/EXO/EXO.xcodeproj`) and launch it.
- Open the menubar popover → expand **Debug Info** → click **Send Bug
Report**.
- Verify the new primary label and helper sentence both appear above the
text editor and wrap cleanly within the popover width.
- Leave the field empty → click **Send** → upload should succeed (no
`user_description` in payload, same as before).
- Fill in a description → click **Send** → upload succeeds and the
success card with **Create GitHub Issue** appears; clicking it opens
GitHub with the description pre-filled.
- Click **Cancel** from the prompting state → returns to idle.

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
- No new automated tests. This is a SwiftUI copy/layout change; existing
`EXOTests` are smoke-level and don't cover `ContentView` view bodies,
and UI snapshot tests aren't worth adding for a two-line copy tweak.
- `nix fmt` reports 0 files changed after the edit; `nix flake check` in
CI will verify formatting/lint for the rest of the tree.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 17:39:36 +00:00
Nadeem Hilal Wani 4939fbe995 feat(dashboard): add Pi integration tab (#1925)
## Summary
Adds a new **Pi** tab to the Integrations page (`/#/integrations`)
alongside the existing Claude Code, OpenCode, Codex, OpenClaw, Open
WebUI, n8n, and Firefox tabs.

[pi](https://pi.dev) (`@mariozechner/pi-coding-agent`) is a terminal
coding agent that supports custom OpenAI-compatible providers via
`~/.pi/agent/models.json`.
This tab gives users a copy-pasteable config to wire pi up to their exo
cluster.

## What's in the tab
- **Model selector** (shown when multiple models are running) — picks
the default model for the generated shell command.
- **Models Config card** — generates `~/.pi/agent/models.json`
registering `exo` as a custom provider:
     - `baseUrl` → `<apiUrl>/v1`
     - `api` → `openai-completions`
     - `apiKey` → `"exo"` (placeholder; exo ignores it)
- `compat.supportsDeveloperRole: false` and
`compat.supportsReasoningEffort: false`, per pi docs recommendation for
local OpenAI-compatible servers
- Auto-populates every running model with `id`, `contextWindow` (from
`/v1/models`), and `input: ["text", "image"]` for vision-capable models
- **Shell Command card** — `pi --provider exo --model <model>` for quick
launch.

The tab gracefully falls back to `your-model-id` when no models are
running, matching the behavior of the other tabs.

   ## Usage

   1. `npm install -g @mariozechner/pi-coding-agent`
   2. Paste the generated config into `~/.pi/agent/models.json`
3. Run `pi` and pick an exo model via `/model` — or run the shell
command directly

   ## Changes

- `dashboard/src/routes/integrations/+page.svelte` — adds `"Pi"` to the
`tabs` tuple, `piModel` state, `piModelsJson` + `piShellCommand`
derivations, and the tab content block.

   Single-file, scoped change — no backend or type changes.

   ## Testing

   - `cd dashboard && npm run build` —  builds cleanly
   - `svelte-check` on the edited file — no new errors
- Manually verified the tab renders, the model selector updates the
generated JSON, and the config reflects `/v1/models` capabilities
(vision → `input: ["text","image"]`,
 `context_length` → `contextWindow`).

   ## Screenshots

<img width="1545" height="1236" alt="pi-tab"
src="https://github.com/user-attachments/assets/38aa179f-4ed9-4a1e-9783-d3baa7738263"
/>
2026-04-22 17:29:47 +00:00
rltakashige 73782ecc65 Fix event mutation causing indexed vs event mismatch (#1964)
Fixes small issue with #1957
2026-04-22 16:12:24 +00:00
rltakashige f6e418ed23 Cleanup on #1952 (#1960) 2026-04-22 17:05:49 +01:00
rltakashigeandEvan 7a312a177b Misc fixes: upstream JACCL all_sum, API, etc. + Add Kimi K2.6 (#1952)
## Motivation

This fixes a bunch of observed model quality issues introduced upstream
in JACCL, as well as API issues and prefix cache calculation.


## Test Plan

### Manual Testing
Tested a bunch

### Automated Testing
Added a test, automated eval tool calls on Kimi K2.6, Minimax M2.7, GPT
OSS and Qwen3.6 models.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-22 15:43:27 +00:00
Evan Quiney 0a549f8846 remove layer loading callback (#1890)
first part of modularising the backend is simplifying some of the
control flow. more tbd.
2026-04-22 14:03:31 +01:00
Evan Quiney df332035ef swap camelcasemodels for frozenmodels globally (#1957) 2026-04-22 11:49:25 +00:00
ciaranbor af673845d3 Ignore HF remote repo changes (temporary fix) (#1958)
## Motivation

Fixes #1918. Downloaded model status reverts from "completed" to
"pending" during each download scan. Reproduced with `zai-org/GLM-5.1`.

## Changes

- `coordinator.py`: In the periodic rescan, don't downgrade
already-completed models; fall back to `resolve_existing_model()`
(safetensors weight check) when per-file size check reports incomplete
- New `test_download_status_not_lost.py`: 3 regression tests

## Why It Works

The rescan compares local file sizes against HF's `main` revision. When
HF updates text files (README, jinja, etc.), remote sizes change but
local files still match the old revision — causing a false "incomplete".
The fix uses the safetensors weight check as ground truth instead.

Long-term: pin the downloaded revision SHA rather than always checking
against `main`.

## Test Plan

### Manual Testing

- Mac Studio M3 Ultra with GLM-5.1 downloaded (natural reproduction of
the issue)
- Confirmed GLM-5.1 stays `DownloadCompleted` through multiple rescan
cycles

### Automated Testing

- 3 new tests: completed-not-downgraded, fallback-to-resolve,
genuinely-incomplete-stays-pending
2026-04-22 11:11:01 +01:00
ciaranbor 49670c8624 Handle missing total_size in safetensors index files (#1956)
## Motivation

Image models fail to load after a mid-download instance deletion and
recreation. The system skips the download and crashes with
`FileNotFoundError: No safetensors files found in .../vae`.

## Changes

- Make `ModelSafetensorsIndexMetadata.total_size` optional (`PositiveInt
| None = None`)
- Add null guard in `fetch_safetensors_size`
- Add regression test

## Why It Works

Exolabs quantized image models have safetensors index files with mflux
metadata (`quantization_level`, `mflux_version`) but no `total_size`.
The required `PositiveInt` field caused Pydantic validation to fail,
which was silently swallowed by `except Exception: continue` in
`_scan_model_directory`. This skipped all weight map checks, making
incomplete models appear complete.

## Test Plan

### Manual Testing

- Hardware: Mac Studio
- Before: `CreateRunner → LoadModel` (crash). After: `CreateRunner →
DownloadModel` (correct).

### Automated Testing

- `test_safetensors_index.py`: 3 cases covering missing, valid, and null
metadata
2026-04-21 16:39:14 +01:00
rltakashige fcc3718efb Add sampling defaults (#1947)
## Motivation

Model quality issues

### Manual Testing
TODO
2026-04-21 06:45:33 +00:00
rltakashige 8ccfd7fcb6 Fix some misc build issues (#1948)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-21 07:41:07 +01:00
Evan Quiney 7b416155de bump uv lock for linux builds (#1942)
followup from a rebase issue in #1874
2026-04-20 11:41:40 +00:00
Evan Quiney 93a24748e6 mlx cuda 13 (dgx spark) support (#1874) 2026-04-20 11:49:12 +01:00
Evan Quiney e32829e51d chore: bump versions in line with release (#1941) 2026-04-20 09:39:28 +00:00
rltakashige 09e894dd52 Fix vision models on M5 Pro/Max MacBooks (#1927)
## Motivation

Vision models don't understand images on M5 series MacBooks. The
upstream NAX addmm fix (https://github.com/ml-explore/mlx/pull/3422)
fixes this.

## Why It Works
Same conclusion I came to when I was debugging the issue on an M5 Max.
It works after this fix.

## Test Plan

### Manual Testing
Works for Qwen3.5 27B
2026-04-19 12:55:20 +01:00
rltakashige bf8aacfd41 Improve build CI (#1920)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-17 20:55:15 +00:00
af9e847edb fix: force gc + clear_cache after KV prefix cache eviction (#1832)
## Summary
- After `KVPrefixCache` evicts LRU entries, the MLX Metal buffers stay
allocated until Python's GC runs
- This leaks ~3-4 GB between long-context requests, reducing the
effective context ceiling for back-to-back requests
- Adding `gc.collect()` + `mx.clear_cache()` after eviction frees Metal
buffers promptly

## Test plan
- [x] Measured on 2-node PP cluster with Qwen3.5-397B-A17B-4bit at 63K
context
- [x] Before: 108.88 GB retained after eviction (3.78 GB above baseline)
- [x] After: 105.48 GB retained after eviction (0.38 GB above baseline —
draft model KV + minor overhead)
- [x] `gc.collect()` adds ~2-3ms latency, runs once per eviction cycle
(not per token)
- [ ] Verify with `uv run pytest`

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Adam Durham <adam@example.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-04-17 17:57:32 +00:00
mlpy0 01598960bd Add model card for Qwen3.6-35B-A3B-8bit (#1917)
Adds the 8bit variant missing from #1907 — the safetensors index is now
live on HF.

- `mlx-community/Qwen3.6-35B-A3B-8bit` (~35 GB)

Architectural fields match the existing 4bit/5bit/bf16 cards.
`storage_size.in_bytes` is taken from `metadata.total_size` of the
upstream `model.safetensors.index.json`.
2026-04-17 10:06:23 +00:00
63b8e64715 Add model cards for Qwen3.6-35B-A3B variants (#1907)
## Motivation

`mlx-community` has just published the new **Qwen3.6-35B-A3B**
multimodal MoE family on HuggingFace. Without static model cards exo
doesn't surface these models in the dashboard picker or match its
placement / prefill logic, so users can't one-click launch them. This PR
adds cards for the three quants whose safetensors indexes are already
live on HF (4bit / 5bit / bf16).

## Changes

Three new TOML files in `resources/inference_model_cards/`:

- `mlx-community--Qwen3.6-35B-A3B-4bit.toml` (~19 GB)
- `mlx-community--Qwen3.6-35B-A3B-5bit.toml` (~23 GB)
- `mlx-community--Qwen3.6-35B-A3B-bf16.toml` (~65 GB)

All three share the same architectural fields (`n_layers = 40`,
`hidden_size = 2048`, `num_key_value_heads = 2`, `context_length =
262144`, capabilities `text, thinking, thinking_toggle, vision`,
`base_model = "Qwen3.6 35B A3B"`) — only `model_id`, `quantization`, and
`storage_size.in_bytes` differ between variants.

## Why It Works

- Qwen3.6-35B-A3B reuses the `qwen3_5_moe` architecture
(`Qwen3_5MoeForConditionalGeneration`) — the same one already wired into
exo's MLX runner at `src/exo/worker/engines/mlx/auto_parallel.py:47` via
`Qwen3_5MoeModel`. The architectural fields are taken verbatim from the
HF `config.json.text_config` and match the existing `Qwen3.5-35B-A3B-*`
cards.
- Storage sizes are the exact `metadata.total_size` read from each
variant's `model.safetensors.index.json` on HF, so download progress and
cluster-memory-fit checks are accurate.
- Vision support is flagged in `capabilities`; the `[vision]` block is
auto-detected by `ModelCard._autodetect_vision` from the upstream
`config.json`, so no hand-written vision config is required.
- The card loader (`_refresh_card_cache` in
`src/exo/shared/models/model_cards.py`) globs every `.toml` in
`resources/inference_model_cards/` on startup, so nothing else needs to
change — the `/models` endpoint and the dashboard picker pick them up
automatically.

The `mxfp4` / `mxfp8` / `nvfp4` variants are still uploading upstream
(index JSONs currently 404) and can be added in a follow-up PR once HF
completes.

## Test Plan

### Manual Testing

Hardware: MacBook Pro M4 Max, 48 GB unified memory.

- Built the dashboard, ran `uv run exo`, waited for the API to come up
on `http://localhost:52415`.
- `curl -s http://localhost:52415/models` returns the three new model
ids (`mlx-community/Qwen3.6-35B-A3B-{4bit,5bit,bf16}`) alongside
existing models.
- Opened the dashboard, clicked SELECT MODEL, typed "Qwen3.6" into the
search box. A single **"Qwen3.6 35B A3B"** group appears showing `3
variants (19GB-65GB)`. Expanding it lists the `4bit` / `5bit` / `bf16`
quants with sizes `19GB` / `23GB` / `65GB`, exactly as expected:

![Qwen3.6 35B A3B in model
picker](https://gist.githubusercontent.com/AlexCheema/68c2c02da9450b44968e6b0e0b1d255e/raw/127119f70382353c65a847188e5a2c9013db68d2/qwen36-picker.png)

- Programmatically loaded each TOML via `ModelCard.load_from_path(...)`
and confirmed the parsed fields (layers / hidden / KV heads / context /
quant / base_model / caps / bytes) match what's written in the files.

### Automated Testing

No code paths were touched — these are pure TOML data files that plug
into the existing model-card loader. The existing pytest suite covers
TOML parsing and card serving; adding new TOMLs doesn't require new test
scaffolding. `uv run ruff check` and `nix fmt` are clean.

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Ryuichi Leo Takashige <rl.takashige@gmail.com>
2026-04-16 23:25:26 +01:00
rltakashige 28c797846a Update mlx and mlx lm to latest (#1906)
Just bumping to the very latest upstream versions.
2026-04-16 10:59:33 +00:00
rltakashige 058bb08261 Allow copying on dashboard even on HTTP (#1902)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-15 23:30:12 +01:00
rltakashige 3eead80238 Better environment variables in MacOS app (#1901)
## Motivation

Closes #1858 

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-15 20:14:52 +00:00
rltakashige 87329c80ef Add usage stats to tool calls and handle multiple tool calls correctly (#1899)
## Motivation

Tool calls are usually not end tokens, so they didn't have usage stats.
2026-04-15 19:40:00 +01:00
rltakashige 8cdc833892 Drain tokens silently skipped in thinking parsing (#1898)
## Motivation
Closes #1882
2026-04-15 14:23:07 +00:00
rltakashige 2cd66ae4cf Fix out of order event idx causing fatal crashes (#1894)
## Motivation

<img width="828" height="373" alt="Screenshot 2026-04-14 at 22 56 52"
src="https://github.com/user-attachments/assets/f8f48c1d-68c5-4acc-a6de-9d180672da9d"
/>

if is_new_master=True, _elect_loop creates a new EventRouter before the
worker has receivers. Then, event router runs _run_ext_in and
buf.drain_indexed() will pick off events, even though
self.internal_outbound is not populated fully.

Finally, when the worker does try requesting events, the next event it
receives is not the first event, meaning the worker crashes.

## Changes

Start the event router after all the receivers are registered

## Why It Works

self.internal_outbound is populated before the loop begins.

## Test Plan

### Manual Testing
No more crashes observed in testing (it's actually quite easy to
reproduce the issue if you have one node with this fix but the other
node on main).

I'm convinced this is a fix, at least.
2026-04-15 08:46:02 +00:00
rltakashige 2ecefa0cfe Fix Qwen3-VL and autodetect vision config (#1893)
## Motivation

Qwen3 VL TP doesn't work atm, and vision is not behaving.

## Test Plan

### Manual Testing
Works now.
2026-04-14 23:05:55 +01:00
rltakashige b8eaf707a8 Add gemma 4 tensor parallelism (#1891) 2026-04-14 20:31:59 +01:00
rltakashige 8d81811b89 Try harder to clean up processes nicely (#1889)
## Motivation

Model loading is actually quite reliable now. No need to kill if you
have a slow SSD or it's a massive model; the user can shut the instance
down if necessary.

This was a major cause of signal=9 issues although not the only one (can
happen during inference too?).
The reason signal=9 is so bad is that RDMA will no longer work until
restart if this ever happens.

## Changes

- no more model load timeout
- no more crazy sigkills
- try harder to clean up processes on model shutdown

## Test Plan

### Manual Testing
Tested with some RDMA instances
2026-04-14 16:37:49 +01:00
rltakashige f2709dcde6 Add prefix cache flag to exo bench (#1888)
## Motivation
For using Exo-Bench extensively, there are many cases that we could use
prefix caching to speed up the benchmarks, especially when the focus is
on the token generation.

At the same time, it's very clear that prefix caching decode tokens is
not very useful in most current scenarios. Surprisingly, even for
non-thinking models, the chat template means that a continued
conversation will be formatted such that the existing cache is not
effective.

We already (slightly accidentally) do this for the batch generator - we
should do it for the sequential generator too.

## Changes

We can now speed up exo bench by having a use prefix caching flag. Of
course, for most accurate pp results, it is better to not have it, but
this speeds up tg and large benchmarking significantly.
Updated methodology to match

## Test Plan

### Manual Testing
Tested on many configurations that the difference in results is
negligible, even with multiple --pp options.
2026-04-14 11:12:58 +01:00
ciaranbor 77ffe039b3 Complete responses api usage response field (#1885)
## Motivation

The Responses API usage response was missing `input_tokens_details` and
`output_tokens_details`. The chat completions API already reports these.

## Changes

- Added `InputTokensDetails` (`cached_tokens`) and `OutputTokensDetails`
(`reasoning_tokens`) to `ResponseUsage`
- Extracted shared `_build_response_usage()` helper for both streaming
and non-streaming paths

## Test Plan

### Manual Testing

4-node cluster, `Qwen3-30B-A3B-4bit` — verified both detail objects
present with correct values in streaming and non-streaming responses.

### Automated Testing

13 tests in `test_openai_responses_api.py`.
2026-04-13 17:38:33 +00:00
rltakashige 3f0df404a5 Reduce memory consumption by adding Flash Attention to Qwen3.5 and Gemma 4, and fix RotatingKVCache prefix cache memory leak (#1886)
## Motivation

Part 1 of many memory improvements.

## Changes
As written in the title

## Test Plan

### Manual Testing
Gemma 4 26B cache reduced from 54GB -> 10GB per 100k tokens, Qwen3.5 35B
A3B cache reduced from 21GB every 100000 tokens to 7GB.
2026-04-13 18:32:17 +01:00
Evan Quiney 9b381f7bfe bump and simplify flake (#1866)
seems like stablepkgs swiftfmt works now! also bump macmon to 0.7
2026-04-13 15:45:17 +00:00
Alex Cheema d2f67b5d10 dashboard: group Gemma under Google with proper logo (#1883)
## Motivation

In the dashboard model picker sidebar, the Gemma 4 models were showing
up under a "Gemma" family with the generic fallback tick/checkmark icon
(the default case in `FamilyLogos.svelte`), since no dedicated logo
branch existed for `family === "gemma"`. Every other vendor (Meta,
NVIDIA, OpenAI, DeepSeek, Qwen, …) has its own brand mark.

Gemma is Google's model family, so it should live under a **Google**
bucket that future Google-authored models can join, and it should render
with a proper Google logo in the same style as its neighbors.

## Changes

- `dashboard/src/lib/components/FamilyLogos.svelte`: added a `family ===
"google"` branch rendering a monochrome Google "G" as a single `<path>`
inside the shared `24×24` viewBox with `fill="currentColor"`, matching
the other vendor logos.
- `dashboard/src/lib/components/FamilySidebar.svelte`: added `google:
"Google"` to the `familyNames` display map.
- `dashboard/src/lib/components/ModelPickerModal.svelte`: inserted
`"google"` into the `familyOrder` array (next to `"llama"`) so the
vendor has a deterministic sort position.
- `resources/inference_model_cards/mlx-community--gemma-4-*.toml` (16
files): changed `family = "gemma"` → `family = "google"`. `base_model =
"Gemma 4 …"` is unchanged, so the model titles still read "Gemma".

## Why It Works

The sidebar builds its family list from whatever values appear in
`model.family` across the loaded model cards (`ModelPickerModal.svelte`
`uniqueFamilies`). Renaming the family string on the 16 Gemma cards from
`"gemma"` to `"google"` collapses them into a single "Google" bucket,
and the new logo branch + display-name map entry gives that bucket a
real brand mark and label. All other logos share the same `w-6 h-6 /
viewBox="0 0 24 24" / fill="currentColor"` shape, so inheriting
`text-exo-yellow` / `text-white/50` just works.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro M3 Max -->
- `cd dashboard && npm install && npm run build` — dashboard builds
cleanly.
- `uv run exo`, opened `http://localhost:52415`, clicked **SELECT
MODEL**:
- sidebar shows a **Google** entry with a monochrome Google "G" logo in
the same style as Meta / NVIDIA / etc.
  - old "Gemma" entry with the generic tick is gone.
- clicking **Google** filters to the Gemma 4 variants (e2b / e4b / 26B
A4B / 31B).
- hover/selected color states switch between `text-white/50` and
`text-exo-yellow` correctly.

### Automated Testing
- No new tests — this is a cosmetic grouping/logo change. Existing
dashboard build verifies the Svelte + TS compiles.
2026-04-13 14:08:15 +00:00
8973503322 fix: use configured api_port for IP connectivity probes (#1877)
## Motivation

Fixes #1861

When `--api-port` is set to a non-default value (e.g., `--api-port
55555`), the IP connectivity discovery system still probes peers on the
hardcoded default port 52415. Since the API is not listening on 52415,
all reachability checks fail, the topology reports zero reachable nodes,
and the dashboard shows "No valid configurations for current settings."

## Changes

Thread the configured `api_port` from `Args` through `Worker` into the
reachability probe functions:

- `net_profile.py`: `check_reachability()` and `check_reachable()`
accept an `api_port` parameter (default 52415 for backward
compatibility)
- `worker/main.py`: `Worker` stores `api_port` and passes it to
`check_reachable()`, uses it in `Multiaddr` construction and the mDNS
connection filter
- `main.py`: passes `args.api_port` to the `Worker` constructor

## Why It Works

The `/node_id` endpoint used by reachability probes is served by the
FastAPI app, which binds to `args.api_port`. The probes must use the
same port the API is actually listening on. Before this fix, the port
was hardcoded in three places in `net_profile.py` and `worker/main.py`;
now it uses the value from the CLI flag.

## Test Plan

### Manual Testing
<!-- Hardware: not available for multi-node testing -->
- Verified ruff passes on all changed files
- Code inspection: traced `api_port` flow from `Args.parse()` →
`Node.create()` → `Worker.__init__()` → `_poll_connection_updates()` →
`check_reachable()` → `check_reachability()` → HTTP probe URL

### Automated Testing
- No existing automated tests cover the reachability probe code path
- The new `api_port` parameter defaults to `52415`, so all existing
behavior is preserved when `--api-port` is not specified

---------

Co-authored-by: lawrence3699 <lawrence3699@users.noreply.github.com>
Co-authored-by: Evan <evanev7@gmail.com>
2026-04-13 14:01:42 +00:00
Alex CheemaandClaude Opus 4.6 eb9228615f models: add MiniMax M2.7 cards (#1884)
## Motivation

The mlx-community [MiniMax-M2.7
collection](https://huggingface.co/collections/mlx-community/minimax-m27)
landed but exo didn't have model cards for any of the variants yet, so
they weren't selectable from the dashboard model picker. Adding cards
also makes them discoverable under the existing MiniMax family entry.

## Changes

Added 6 new model cards in `resources/inference_model_cards/`, one per
quant of MiniMax M2.7:

- `mlx-community--MiniMax-M2.7.toml` (bf16, full precision — 457 GB)
- `mlx-community--MiniMax-M2.7-4bit.toml` (128 GB)
- `mlx-community--MiniMax-M2.7-4bit-mxfp4.toml` (121 GB)
- `mlx-community--MiniMax-M2.7-5bit.toml` (157 GB)
- `mlx-community--MiniMax-M2.7-6bit.toml` (185 GB)
- `mlx-community--MiniMax-M2.7-8bit.toml` (243 GB)

All six use `family = "minimax"` and share `base_model = "MiniMax M2.7"`
so they collapse into a single group in the picker with the existing
MiniMax logo. Architecture fields (`n_layers = 62`, `hidden_size =
3072`, `num_key_value_heads = 8`, `context_length = 196608`) were read
from each repo's `config.json`; `storage_size.in_bytes` was summed from
the HF tree API per repo.

`capabilities = ["text", "thinking"]` follows the existing MiniMax M2.5
cards — the chat template always emits `<think>` tags (no toggle),
matching M2.5 behavior.

## Why It Works

Model cards in `resources/inference_model_cards/` are auto-loaded by
`src/exo/shared/models/model_cards.py::get_model_cards`. The dashboard
picker groups by `base_model` and filters by `family`, so sharing both
across all six variants gives a single "MiniMax M2.7" group under the
MiniMax sidebar entry, with the quant variants exposed as selectable
sub-options.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro M3 Max -->
- Ran `uv run python -c "…await get_model_cards()…"` and confirmed all 6
new cards load with `family=minimax`, `base_model="MiniMax M2.7"`, and
correct quant + byte sizes.
- `cd dashboard && npm run build` then `uv run exo`, opened the model
picker → **MiniMax** family → **MiniMax M2.7** group shows all six quant
variants.

### Automated Testing
- No new automated tests — these are data files validated by the
existing Pydantic `ModelCard` schema at load time.

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-13 14:47:09 +01:00
MikkoParkkola 4b13735ea3 build: remove pyinstaller temp artifacts (#1868)
removes PyInstaller `build/` leftovers after `just package`
2026-04-11 11:46:03 +00:00
rltakashigeandEvan 196543ce69 Add Gemma 4 + VLM fixes + thinking parsing updates (#1851)
## Motivation
Add support for Gemma 4, including VLM!

## Changes

- Add auto parallel strategies and model cards for Gemma 4
- Normalise Gemma 4's special Vision Transformer handling to be in line
with the rest of our vision processors.
- Also adds reprs to messages and b64 hashes to prevent log spam.

## Test Plan

### Manual Testing
Tested manually on 4bit E2B and 8bit 26B

### Automated Testing
Model onboarding shows small logit diffs.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-11 12:29:33 +01:00
ciaranbor 6172617b00 add env override to macos app (#1869)
## Motivation

- Let users pass/override arbitrary exo env vars from the macOS app
without a code change.

## Changes

- `ExoProcessController.swift`: `CustomEnvironmentVariable` struct +
`@Published` list persisted to `UserDefaults`, injected into the child
process after built-ins.
- `SettingsView.swift`: new **Environment** tab with add/remove rows,
trim + dedup on save, and a POSIX name validator with a warning badge.

## Why It Works

- Custom vars applied last in `makeEnvironment`, so overriding a
built-in works with no special-casing.

## Test Plan

### Manual Testing

- Set `EXO_LIBP2P_NAMESPACE` via the new UI; confirmed override in
`~/.exo/exo_log/exo.log`.
2026-04-10 17:39:55 +01:00
ciaranbor 93a980a61e just package first builds dashboard (#1867)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-10 16:25:30 +00:00
ciaranbor 2962ebee60 Fix pdf inputs on Safari (#1865)
## Motivation

PDF attachments weren't working on Safari

## Changes

Create async readable stream if none exists

## Why It Works
pdfjs-dist requires an async readable stream internally

## Test Plan

### Manual Testing
pdf attachments now work on Safari, still work on Firefox
2026-04-10 14:59:34 +00:00
rltakashigeandEvan abd75ae06c Truncate long logs with repr (#1854)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-10 15:53:48 +01:00
kaiisfree ee2e505b3c fix: handle BrokenResourceError in download progress callback (#1846)
## Summary
- Wraps progress callback `send()` in try/except to gracefully handle
`BrokenResourceError` when the memory stream is closed
- Prevents unhandled `ExceptionGroup` from crashing the process when the
download consumer disconnects during transfer

## Root Cause
The download progress callback sends updates through an anyio memory
object stream. When the receiving end closes (e.g., client disconnect,
timeout, or task cancellation), `send()` raises `BrokenResourceError`.
Inside an anyio `TaskGroup`, this unhandled exception becomes an
`ExceptionGroup` that propagates up and crashes the coordinator.

## Fix
Catch `BrokenResourceError` (and `ClosedResourceError` for completeness)
in the progress callback and handle gracefully — the download continues
but progress updates are silently dropped for disconnected consumers.

Fixes #1844

🤖 Generated with [Claude Code](https://claude.com/claude-code)
2026-04-10 11:43:42 +00:00
Evan Quiney f2e6b1ef76 prevent some crash loops (#1827)
extension to #1763 that prevents crash looping in some common scenarios.
2026-04-09 11:34:35 +00:00
ciaranbor e2e17eafb7 Fix reasoning_tokens counting for multi-token thinking tag models (#1848)
## Motivation

`reasoning_tokens` is always 0 in usage stats, even when thinking
content streams correctly via `reasoning_content` SSE deltas. The MLX
generators had their own thinking detection comparing individual
detokenized tokens against think tags — this never fires for models
where tags span multiple tokens (e.g. gpt-oss-120b) or are already in
the prompt.

## Changes

- Removed broken per-token thinking detection from `batch_generate.py`
and `generate.py`
- Added `_count_reasoning_tokens` wrapper in `model_output_parsers.py`
that counts `is_thinking=True` responses and patches the total into
Usage on the final response
- Wired it as the outermost stage of `apply_all_parsers`, so it works
regardless of which parser sets `is_thinking`
- Added 3 tests covering `parse_thinking_models` and `parse_gpt_oss`
paths

## Why It Works

The parser pipeline already correctly sets `is_thinking` on each
response. Counting at the output of `apply_all_parsers` means one
counting point that works for all model types, replacing the duplicate
broken logic in two generators.

## Test Plan

### Manual Testing

- 4-node cluster, `mlx-community/gpt-oss-120b-MXFP4-Q8`
- Main branch: `reasoning_tokens: 0` — fix branch: `reasoning_tokens:
25`

### Automated Testing

- 3 new tests: explicit think tags, `starts_in_thinking=True`, and
gpt-oss Harmony analysis channel
2026-04-09 12:29:35 +01:00
ciaranbor b12cd1b186 Cancel SSE keep-alive when instance is deleted (#1828)
## Motivation

When a model instance is deleted (e.g. node disconnect, manual
teardown), any in-flight SSE streaming connections for that instance
hang indefinitely. The API never closes the response stream, so clients
block forever waiting for more chunks.

## Changes

- Listen for `InstanceDeleted` events in the API event loop
- Add `_close_streams_for_instance()` to find and close any active
text/image generation queues tied to tasks on the deleted instance
- Add unit tests covering text gen, image gen, and
unrelated-instance-not-closed scenarios

## Why It Works

When an instance is deleted, we iterate `state.tasks` to find commands
running on that instance, then close and remove their send-side queue
handles. This causes the SSE generator to terminate, unblocking the
client.

## Test Plan

### Manual Testing
- This was causing issues for me on another branch (integration tests).
Including this fix solved the issue

### Automated Testing
- `test_instance_deleted_stream_cleanup.py`: 3 tests covering text gen
cleanup, image gen cleanup, and ensuring unrelated streams are not
affected
2026-04-08 16:14:28 +01:00
mlpy0 62570227ff Catch ClosedResourceError when forwarding chunks to client queues (#1856)
While stress-testing inference with rapid client cancels mid-stream, I
hit a reproducible crash where the entire exo process exits.

When a client cancels a streaming chat completion partway through, its
receive stream gets closed cleanly via its context manager. The producer
in `API._apply_state` then calls `queue.send(event.chunk)`, which raises
`anyio.ClosedResourceError` rather than `BrokenResourceError`. The
existing handler only catches `BrokenResourceError`, so the exception
propagates through the API task group, kills the Node task group, and
the process exits with `EXO Shutdown complete`.

Trace from one of the crashes:

```
File "exo/api/main.py", line 1818, in _apply_state
    await queue.send(event.chunk)
File "anyio/streams/memory.py", line 212, in send_nowait
    raise ClosedResourceError
anyio.ClosedResourceError
```

The fix is to catch `ClosedResourceError` alongside
`BrokenResourceError` in both queue handlers (text and image), so the
dead queue gets dropped and `_apply_state` keeps running for other
in-flight requests.
2026-04-08 08:50:04 +00:00
Alex CheemaandClaude Opus 4.6 645bc20950 Add Fast Synch Enabled toggle to macOS app settings (#1852)
## Motivation

The exo backend already supports `--fast-synch` / `--no-fast-synch` CLI
flags and the `EXO_FAST_SYNCH` environment variable, but there was no
way to toggle this from the macOS app UI. Users who want fast CPU-to-GPU
synchronization for RDMA with Tensor Parallelism had to use CLI flags.

## Changes

- **ExoProcessController.swift**: Added `fastSynchEnabled`
UserDefaults-backed property and pass `EXO_FAST_SYNCH=on` to the exo
process environment when enabled.
- **SettingsView.swift**: Added a "Performance" section to the Advanced
tab with a "Fast Synch Enabled" toggle, an info icon (ⓘ) tooltip
explaining the feature and trade-offs, and a "Save & Restart" button.

## Why It Works

Follows the exact same pattern as the existing `offlineMode` and
`enableImageModels` settings — UserDefaults persistence, `@Published`
property with `didSet`, environment variable passthrough in
`makeEnvironment()`, and pending state with Save & Restart in the
settings UI. The `EXO_FAST_SYNCH=on` value matches what the Python
backend already reads in `main.py`.

## Test Plan

### Manual Testing
<!-- Hardware: macOS app -->
- Open Settings → Advanced tab → verify "Performance" section with "Fast
Synch Enabled" toggle appears
- Hover the ⓘ icon → verify tooltip explains the feature and GPU lock
trade-off
- Toggle on → click "Save & Restart" → verify process restarts with
`EXO_FAST_SYNCH=on` in env
- Close and reopen Settings → verify the toggle state persists
- Verify "Save & Restart" button is disabled when no changes are pending

### Automated Testing
- Existing settings patterns are well-established; no new automated
tests needed for this UI toggle

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 01:04:42 +00:00
rltakashige 5757c27dd5 Add download utility script (#1855)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-08 00:58:39 +00:00
Andrei Cravtov fd5b23281c Workspace tweaks (#1849)
## Changes

Mostly chore changes around vscode and jetbrains workspace settings, and
some basedpyright settings tweaks, to allow direnv to work and nixd
autocomplete with flake parts to work
2026-04-07 17:26:29 +00:00
rltakashige 43b3df45fb Fix BatchGenerator in line with upstream refactor (and prevent Qwen3.5 memory leak) (#1835)
## Motivation

MLX LM has had a massive refactor to their BatchGenerator recently.
Since we'd like new features from MLX LM such as Gemma 4, we need to
update the code to handle this.

Additionally this fixes a significant memory leak in GatedDeltaNet (the
difference is quite substantial, up to 1GB every 1000 tokens, explaining
several memory issues users were facing with Qwen3.5 models)

## Testing
Before
<img width="3146" height="884" alt="image"
src="https://github.com/user-attachments/assets/5af0f55a-393c-4a32-9eed-ae43f1611af4"
/>


After (no memory leak, as one of the changes upstream)
<img width="3190" height="892" alt="image"
src="https://github.com/user-attachments/assets/f0bd128d-fd48-40d4-9bbd-50a564beab14"
/>
2026-04-07 11:50:12 +00:00
mlpy0 24420eb10a Fix reasoning_tokens always reported as 0 for thinking models (#1836)
When `enable_thinking` is set, chat templates (Qwen3, DeepSeek, etc.)
append `<think>` to the prompt. The model starts generating thinking
content directly without emitting a `<think>` token in the output
stream.

Both generators initialized `in_thinking = False` and only set it to
`True` on seeing a `<think>` token in output. Since that token was part
of the prompt, the flag never flipped and `reasoning_tokens` stayed at 0
in the usage response.

Fix: initialize `in_thinking` from `detect_thinking_prompt_suffix()`,
which already exists and is used by `model_output_parsers` for routing
thinking content correctly.
2026-04-05 00:05:18 +00:00
rltakashige 59669c1168 Tighten EXO bench concurrency numbers and explain methodology (#1811)
## Motivation

The timings in the batch generator are a little optimistic; a minor
change is needed to make them more correct.

## Changes

Include the time spent in the API in the generation tps and make sure to
send all requests simultaneously
2026-04-05 00:57:52 +01:00
ciaranbor 1d2ce464dc Allow pausing and deleting active downloads (#1829)
## Motivation

No way to pause active downloads or delete partial/failed downloads from
the dashboard.

## Changes

- **Backend:** Added `POST /download/cancel` endpoint with
`CancelDownloadParams`/`CancelDownloadResponse` types. Wires into the
existing `CancelDownload` command + coordinator handler.
- **Dashboard store:** Added `cancelDownload(nodeId, modelId)` function.
- **Dashboard UI:**
  - Pause + delete buttons on active (downloading) cells
  - Delete button on paused/pending and failed cells
- Extracted duplicated SVG icons into `{#snippet}` blocks (`trashIcon`,
`downloadIcon`, `pauseIcon`, `deleteButton`)
- **Tests:** 3 coordinator-level tests for cancel: active download →
pending, nonexistent → no-op, cancel then resume.

## Why It Works

`CancelDownload` command and coordinator handler already existed — just
needed an HTTP endpoint and dashboard wiring. Delete endpoint already
supported all download states.

## Test Plan

### Manual Testing

Started a model download, paused it. Deleted some paused downloads.
Deleted some ongoing downloads.

### Automated Testing

- `test_cancel_active_download_transitions_to_pending` — cancels
in-progress download, asserts `DownloadPending` event and cleanup
- `test_cancel_nonexistent_download_is_noop` — no events emitted
- `test_cancel_then_resume_download` — restart after cancel works
2026-04-02 15:56:33 +01:00
ciaranborandEvan eb6ae9fd3c Prevent failed instance retries (#1763)
## Motivation

Currently, when a runner fails, the master retries the instance. Most of
the time, this causes a loop over failure. Retries need backoff and a
cap.

## Changes

- src/exo/worker/main.py: Before creating a runner, check an exponential
backoff timer per instance. After EXO_MAX_INSTANCE_RETRIES failures,
send DeleteInstance to permanently remove the instance. Record attempts
on Shutdown; reset on InstanceDeleted.
- src/exo/utils/keyed_backoff.py: Add attempts() method to query retry
count
- src/exo/shared/constants.py: Add EXO_MAX_INSTANCE_RETRIES = 3.

## Why It Works

The worker gates CreateRunner tasks behind a KeyedBackoff, adding
exponential delay (2s base, 30s cap) between retries. After 3 failures
the worker sends DeleteInstance, stopping retries entirely. The backoff
resets when the instance is deleted, so a fresh placement starts clean.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-01 21:03:34 +01:00
rltakashige 4688adb5d2 Support PDFs in dashboard (#1822)
Like ChatGPT does, we now send both the extracted text and the image of
each PDF page.
2026-03-31 18:25:40 +01:00
rltakashige d9ed943034 Fix Nemotron cache leak upstream (#1819)
## Motivation
Nemotron Cascade and Nano failing at long decodes.

## Changes

Fixed upstream, just change pyproject and uv lock here.


## Test Plan
### Automated Testing
Tested with a reproduce script upstream
2026-03-30 16:53:21 +00:00
rltakashige c6815bfdce Only update KV prefix cache on a good cache hit (#1817)
## Motivation

Addresses #1816 

## Changes

Update on min prefix cache > min_prefix_hit_length **and** hit ratio >
_MIN_PREFIX_HIT_RATIO_TO_UPDATE
min_prefix_hit_length = max(1000, system prompt length) -> system
prompts must match exactly.

## Test Plan

### Manual Testing
Test on OpenCode and Claude Code
2026-03-30 15:04:38 +01:00
rltakashige 39c39e8199 Integrations helpers (#1810)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-03-30 14:28:41 +01:00
rltakashige e5cb7b80d0 Add SSE-keepalive to not time out on long prefill on clients (#1803)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-03-30 12:18:38 +01:00
rltakashigeandDavid Hind 635801d515 Add multimodality! (#1802)
## Motivation

Images!

TODO (in a future PR): Add audio and video support.

## Test Plan

### Manual Testing
<img width="2652" height="1900" alt="image"
src="https://github.com/user-attachments/assets/7d3a7137-542f-4f94-9193-2c73b7c4a5ec"
/>

<img width="2770" height="1956" alt="image"
src="https://github.com/user-attachments/assets/e3c3a096-8029-4409-97a6-aca31a9a3f24"
/>
<img width="2738" height="1768" alt="image"
src="https://github.com/user-attachments/assets/d70ea37f-cd1d-4a4c-ad08-3beb9fafa380"
/>

(And batching also works)

---------

Co-authored-by: David Hind <davehind@yahoo.co.uk>
2026-03-30 11:52:19 +01:00
rltakashige 2efbb8ab4f Improve exo harness with path state (#1815)
<img width="3224" height="1476" alt="image"
src="https://github.com/user-attachments/assets/d90a7d8a-9fe5-43a1-a715-1ef7ecc15422"
/>
2026-03-30 11:20:46 +01:00
Evan Quiney c6c5a3e73c feat: /state/paths (#1796)
adds a path option to the /state endpoint, allowing you to query
subfields of state without grabbing the whole blob

## test plan
poking around in the api
2026-03-30 10:10:00 +00:00
ArvidSU 10ef7ec9e8 feat: add Firefox AI sidebar (?q=) support to dashboard (#1814)
This PR builds on https://github.com/exo-explore/exo/pull/1677 to enable
custom prompts sent from Firefox `browser.ml.chat` to EXO dashboard
using URL parameters in sidebar for summary and other browser
interactions. See "Summarize page" example below.

## Summary
- Parse `?q=<encoded prompt>` URL parameter on page load and auto-submit
it as a chat message
- Clean up the URL with `history.replaceState` to prevent re-submission
on refresh
- Defer auto-send until both cluster state and model list are loaded so
model auto-selection works correctly

## Context
Firefox's built-in AI sidebar (`about:config: browser.ml.chat.enabled`)
integrates with chat providers by appending the user's prompt as
`?q=<URL-encoded prompt>`. Previously the exo dashboard ignored this
parameter. Users can now configure `http://localhost:52415` as a Firefox
AI chatbot provider.

See: https://support.mozilla.org/en-US/kb/ai-chatbot

## Technical notes
- Frontend-only change in `dashboard/src/routes/+page.svelte`
- Uses a Svelte `$effect` that reacts to `pendingFirefoxQuery`, `data`
(cluster state), and `models.length` — fires exactly once when all three
are ready
- If no model is selected, `handleAutoSend` auto-picks the best
available model; if no model fits memory, a toast is shown
- If a model is selected but not running, the message is queued until
the model loads

## Testing
```
http://localhost:52415/?q=Hello+world
http://localhost:52415/?q=Summarize+this+page%3A+%5Bpage+title%5D+%5Bpage+url%5D
```

<img width="2056" height="1329" alt="image"
src="https://github.com/user-attachments/assets/74463eb4-ca1a-400d-806a-c19ba93147b9"
/>
2026-03-30 11:02:35 +01:00
Evan Quiney 1e51dc89b0 chore: bump exo-version with release version (#1807)
our pyproject.toml version was 0.3.68 - update to .69 in line with
release!!
2026-03-27 11:47:13 +00:00
Alex CheemaandClaude Opus 4.6 5327bdde84 Fix custom model add requiring two attempts + enlarge sidebar buttons (#1805)
## Motivation

Adding a custom model from the Hub tab shows "Added" toast but the model
doesn't appear in the All tab. You have to add it a second time for it
to work. Also, the "All" button in the model picker sidebar is too small
to read comfortably.

## Changes

**Race condition fix (`src/exo/api/main.py`):**
- Call `add_to_card_cache(card)` directly in `add_custom_model()` after
sending the `ForwarderCommand`, before the API response returns

**Sidebar sizing
(`dashboard/src/lib/components/FamilySidebar.svelte`):**
- Increased sidebar min-width from 72/64px to 80/72px
- Increased "All" icon from `w-5 h-5` to `w-6 h-6`
- Increased all sidebar labels from 9px to 11px

## Why It Works

`POST /models/add` sends a `ForwarderCommand(AddCustomModelCard)` and
returns immediately. The frontend then calls `GET /models` which reads
from `_card_cache`. But the cache was only updated by the worker event
handler after the event round-trips through the master — a race the
frontend almost always loses. By updating the cache directly in the API
handler, `GET /models` immediately reflects the new model. The worker's
later `add_to_card_cache` call is idempotent (dict key assignment).

## Test Plan

### Manual Testing
<!-- Hardware: any Mac -->
- Open model picker → Hub tab → add a custom model → verify it appears
in All tab on the first attempt
- Verify sidebar "All" button and other labels are visually larger and
readable

### Automated Testing
- `uv run basedpyright` passes with 0 errors
- `uv run ruff check` passes

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 17:57:00 -07:00
ciaranbor 15f1b61f4c Rework model storage directory management (for external storage) (#1765)
## Motivation

Replace confusing EXO_MODELS_DIR/EXO_MODELS_PATH with clearer
multi-directory support, enabling automatic download spillover across
volumes.

## Changes

- EXO_MODELS_DIRS: colon-separated writable dirs (default always
prepended, first with enough space wins)
- EXO_MODELS_READ_ONLY_DIRS: colon-separated read-only dirs (protected
from deletion)
- select_download_dir(): picks writable dir by free space
- resolve_existing_model(): unified lookup across all dirs
- is_read_only_model_dir(): path-based read-only detection instead of
hardcoded flag
- Updated coordinator, worker, model cards, tests

## Why It Works

Default dir always included so zero-config behavior is unchanged. Disk
space checked at download time for automatic spillover. Read-only status
derived from path, not hardcoded.

## Test Plan

### Manual Testing

- No env vars set → identical behavior
- EXO_MODELS_DIRS=/Volumes/SSD/models → downloads to external storage
- EXO_MODELS_READ_ONLY_DIRS=/mnt/nfs → models found, deletion blocked

### Automated Testing

- 4 new tests in test_xdg_paths.py (prepend, default-only, overlap,
empty read-only)
- Existing tests updated to patch new constants
2026-03-26 17:46:46 +00:00
Michael HarriganandEvan 9034300163 [Fix] Node hang on reelection (#1801)
## Motivation

During master reelection, `_elect_loop` called `worker.shutdown()` (fire
& forget) then immediately created and started a new Worker.

This caused the old runner subprocess's Metal/GPU teardown to race with
the new worker's startup, resulting in `IOConnectUnmapMemory failed:
kr=0xe00002bc` errors and a full node hang requiring `^C`. Same issue
existed for `DownloadCoordinator`.

## Changes

- Added `anyio.Event`-based `_stopped` signal to `Worker` and
`DownloadCoordinator`, set at the end of their `run()` finally blocks
- Added `wait_stopped()` async method to both classes
- Updated `_elect_loop` to `await wait_stopped()` after calling
`shutdown()` on the old Worker and DownloadCoordinator before creating
replacements

## Why It Works

The old Worker's task group contains the RunnerSupervisor tasks, whose
finally blocks join the runner subprocess (with 5s timeout + SIGTERM +
SIGKILL escalation). By awaiting `wait_stopped()`, we guarantee the old
runner process has fully exited — including GPU memory cleanup — before
a new Worker can start and potentially access the GPU. This eliminates
the race without changing the shutdown mechanics themselves.

## Test Plan

### Manual Testing
Hardware: M4 Pro Mac Mini 24GB + M3 Ultra Mac Studio 96GB, connected via
Thunderbolt

**Repro steps:**
1. Start exo on two nodes with a model sharded across both (e.g.
`Josiefied-Qwen3-14B-abliterated-v3-4bit`)
2. Wait for "runner ready" on both
3. `kill -9` the master node
4. Observe the surviving node's re-election behavior

**Before fix (original crash):**
```
[ 11:02:39.0896AM ] Runner supervisor shutting down
[ 11:02:39.0905AM ] bye from the runner
[ 11:02:39.1052AM ] Stopping Worker
IOConnectUnmapMemory failed: kr=0xe00002bc
IOConnectUnmapMemory failed: kr=0xe00002bc
IOConnectUnmapMemory failed: kr=0xe00002bc
IOConnectUnmapMemory failed: kr=0xe00002bc
^C[ 11:03:45 ] ← hung for over a minute, required manual kill
```

**After fix (clean re-election):**
```
[ 12:15:22.4703PM ] runner loaded
[ 12:15:24.1672PM ] runner ready
[ 12:15:33.5393PM ] Waiting for other campaign to finish
[ 12:15:36.5409PM ] Node elected Master
[ 12:15:36.5413PM ] Unpausing API
```
No `IOConnectUnmapMemory` errors, no hang, no `^C` needed.

### Automated Testing
- No existing tests cover the `_elect_loop` re-election path; this is an
integration-level flow requiring a live router/election/worker stack
- All existing tests pass (307/308, 1 pre-existing Rust binding failure)
- basedpyright: 0 errors, ruff: all checks passed

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-26 17:28:47 +00:00
rltakashige 1d1dfaa1f3 Don't download original/ and metal/ folders from HF (#1800)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-03-26 13:36:07 +00:00
Evan Quiney 7625213df0 fix: enable macmon if preflight fails (#1799)
missed in #1747, issue #1798.

### the issue

we didn't set the memory poll rate after failling the macmon preflight,
only after failing the followups - as we never ran macmon if preflight
failed, we never hit the followup errors etc.

### testing

requires testing on an m5 pro, but the core issue is solved.
2026-03-26 11:35:22 +00:00
Alex CheemaandClaude Opus 4.6 f318f9ea14 Fix macOS build bundling wrong macmon binary (#1797)
## Motivation

PR #1747 fixed macmon support for M5 Pro/Max by pinning the
`swiftraccoon/macmon` fork in `flake.nix`. This works when running from
source (via Nix) but the distributed macOS `.app` build was still broken
on M5 Pro/Max because it was bundling the wrong macmon.

The error on M5 Pro/Max:
```
macmon preflight failed with return code -6: thread 'main' panicked at src/sources.rs:394:41
```

## Changes

- Removed `macmon` from `brew install` in `build-app.yml` — this was
installing the upstream `vladkens/macmon` which doesn't support M5
Pro/Max
- Added a new step that resolves the pinned macmon fork from the Nix dev
shell (same `swiftraccoon/macmon` at rev `9154d23` already defined in
`flake.nix`) and adds it to `$GITHUB_PATH`
- Added a safety `brew uninstall macmon` to ensure no Homebrew macmon
can shadow the pinned version

## Why It Works

PyInstaller bundles macmon via `shutil.which("macmon")`. Previously this
found the Homebrew (upstream) binary. Now it finds the Nix-overlayed
fork that has M5 Pro/Max support, because `$GITHUB_PATH` prepends the
Nix store path before the PyInstaller step runs.

## Test Plan

### Manual Testing
<!-- Hardware: M5 Pro -->
- Trigger a macOS build and verify the bundled macmon is the pinned fork
- Run the built `.app` on M5 Pro/Max and confirm macmon preflight
succeeds

### Automated Testing
- Existing CI build workflow will validate that the macmon binary is
found and bundled correctly

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 09:47:48 +00:00
ciaranbor 30fd5aa1cc Prefer higher % downloaded nodes for API placement previews (#1795)
Follow up to https://github.com/exo-explore/exo/pull/1767

Same thing for placement previews through API
2026-03-25 17:26:31 +00:00
ciaranbor 6de14cfedb Support image generation cancellation (#1774)
## Motivation

Support cancelling image generation, similar to existing support for
cancelling text generation

## Changes

- Dashboard (app.svelte.ts): Wire up AbortController for both
generateImage and editImage API calls. On abort, show "Cancelled"
instead of an error. Clean up the controller in finally.
- Pipeline runner (pipeline/runner.py): Introduce a cancel_checker
callback and NaN-sentinel cancellation protocol for distributed
diffusion:
  - _check_cancellation() - only rank 0 polls the cancel callback
- _send() - replaces data with NaN sentinels when cancelling, so
downstream ranks detect cancellation via _recv_and_check()
  - _recv() / _recv_like() wrappers that eval and check for NaN sentinel
  - After cancellation, drains any pending ring recv to prevent deadlock
  - Skips partial image yields and final decode when cancelled
- Image runner (runner/image_models/runner.py): Deduplicate the
ImageGeneration and ImageEdits match arms into a shared
_run_image_task() method. Thread a cancel_checker closure (backed by the
existing cancel_receiver + cancelled_tasks set) into generate_image().
- Plumbing (distributed_model.py, generate.py): Pass cancel_checker
through the call chain.

## Why It Works

- Rank 0 is the only node that knows about task-level cancellation. When
it detects cancellation, it sends NaN tensors instead of real data.
Higher-order ranks detect the NaN sentinel on recv, set their own
_cancelling flag, and propagate NaN forward
- A drain step after the loop prevents the deadlock case where the last
rank already sent patches that the first would never consume.
- For single-node mode, the loop simply breaks immediately on
cancellation.

## Test Plan

### Automated Testing

New tests in src/exo/worker/tests/unittests/test_image
2026-03-25 16:56:04 +00:00
347 changed files with 23742 additions and 5139 deletions

No files matched your search

+7
View File
@@ -1 +1,8 @@
use flake
# creates .venv if doesn't exist and loads its environment
export VIRTUAL_ENV=".venv"
if ! [ -d "./$VIRTUAL_ENV" ]; then
uv venv
fi
layout python
+124 -4
View File
@@ -32,7 +32,6 @@ jobs:
SPARKLE_ED25519_PRIVATE: ${{ secrets.SPARKLE_ED25519_PRIVATE }}
SPARKLE_S3_BUCKET: ${{ secrets.SPARKLE_S3_BUCKET }}
SPARKLE_S3_PREFIX: ${{ secrets.SPARKLE_S3_PREFIX }}
EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT: ${{ secrets.EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT }}
AWS_REGION: ${{ secrets.AWS_REGION }}
EXO_BUILD_NUMBER: ${{ github.run_number }}
EXO_LIBP2P_NAMESPACE: ${{ github.ref_name }}
@@ -159,7 +158,7 @@ jobs:
fi
- name: Install Homebrew packages
run: brew install just awscli macmon
run: brew install just awscli
- name: Install UV
uses: astral-sh/setup-uv@v6
@@ -239,10 +238,92 @@ jobs:
# Export keychain path for other steps
echo "BUILD_KEYCHAIN_PATH=$KEYCHAIN_PATH" >> $GITHUB_ENV
# ============================================================
# Pre-flight credential / profile validation
# Runs BEFORE the ~16 min build so auth/expiry failures surface in <1 min.
# ============================================================
- name: Validate Apple notarization credentials
env:
APPLE_NOTARIZATION_USERNAME: ${{ secrets.APPLE_NOTARIZATION_USERNAME }}
APPLE_NOTARIZATION_PASSWORD: ${{ secrets.APPLE_NOTARIZATION_PASSWORD }}
APPLE_NOTARIZATION_TEAM: ${{ secrets.APPLE_NOTARIZATION_TEAM }}
run: |
# All-or-nothing: either all three creds are set, or none are.
CRED_COUNT=0
for v in "$APPLE_NOTARIZATION_USERNAME" "$APPLE_NOTARIZATION_PASSWORD" "$APPLE_NOTARIZATION_TEAM"; do
[[ -n "$v" ]] && CRED_COUNT=$((CRED_COUNT + 1))
done
if [[ "$CRED_COUNT" -eq 0 ]]; then
echo "No notarization credentials configured — skipping notarization for this build."
exit 0
fi
if [[ "$CRED_COUNT" -ne 3 ]]; then
echo "ERROR: partial notarization credentials set ($CRED_COUNT/3). Aborting before build."
exit 1
fi
# Cheap, ~5s, auth-only call. Fails instantly with a clear message if
# the app-specific password is stale, wrong team-id, etc.
echo "Verifying Apple notarization credentials via notarytool history..."
if ! xcrun notarytool history \
--apple-id "$APPLE_NOTARIZATION_USERNAME" \
--password "$APPLE_NOTARIZATION_PASSWORD" \
--team-id "$APPLE_NOTARIZATION_TEAM" >/dev/null; then
echo "ERROR: notarytool rejected the provided credentials. Fix before rerunning."
echo "Common causes: app-specific password expired/revoked, wrong team-id,"
echo "Apple ID not on the team, or 2FA not configured for this Apple ID."
exit 1
fi
echo "Apple notarization credentials OK."
- name: Validate provisioning profile expiry
run: |
PROFILE="$HOME/Library/Developer/Xcode/UserData/Provisioning Profiles/EXO.provisionprofile"
if [[ ! -f "$PROFILE" ]]; then
echo "ERROR: provisioning profile not found at $PROFILE"
exit 1
fi
EXPIRY=$(security cms -D -i "$PROFILE" | plutil -extract ExpirationDate raw -o - - 2>/dev/null || true)
if [[ -z "$EXPIRY" ]]; then
echo "WARNING: could not read ExpirationDate from provisioning profile; skipping expiry check."
exit 0
fi
# Try a couple of known plutil date formats. If none parse, skip the check rather
# than risk a false-positive "expired" block on a format we didn't anticipate.
EXPIRY_EPOCH=""
for fmt in "%Y-%m-%dT%H:%M:%SZ" "%Y-%m-%d %H:%M:%S %z" "%Y-%m-%d %H:%M:%S +0000"; do
if parsed=$(date -j -f "$fmt" "$EXPIRY" +%s 2>/dev/null); then
EXPIRY_EPOCH="$parsed"
break
fi
done
if [[ -z "$EXPIRY_EPOCH" ]]; then
echo "WARNING: could not parse ExpirationDate '$EXPIRY'; skipping expiry check."
exit 0
fi
NOW_EPOCH=$(date +%s)
if [[ "$EXPIRY_EPOCH" -le "$NOW_EPOCH" ]]; then
echo "ERROR: provisioning profile expired on $EXPIRY. Regenerate it before rerunning."
exit 1
fi
DAYS_LEFT=$(( (EXPIRY_EPOCH - NOW_EPOCH) / 86400 ))
echo "Provisioning profile valid until $EXPIRY ($DAYS_LEFT days remaining)."
if [[ "$DAYS_LEFT" -lt 14 ]]; then
echo "WARNING: profile expires in under 14 days — regenerate soon."
fi
# ============================================================
# Build the bundle
# ============================================================
- name: Add pinned macmon to PATH
run: |
MACMON_DIR=$(nix develop --command sh -c 'dirname $(which macmon)')
echo "Using macmon from: $MACMON_DIR"
echo "$MACMON_DIR" >> $GITHUB_PATH
# Remove any Homebrew macmon so PyInstaller can't accidentally pick it up
brew uninstall macmon 2>/dev/null || true
- name: Build PyInstaller bundle
run: uv run pyinstaller packaging/pyinstaller/exo.spec
@@ -265,7 +346,6 @@ jobs:
EXO_BUILD_COMMIT="$GITHUB_SHA" \
SPARKLE_FEED_URL="$SPARKLE_FEED_URL" \
SPARKLE_ED25519_PUBLIC="$SPARKLE_ED25519_PUBLIC" \
EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT="$EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT" \
CODE_SIGNING_IDENTITY="$SIGNING_IDENTITY" \
CODE_SIGN_INJECT_BASE_ENTITLEMENTS=YES
mkdir -p ../../output
@@ -298,11 +378,41 @@ jobs:
APPLE_NOTARIZATION_PASSWORD: ${{ secrets.APPLE_NOTARIZATION_PASSWORD }}
APPLE_NOTARIZATION_TEAM: ${{ secrets.APPLE_NOTARIZATION_TEAM }}
run: |
set -o pipefail
cd output
security unlock-keychain -p "$MACOS_CERTIFICATE_PASSWORD" "$BUILD_KEYCHAIN_PATH"
SIGNING_IDENTITY=$(security find-identity -v -p codesigning "$BUILD_KEYCHAIN_PATH" | awk -F '"' '{print $2}')
# Fail fast if notarization creds are partial. All-or-nothing.
CRED_COUNT=0
for v in "$APPLE_NOTARIZATION_USERNAME" "$APPLE_NOTARIZATION_PASSWORD" "$APPLE_NOTARIZATION_TEAM"; do
[[ -n "$v" ]] && CRED_COUNT=$((CRED_COUNT + 1))
done
if [[ "$CRED_COUNT" -ne 0 && "$CRED_COUNT" -ne 3 ]]; then
echo "ERROR: partial Apple notarization credentials set ($CRED_COUNT/3). Aborting."
exit 1
fi
/usr/bin/codesign --deep --force --timestamp --options runtime \
--sign "$SIGNING_IDENTITY" EXO.app
# Pre-flight: verify the signed app BEFORE building DMG and submitting to Apple.
# If this fails, notarization will fail too — cheap way to fail in seconds, not 15 minutes.
echo "===== codesign --verify EXO.app ====="
if ! /usr/bin/codesign --verify --deep --strict --verbose=2 EXO.app; then
echo "ERROR: EXO.app failed codesign verification. Dumping signing status of every executable:"
find EXO.app -type f \( -perm -111 -o -name "*.dylib" -o -name "*.so" -o -name "*.framework" \) -print0 |
while IFS= read -r -d '' f; do
printf -- '--- %s\n' "$f"
/usr/bin/codesign -dv --verbose=2 "$f" 2>&1 | sed 's/^/ /' || true
done
exit 1
fi
# Gatekeeper assessment. A failure here strongly predicts notarization rejection.
echo "===== spctl assessment (predicts notarization outcome) ====="
/usr/bin/spctl -a -vvv -t install EXO.app || echo "WARNING: spctl assessment failed — notarization is likely to fail too."
mkdir -p dmg-root
cp -R EXO.app dmg-root/
ln -s /Applications dmg-root/Applications
@@ -310,12 +420,22 @@ jobs:
hdiutil create -volname "EXO" -srcfolder dmg-root -ov -format UDZO "$DMG_NAME"
/usr/bin/codesign --force --timestamp --options runtime \
--sign "$SIGNING_IDENTITY" "$DMG_NAME"
echo "===== codesign --verify DMG ====="
if ! /usr/bin/codesign --verify --verbose=2 "$DMG_NAME"; then
echo "ERROR: DMG failed codesign verification."
exit 1
fi
if [[ -n "$APPLE_NOTARIZATION_USERNAME" ]]; then
echo "===== notarytool submit ====="
# `|| true` so set -e doesn't abort before we can echo output / fetch the log.
# We rely on the parsed STATUS below to decide pass/fail.
SUBMISSION_OUTPUT=$(xcrun notarytool submit "$DMG_NAME" \
--apple-id "$APPLE_NOTARIZATION_USERNAME" \
--password "$APPLE_NOTARIZATION_PASSWORD" \
--team-id "$APPLE_NOTARIZATION_TEAM" \
--wait --timeout 15m 2>&1)
--wait --timeout 15m 2>&1) || true
echo "$SUBMISSION_OUTPUT"
SUBMISSION_ID=$(echo "$SUBMISSION_OUTPUT" | awk 'tolower($1)=="id:" && $2 ~ /^[0-9a-fA-F-]+$/ {print $2; exit}')
-3
View File
@@ -91,9 +91,6 @@ jobs:
nix build .#metal-toolchain
fi
# Build mlx (depends on metal-toolchain)
nix build .#mlx
- name: Build all Nix outputs
run: |
nix flake show --json | jq -r '
+2
View File
@@ -38,3 +38,5 @@ bench/**/*.json
# tmp
tmp/models
/build/exo
/.claude/skills
-31
View File
@@ -1,31 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="EMPTY_MODULE" version="4">
<component name="FacetManager">
<facet type="Python" name="Python facet">
<configuration sdkName="Python 3.13 virtualenv at ~/Desktop/exo/.venv" />
</facet>
</component>
<component name="Go" enabled="true" />
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<sourceFolder url="file://$MODULE_DIR$/scripts/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/exo_pyo3_bindings/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/exo_pyo3_bindings/tests" isTestSource="true" />
<sourceFolder url="file://$MODULE_DIR$/rust/util/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/networking/examples" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/networking/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/networking/tests" isTestSource="true" />
<sourceFolder url="file://$MODULE_DIR$/rust/system_custodian/src" isTestSource="false" />
<excludeFolder url="file://$MODULE_DIR$/.venv" />
<excludeFolder url="file://$MODULE_DIR$/.direnv" />
<excludeFolder url="file://$MODULE_DIR$/build" />
<excludeFolder url="file://$MODULE_DIR$/dist" />
<excludeFolder url="file://$MODULE_DIR$/.go_cache" />
<excludeFolder url="file://$MODULE_DIR$/rust/target" />
</content>
<orderEntry type="jdk" jdkName="Python 3.13 (exo)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
<orderEntry type="library" name="Python 3.13 virtualenv at ~/Desktop/exo/.venv interpreter library" level="application" />
</component>
</module>
+1 -1
View File
@@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ExternalDependencies">
<plugin id="systems.fehn.intellijdirenv" />
<plugin id="al.aoli.intellijdirenv" />
</component>
</project>
-14
View File
@@ -1,14 +0,0 @@
<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyCompatibilityInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ourVersions">
<value>
<list size="1">
<item index="0" class="java.lang.String" itemvalue="3.14" />
</list>
</value>
</option>
</inspection_tool>
</profile>
</component>
-3
View File
@@ -4,7 +4,4 @@
<option name="sdkName" value="Python 3.13 (exo)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (exo)" project-jdk-type="Python SDK" />
<component name="PythonCompatibilityInspectionAdvertiser">
<option name="version" value="3" />
</component>
</project>
+6 -5
View File
@@ -1767,12 +1767,12 @@ def clip(
array: The clipped array.
"""
def compile(
fun: Callable,
def compile[F: Callable[..., object]](
fun: F,
inputs: object | None = ...,
outputs: object | None = ...,
shapeless: bool = ...,
) -> Callable:
) -> F:
"""
Returns a compiled function which produces the same output as ``fun``.
@@ -2915,8 +2915,8 @@ def gather_mm(
a: array,
b: array,
/,
lhs_indices: array,
rhs_indices: array,
lhs_indices: array | None = ...,
rhs_indices: array | None = ...,
*,
sorted_indices: bool = ...,
stream: Stream | Device | None = ...,
@@ -4707,6 +4707,7 @@ def softmax(
/,
axis: int | Sequence[int] | None = ...,
*,
precise: bool = ...,
stream: Stream | Device | None = ...,
) -> array:
"""
+4
View File
@@ -57,6 +57,10 @@ class Module(dict):
def __init__(self) -> None:
"""Should be called by the subclasses of ``Module``."""
def __getitem__(self, key: str) -> mx.array | Module: ...
def get(
self, key: str, default: mx.array | Module | None = ...
) -> mx.array | Module | None: ...
@property
def training(self): # -> bool:
"""Boolean indicating if the model is in training mode."""
+5 -3
View File
@@ -2,7 +2,7 @@
This type stub file was generated by pyright.
"""
from typing import Callable, Optional, Union
from typing import Any, Callable, Optional, Union
import mlx.core as mx
from base import Module
@@ -13,8 +13,10 @@ def quantize(
bits: int = ...,
*,
mode: str = ...,
class_predicate: Optional[Callable[[str, Module], Union[bool, dict]]] = ...,
): # -> None:
class_predicate: Optional[
Callable[[str, Module], Union[bool, dict[str, Any]]]
] = ...,
) -> None:
"""Quantize the sub-modules of a module according to a predicate.
By default all layers that define a ``to_quantized(group_size, bits)``
+5
View File
@@ -0,0 +1,5 @@
"""
This type stub file was generated by pyright.
"""
__version__ = ...
+282 -95
View File
@@ -3,13 +3,12 @@ This type stub file was generated by pyright.
"""
import contextlib
from dataclasses import dataclass
from typing import Any, Callable, Generator, List, Optional, Tuple, Union
import mlx.core as mx
import mlx.nn as nn
from dataclasses import dataclass
from collections import deque
from typing import Any, Callable, Generator, List, Optional, Sequence, Tuple, Union
from transformers import PreTrainedTokenizer
from .tokenizer_utils import TokenizerWrapper
DEFAULT_PROMPT = ...
@@ -29,6 +28,7 @@ def str2bool(string): # -> bool:
...
def setup_arg_parser(): # -> ArgumentParser:
"""Set up and return the argument parser."""
...
generation_stream: mx.Stream
@@ -43,6 +43,7 @@ def wired_limit(
async eval could be running pass in the streams to synchronize with prior
to exiting the context manager.
"""
...
@dataclass
class GenerationResponse:
"""
@@ -91,7 +92,7 @@ def generate_step(
kv_bits: Optional[int] = ...,
kv_group_size: int = ...,
quantized_kv_start: int = ...,
prompt_progress_callback: Optional[Callable[[int], int]] = ...,
prompt_progress_callback: Optional[Callable[[int, int], None]] = ...,
input_embeddings: Optional[mx.array] = ...,
) -> Generator[Tuple[mx.array, mx.array], None, None]:
"""
@@ -117,7 +118,7 @@ def generate_step(
kv_group_size (int): Group size for KV cache quantization. Default: ``64``.
quantized_kv_start (int): Step to begin using a quantized KV cache.
when ``kv_bits`` is non-None. Default: ``0``.
prompt_progress_callback (Callable[[int], int]): A call-back which takes the
prompt_progress_callback (Callable[[int, int], None]): A call-back which takes the
prompt tokens processed so far and the total number of prompt tokens.
input_embeddings (mx.array, optional): Input embeddings to use instead of or in
conjunction with prompt tokens. Default: ``None``.
@@ -125,6 +126,7 @@ def generate_step(
Yields:
Tuple[mx.array, mx.array]: One token and a vector of log probabilities.
"""
...
def speculative_generate_step(
prompt: mx.array,
@@ -170,6 +172,7 @@ def speculative_generate_step(
Tuple[mx.array, mx.array, bool]: One token, a vector of log probabilities,
and a bool indicating if the token was generated by the draft model
"""
...
def stream_generate(
model: nn.Module,
@@ -177,7 +180,7 @@ def stream_generate(
prompt: Union[str, mx.array, List[int]],
max_tokens: int = ...,
draft_model: Optional[nn.Module] = ...,
**kwargs: object,
**kwargs: Any,
) -> Generator[GenerationResponse, None, None]:
"""
A generator producing text based on the given prompt from the model.
@@ -199,6 +202,7 @@ def stream_generate(
GenerationResponse: An instance containing the generated text segment and
associated metadata. See :class:`GenerationResponse` for details.
"""
...
def generate(
model: nn.Module,
@@ -219,6 +223,9 @@ def generate(
kwargs: The remaining options get passed to :func:`stream_generate`.
See :func:`stream_generate` for more details.
"""
...
def _merge_caches(caches: List[List[Any]]) -> List[Any]: ...
@dataclass
class BatchStats:
"""
@@ -242,10 +249,263 @@ class BatchStats:
generation_time: float = ...
peak_memory: float = ...
class SequenceStateMachine:
"""A state machine that uses one Aho-Corasick trie per state to efficiently
track state across a generated sequence.
The transitions are provided as state -> [(sequence, new_state)].
Example:
sm = SequenceStateMachine(
transitions={
"normal": [
(think_start_tokens, "reasoning"),
(tool_start_tokens, "tool"),
(eos, None),
],
"reasoning": [
(think_end_tokens, "normal"),
(eos, None),
],
"tool": [
(tool_end_tokens, None),
(eos, None)
],
},
initial="normal"
)
"""
def __init__(self, transitions=..., initial=...) -> None: ...
def __deepcopy__(self, memo): # -> SequenceStateMachine:
...
def make_state(self): # -> tuple[str, Any, dict[Any, Any]]:
...
@staticmethod
def match(state, x): # -> tuple[tuple[Any, Any | None, Any], Any | None, Any]:
...
class PromptProcessingBatch:
"""
A batch processor for prompt tokens with support for incremental processing.
This class handles batched prompt processing, managing KV caches and preparing
tokens for generation. It supports extending, filtering, and splitting batches.
"""
@dataclass
class Response:
uid: int
progress: tuple
end_of_segment: bool
end_of_prompt: bool
...
def __init__(
self,
model: nn.Module,
uids: List[int],
caches: List[List[Any]],
tokens: Optional[List[List[int]]] = ...,
prefill_step_size: int = ...,
samplers: Optional[List[Callable[[mx.array], mx.array]]] = ...,
fallback_sampler: Optional[Callable[[mx.array], mx.array]] = ...,
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
] = ...,
state_machines: Optional[List[SequenceStateMachine]] = ...,
max_tokens: Optional[List[int]] = ...,
) -> None: ...
def __len__(self): # -> int:
...
def extract_cache(self, idx: int) -> List[Any]: ...
def extend(self, batch): # -> None:
...
def split(self, indices: List[int]): # -> Self:
...
def filter(self, keep: List[int]): # -> None:
...
def prompt(self, tokens: List[List[int]]): # -> None:
"""
Process prompt tokens through the model.
Args:
tokens: List of token sequences to process.
"""
...
def generate(self, tokens: List[List[int]]): # -> GenerationBatch:
"""
Transition from prompt processing to generation.
Args:
tokens: Final tokens for each sequence to start generation.
Returns:
A GenerationBatch ready for token generation.
"""
...
@classmethod
def empty(
cls,
model: nn.Module,
fallback_sampler: Callable[[mx.array], mx.array],
prefill_step_size: int = ...,
): # -> Self:
...
class GenerationBatch:
"""
A batched token generator that manages multiple sequences in parallel.
This class handles the generation phase after prompt processing, managing
KV caches, sampling, and stop sequence detection for multiple sequences.
"""
@dataclass
class Response:
uid: int
token: int
logprobs: mx.array
finish_reason: Optional[str]
current_state: Optional[str]
match_sequence: Optional[List[int]]
prompt_cache: Optional[List[Any]]
all_tokens: Optional[List[int]]
...
model: nn.Module
uids: List[int]
prompt_cache: List[Any]
tokens: List[List[int]]
samplers: Optional[List[Callable[[mx.array], mx.array]]]
fallback_sampler: Callable[[mx.array], mx.array]
logits_processors: Optional[List[List[Callable[[mx.array, mx.array], mx.array]]]]
state_machines: List[SequenceStateMachine]
max_tokens: List[int]
_current_tokens: Optional[mx.array]
_current_logprobs: mx.array | List[mx.array]
_next_tokens: Optional[mx.array]
_next_logprobs: mx.array | List[mx.array]
_token_context: List[Any]
_num_tokens: List[int]
_matcher_states: List[Any]
def __init__(
self,
model: nn.Module,
uids: List[int],
inputs: mx.array,
prompt_cache: List[Any],
tokens: List[List[int]],
samplers: Optional[List[Callable[[mx.array], mx.array]]],
fallback_sampler: Callable[[mx.array], mx.array],
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
],
state_machines: List[SequenceStateMachine],
max_tokens: List[int],
) -> None: ...
def __len__(self) -> int: ...
def extend(self, batch: GenerationBatch) -> None: ...
def extract_cache(self, idx: int) -> List[Any]: ...
def filter(self, keep: List[int]) -> None: ...
def _step(self) -> Tuple[List[int], List[mx.array]]: ...
def next(self) -> List[Response]:
"""
Generate the next batch of tokens.
Returns:
List of Response objects for each sequence in the batch.
"""
...
@classmethod
def empty(
cls, model: nn.Module, fallback_sampler: Callable[[mx.array], mx.array]
): # -> Self:
...
class BatchGenerator:
"""
A batch generator implements continuous batching.
This class provides automatic management of prompt processing and generation
batches, handling the transition between the two.
It also allows for segmented prompt processing which guarantees that the
generator will stop at these boundaries when processing an input.
"""
def __init__(
self,
model: nn.Module,
max_tokens: int = ...,
stop_tokens: Optional[Sequence[Sequence[int]]] = ...,
sampler: Optional[Callable[[mx.array], mx.array]] = ...,
logits_processors: Optional[
List[Callable[[mx.array, mx.array], mx.array]]
] = ...,
completion_batch_size: int = ...,
prefill_batch_size: int = ...,
prefill_step_size: int = ...,
) -> None: ...
def close(self) -> None: ...
def __del__(self): # -> None:
...
@contextlib.contextmanager
def stats(self, stats=...): # -> Generator[Any | BatchStats, Any, None]:
...
_unprocessed_sequences: deque[tuple[Any, ...]]
_prompt_batch: PromptProcessingBatch
_generation_batch: GenerationBatch
_currently_processing: list[Any]
_gen_tokens_counter: int
_steps_counter: int
def _next(
self,
) -> tuple[
List[PromptProcessingBatch.Response], List[GenerationBatch.Response]
]: ...
def insert(
self,
prompts: List[List[int]],
max_tokens: Optional[List[int]] = ...,
caches: Optional[List[List[Any]]] = ...,
all_tokens: Optional[List[List[int]]] = ...,
samplers: Optional[List[Callable[[mx.array], mx.array]]] = ...,
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
] = ...,
state_machines: Optional[List[SequenceStateMachine]] = ...,
) -> List[int]: ...
def insert_segments(
self,
segments: List[List[List[int]]],
max_tokens: Optional[List[int]] = ...,
caches: Optional[List[List[Any]]] = ...,
all_tokens: Optional[List[List[int]]] = ...,
samplers: Optional[List[Callable[[mx.array], mx.array]]] = ...,
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
] = ...,
state_machines: Optional[List[SequenceStateMachine]] = ...,
) -> List[int]: ...
def extract_cache(self, uids: List[int]) -> dict[int, Any]: ...
def remove(
self, uids: List[int], return_prompt_caches: bool = ...
) -> dict[int, Any]: ...
@property
def prompt_cache_nbytes(self) -> int: ...
def next(
self,
) -> tuple[
List[PromptProcessingBatch.Response], List[GenerationBatch.Response]
]: ...
def next_generated(self) -> List[GenerationBatch.Response]: ...
@dataclass
class BatchResponse:
"""
An data object to hold a batch generation response.
A data object to hold a batch generation response.
Args:
texts: (List[str]): The generated text for each prompt.
@@ -255,98 +515,17 @@ class BatchResponse:
texts: List[str]
stats: BatchStats
caches: Optional[List[List[Any]]]
def _left_pad_prompts(prompts: Any, max_length: Optional[int] = ...) -> mx.array: ...
def _right_pad_prompts(prompts: Any, max_length: Optional[int] = ...) -> mx.array: ...
def _make_cache(
model: Any, left_padding: Any, max_kv_size: Optional[int]
) -> List[Any]: ...
def _merge_caches(caches: Any) -> List[Any]: ...
@dataclass
class Batch:
uids: List[int]
y: mx.array
logprobs: List[mx.array] | mx.array
max_tokens: List[int]
num_tokens: List[int]
cache: List[Any]
samplers: List[Callable[[mx.array], mx.array] | None]
logits_processors: List[List[Callable[[mx.array, mx.array], mx.array]]]
tokens: List[mx.array]
def __len__(self) -> int: ...
def filter(self, keep_idx: List[int]) -> None: ...
def extend(self, other: "Batch") -> None: ...
def extract_cache(self, idx: int) -> List[Any]: ...
class BatchGenerator:
model: nn.Module
sampler: Callable[[mx.array], mx.array]
stop_tokens: set[int]
max_kv_size: Optional[int]
prefill_step_size: int
completion_batch_size: int
prefill_batch_size: int
unprocessed_prompts: List[Any]
active_batch: Optional[Batch]
prompt_progress_callback: Callable[[List[Tuple[int, int, int]]], None]
_stats: BatchStats
_next_count: int
@dataclass
class Response:
uid: int
token: int
logprobs: mx.array
finish_reason: Optional[str]
prompt_cache: Any
def __init__(
self,
model: Any,
max_tokens: int = ...,
stop_tokens: Optional[set[int]] = ...,
sampler: Optional[Callable[[mx.array], mx.array]] = ...,
logits_processors: Optional[
List[Callable[[mx.array, mx.array], mx.array]]
] = ...,
completion_batch_size: int = ...,
prefill_batch_size: int = ...,
prefill_step_size: int = ...,
prompt_progress_callback: Optional[
Callable[[List[Tuple[int, int, int]]], None]
] = ...,
max_kv_size: Optional[int] = ...,
) -> None: ...
def close(self) -> None: ...
def insert(
self,
prompts: Any,
max_tokens: Union[List[int], int, None] = ...,
caches: Any = ...,
samplers: Optional[List[Any]] = ...,
logits_processors: Optional[List[Any]] = ...,
) -> List[int]: ...
def remove(
self, uids: List[int], return_prompt_caches: bool = ...
) -> Optional[dict[int, List[Any]]]: ...
def stats(self) -> BatchStats: ...
def next(self) -> List[Response]: ...
def _process_prompts(self, prompts: List[Any]) -> Batch: ...
def _step(
self,
input_tokens: mx.array,
prompt_cache: List[Any],
samplers: Optional[List[Any]],
logits_processors: Optional[List[Any]],
tokens: List[mx.array],
) -> Tuple[mx.array, List[mx.array]]: ...
...
def batch_generate(
model,
tokenizer,
prompts: List[int],
prompts: List[List[int]],
prompt_caches: Optional[List[List[Any]]] = ...,
max_tokens: Union[int, List[int]] = ...,
verbose: bool = ...,
return_prompt_caches: bool = ...,
logits_processors: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = ...,
**kwargs,
) -> BatchResponse:
"""
@@ -355,14 +534,22 @@ def batch_generate(
Args:
model (nn.Module): The language model.
tokenizer (PreTrainedTokenizer): The tokenizer.
prompt (List[List[int]]): The input prompts.
prompts (List[List[int]]): The input prompts.
prompt_caches (List[List[Any]], optional): Pre-computed prompt-caches
for each input prompt. Note, unlike ``generate_step``, the caches
won't be updated in-place.
verbose (bool): If ``True``, print tokens and timing information.
Default: ``False``.
max_tokens (Union[int, List[int]): Maximum number of output tokens. This
can be per prompt if a list is provided.
return_prompt_caches (bool): Return the prompt caches in the batch
responses. Default: ``False``.
logits_processors (List[Callable[[mx.array, mx.array], mx.array]], optional):
A list of functions that take tokens and logits and return the processed logits. Default: ``None``.
kwargs: The remaining options get passed to :obj:`BatchGenerator`.
See :obj:`BatchGenerator` for more details.
"""
...
def main(): # -> None:
...
@@ -0,0 +1,19 @@
"""
This type stub file was generated by pyright.
"""
import mlx.core as mx
import mlx.nn as nn
from functools import partial
@partial(mx.compile, shapeless=True)
def swiglu(gate, x): ...
@partial(mx.compile, shapeless=True)
def xielu(x, alpha_p, alpha_n, beta, eps): # -> array:
...
class XieLU(nn.Module):
def __init__(
self, alpha_p_init=..., alpha_n_init=..., beta=..., eps=...
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
+5 -5
View File
@@ -3,7 +3,7 @@ This type stub file was generated by pyright.
"""
from dataclasses import dataclass
from typing import Optional
from typing import Any, Optional
import mlx.core as mx
@@ -37,10 +37,10 @@ def quantized_scaled_dot_product_attention(
bits: int = ...,
) -> mx.array: ...
def scaled_dot_product_attention(
queries,
keys,
values,
cache,
queries: mx.array,
keys: mx.array,
values: mx.array,
cache: Optional[Any],
scale: float,
mask: Optional[mx.array],
sinks: Optional[mx.array] = ...,
+280
View File
@@ -0,0 +1,280 @@
"""Type stubs for mlx_lm.models.deepseek_v4"""
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import ArraysCache, RotatingKVCache
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
vocab_size: int
hidden_size: int
intermediate_size: int
moe_intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
n_shared_experts: Optional[int]
n_routed_experts: int
num_experts_per_tok: int
head_dim: int
qk_rope_head_dim: int
q_lora_rank: int
o_lora_rank: int
o_groups: int
sliding_window: int
hc_mult: int
hc_sinkhorn_iters: int
hc_eps: float
compress_ratios: Optional[List[int]]
compress_rope_theta: float
rope_theta: float
rope_scaling: Optional[Dict[str, Any]]
rms_norm_eps: float
swiglu_limit: float
attention_bias: bool
max_position_embeddings: int
class DeepseekV4RoPE(nn.Module):
dims: int
freqs: mx.array
def __init__(
self,
dims: int,
base: float,
scaling_config: Optional[Dict[str, Any]] = None,
) -> None: ...
def __call__(
self,
x: mx.array,
offset: int = 0,
inverse: bool = False,
) -> mx.array: ...
class HyperConnection(nn.Module):
dim: int
hc_mult: int
norm_eps: float
def __init__(
self,
dim: int,
hc_mult: int,
norm_eps: float,
sinkhorn_iters: int,
hc_eps: float,
) -> None: ...
class HyperHead(nn.Module):
dim: int
hc_mult: int
def __init__(
self,
dim: int,
hc_mult: int,
norm_eps: float,
hc_eps: float,
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class Compressor(nn.Module):
dim: int
head_dim: int
rope_head_dim: int
compress_ratio: int
overlap: bool
wkv_gate: nn.Linear
ape: mx.array
norm: nn.RMSNorm
rope: DeepseekV4RoPE
def __init__(
self,
dim: int,
compress_ratio: int,
head_dim: int,
rope_head_dim: int,
rms_norm_eps: float,
rope: DeepseekV4RoPE,
) -> None: ...
def __call__(
self,
x: mx.array,
cache: "DeepseekV4Cache",
offset: Any,
key: str = ...,
) -> mx.array: ...
class Indexer(nn.Module):
def __init__(
self,
args: ModelArgs,
compress_ratio: int,
rope: DeepseekV4RoPE,
) -> None: ...
class _CompressorBranch:
buffer_kv: Optional[mx.array]
buffer_gate: Optional[mx.array]
prev_kv: Optional[mx.array]
prev_gate: Optional[mx.array]
pool: Optional[mx.array]
buffer_lengths: Optional[List[int]]
pool_lengths: Optional[List[int]]
buffer_count: int
_new_pool_lengths: Optional[List[int]]
def __init__(self) -> None: ...
class DeepseekV4Cache:
local: RotatingKVCache
offset: int
keys: Optional[mx.array]
values: Optional[mx.array]
state: Any
meta_state: Any
nbytes: int
_branches: Dict[str, _CompressorBranch]
_pending_lengths: Optional[List[int]]
def __init__(self, sliding_window: int) -> None: ...
def update_and_fetch(
self, keys: mx.array, values: mx.array
) -> tuple[mx.array, mx.array]: ...
def is_trimmable(self) -> bool: ...
def trim(self, n: int) -> int: ...
def empty(self) -> bool: ...
def size(self) -> int: ...
def prepare(
self,
*,
left_padding: Optional[List[int]] = None,
lengths: Optional[List[int]] = None,
right_padding: Optional[List[int]] = None,
) -> None: ...
def finalize(self) -> None: ...
def filter(self, batch_indices: mx.array) -> None: ...
def extend(self, other: "DeepseekV4Cache") -> None: ...
def extract(self, idx: int) -> "DeepseekV4Cache": ...
@classmethod
def merge(cls, caches: List["DeepseekV4Cache"]) -> "DeepseekV4Cache": ...
class V4Attention(nn.Module):
args: ModelArgs
layer_id: int
dim: int
n_heads: int
head_dim: int
rope_head_dim: int
nope_head_dim: int
n_groups: int
q_lora_rank: int
o_lora_rank: int
window: int
eps: float
scale: float
compress_ratio: int
wqkv_a: nn.Linear
q_norm: nn.RMSNorm
wq_b: nn.Linear
kv_norm: nn.RMSNorm
attn_sink: mx.array
wo_a: nn.Linear
wo_b: nn.Linear
rope: DeepseekV4RoPE
compressor: Compressor
indexer: Indexer
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class DeepseekV4MLP(nn.Module):
gate_proj: nn.Linear
up_proj: nn.Linear
down_proj: nn.Linear
def __init__(
self,
hidden_size: int,
intermediate_size: int,
swiglu_limit: float = 0.0,
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class MoEGate(nn.Module):
weight: mx.array
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(
self, x: mx.array, input_ids: mx.array
) -> tuple[mx.array, mx.array]: ...
class DeepseekV4MoE(nn.Module):
num_experts_per_tok: int
switch_mlp: SwitchGLU
gate: MoEGate
shared_experts: DeepseekV4MLP
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(self, x: mx.array, input_ids: mx.array) -> mx.array: ...
class DeepseekV4Block(nn.Module):
attn_norm: nn.RMSNorm
attn: V4Attention
hc_attn: HyperConnection
ffn_norm: nn.RMSNorm
ffn: DeepseekV4MoE
hc_ffn: HyperConnection
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(
self,
h: mx.array,
cache: Optional[Any],
input_ids: mx.array,
) -> mx.array: ...
class DeepseekV4Model(nn.Module):
args: ModelArgs
vocab_size: int
embed_tokens: nn.Embedding
layers: list[DeepseekV4Block]
norm: nn.RMSNorm
hc_head: HyperHead
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Any]] = None,
) -> mx.array: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
model: DeepseekV4Model
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Any]] = None,
) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
def make_cache(self) -> list[RotatingKVCache | DeepseekV4Cache]: ...
@property
def layers(self) -> list[DeepseekV4Block]: ...
+31
View File
@@ -0,0 +1,31 @@
from dataclasses import dataclass
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from . import gemma4_text
from .base import BaseModelArgs
from .cache import KVCache, RotatingKVCache
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
text_config: Optional[dict[str, Any]]
vocab_size: int
def __post_init__(self) -> None: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
language_model: gemma4_text.Model
def __init__(self, args: ModelArgs) -> None: ...
def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@property
def layers(self) -> list[gemma4_text.DecoderLayer]: ...
@property
def quant_predicate(self) -> Any: ...
def make_cache(self) -> list[KVCache | RotatingKVCache]: ...
+179
View File
@@ -0,0 +1,179 @@
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import KVCache, RotatingKVCache
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
num_hidden_layers: int
intermediate_size: int
num_attention_heads: int
head_dim: int
global_head_dim: int
global_partial_rotary_factor: float
rms_norm_eps: float
vocab_size: int
vocab_size_per_layer_input: int
num_key_value_heads: int
num_global_key_value_heads: Optional[int]
num_kv_shared_layers: int
pad_token_id: int
hidden_size_per_layer_input: int
rope_traditional: bool
partial_rotary_factor: float
rope_parameters: Optional[Dict[str, Any]]
sliding_window: int
sliding_window_pattern: int
max_position_embeddings: int
attention_k_eq_v: bool
final_logit_softcapping: float
use_double_wide_mlp: bool
enable_moe_block: bool
num_experts: Optional[int]
top_k_experts: Optional[int]
moe_intermediate_size: Optional[int]
layer_types: Optional[List[str]]
tie_word_embeddings: bool
def __post_init__(self) -> None: ...
class MLP(nn.Module):
gate_proj: nn.Linear
down_proj: nn.Linear
up_proj: nn.Linear
def __init__(self, config: ModelArgs, layer_idx: int = 0) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class Router(nn.Module):
proj: nn.Linear
scale: mx.array
per_expert_scale: mx.array
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
class Experts(nn.Module):
switch_glu: SwitchGLU
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self, x: mx.array, top_k_indices: mx.array, top_k_weights: mx.array
) -> mx.array: ...
class Attention(nn.Module):
layer_idx: int
layer_type: str
is_sliding: bool
head_dim: int
n_heads: int
n_kv_heads: int
use_k_eq_v: bool
scale: float
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
q_norm: nn.Module
k_norm: nn.Module
v_norm: nn.Module
rope: nn.Module
def __init__(self, config: ModelArgs, layer_idx: int) -> None: ...
def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
class DecoderLayer(nn.Module):
layer_idx: int
layer_type: str
self_attn: Attention
mlp: MLP
enable_moe: bool
router: Router
experts: Experts
input_layernorm: nn.Module
post_attention_layernorm: nn.Module
pre_feedforward_layernorm: nn.Module
post_feedforward_layernorm: nn.Module
post_feedforward_layernorm_1: nn.Module
post_feedforward_layernorm_2: nn.Module
pre_feedforward_layernorm_2: nn.Module
hidden_size_per_layer_input: int
per_layer_input_gate: Optional[nn.Linear]
per_layer_projection: Optional[nn.Linear]
post_per_layer_input_norm: Optional[nn.Module]
layer_scalar: mx.array
def __init__(self, config: ModelArgs, layer_idx: int) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = ...,
cache: Optional[Any] = ...,
per_layer_input: Optional[mx.array] = ...,
shared_kv: Optional[tuple[mx.array, mx.array]] = ...,
offset: Optional[mx.array] = ...,
) -> tuple[mx.array, tuple[mx.array, mx.array], mx.array]: ...
class Gemma4TextModel(nn.Module):
config: ModelArgs
vocab_size: int
window_size: int
sliding_window_pattern: int
num_hidden_layers: int
embed_tokens: nn.Embedding
embed_scale: float
layers: list[DecoderLayer]
norm: nn.Module
hidden_size_per_layer_input: int
embed_tokens_per_layer: Optional[nn.Embedding]
per_layer_model_projection: Optional[nn.Linear]
per_layer_projection_norm: Optional[nn.Module]
previous_kvs: list[int]
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self,
inputs: Optional[mx.array] = ...,
cache: Optional[list[Any]] = ...,
input_embeddings: Optional[mx.array] = ...,
per_layer_inputs: Optional[mx.array] = ...,
) -> mx.array: ...
def _get_per_layer_inputs(
self,
input_ids: Optional[mx.array],
input_embeddings: Optional[mx.array] = ...,
) -> mx.array: ...
def _project_per_layer_inputs(
self,
input_embeddings: mx.array,
per_layer_inputs: Optional[mx.array] = ...,
) -> mx.array: ...
def _make_masks(self, h: mx.array, cache: list[Any]) -> list[Any]: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
model: Gemma4TextModel
final_logit_softcapping: float
tie_word_embeddings: bool
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@property
def layers(self) -> list[DecoderLayer]: ...
@property
def head_dim(self) -> int: ...
@property
def n_kv_heads(self) -> int: ...
@property
def quant_predicate(self) -> Any: ...
def make_cache(self) -> list[KVCache | RotatingKVCache]: ...
+103
View File
@@ -0,0 +1,103 @@
"""Type stubs for mlx_lm.models.gpt_oss"""
from dataclasses import dataclass
from typing import Any, List, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import KVCache
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
num_local_experts: int
num_experts_per_tok: int
vocab_size: int
rms_norm_eps: float
sliding_window: int
layer_types: Optional[List[str]]
def mlx_topk(a: mx.array, k: int, axis: int = -1) -> tuple[mx.array, mx.array]: ...
class AttentionBlock(nn.Module):
head_dim: int
num_attention_heads: int
num_key_value_heads: int
num_key_value_groups: int
sinks: mx.array
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
sm_scale: float
rope: nn.Module
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class TransformerBlock(nn.Module):
self_attn: AttentionBlock
mlp: MLPBlock
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class MLPBlock(nn.Module):
hidden_size: int
num_local_experts: int
num_experts_per_tok: int
experts: SwitchGLU
router: nn.Linear
sharding_group: Optional[mx.distributed.Group]
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class GptOssMoeModel(nn.Module):
embed_tokens: nn.Embedding
norm: nn.RMSNorm
layer_types: List[str]
layers: list[TransformerBlock]
window_size: int
swa_idx: int
ga_idx: int
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
class Model(nn.Module):
model_type: str
model: GptOssMoeModel
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
@property
def layers(self) -> list[nn.Module]: ...
def make_cache(self) -> list[KVCache]: ...
+94
View File
@@ -0,0 +1,94 @@
"""Type stubs for mlx_lm.models.minimax"""
from dataclasses import dataclass
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
num_local_experts: int
num_experts_per_tok: int
max_position_embeddings: int
class MiniMaxAttention(nn.Module):
num_heads: int
num_attention_heads: int
num_key_value_heads: int
head_dim: int
scale: float
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
q_norm: nn.Module
k_norm: nn.Module
rope: nn.Module
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class MiniMaxSparseMoeBlock(nn.Module):
num_experts_per_tok: int
gate: nn.Linear
switch_mlp: SwitchGLU
e_score_correction_bias: mx.array
sharding_group: Optional[mx.distributed.Group]
def __init__(self, args: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class MiniMaxDecoderLayer(nn.Module):
self_attn: MiniMaxAttention
block_sparse_moe: MiniMaxSparseMoeBlock
input_layernorm: nn.RMSNorm
post_attention_layernorm: nn.RMSNorm
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class MiniMaxModel(nn.Module):
embed_tokens: nn.Embedding
layers: list[MiniMaxDecoderLayer]
norm: nn.RMSNorm
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
class Model(nn.Module):
model_type: str
model: MiniMaxModel
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
@property
def layers(self) -> list[MiniMaxDecoderLayer]: ...
+14
View File
@@ -92,6 +92,15 @@ class NemotronHAttention(nn.Module):
cache: Optional[KVCache] = None,
) -> mx.array: ...
class MoEGate(nn.Module):
config: ModelArgs
top_k: int
norm_topk_prob: bool
weight: mx.array
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
class NemotronHMLP(nn.Module):
up_proj: nn.Linear
down_proj: nn.Linear
@@ -102,9 +111,14 @@ class NemotronHMLP(nn.Module):
def __call__(self, x: mx.array) -> mx.array: ...
class NemotronHMoE(nn.Module):
config: ModelArgs
num_experts_per_tok: int
moe_latent_size: Optional[int]
switch_mlp: SwitchMLP
gate: MoEGate
shared_experts: NemotronHMLP
fc1_latent_proj: nn.Linear
fc2_latent_proj: nn.Linear
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
@@ -71,6 +71,7 @@ class Qwen3NextAttention(nn.Module):
class Qwen3NextSparseMoeBlock(nn.Module):
norm_topk_prob: bool
num_experts: int
num_experts_per_tok: int
top_k: int
gate: nn.Linear
switch_mlp: SwitchGLU
+4
View File
@@ -48,6 +48,10 @@ def make_logits_processors(
logit_bias: Optional[Dict[int, float]] = ...,
repetition_penalty: Optional[float] = ...,
repetition_context_size: Optional[int] = ...,
presence_penalty: Optional[float] = ...,
presence_context_size: Optional[int] = ...,
frequency_penalty: Optional[float] = ...,
frequency_context_size: Optional[int] = ...,
) -> list[Callable[[mx.array, mx.array], mx.array]]:
"""
Make logits processors for use with ``generate_step``.
+2
View File
@@ -117,6 +117,8 @@ class TokenizerWrapper:
think_end: str | None
think_start_id: int | None
think_end_id: int | None
think_start_tokens: list[int] | None
think_end_tokens: list[int] | None
def __init__(
self,
+50
View File
@@ -0,0 +1,50 @@
"""
This type stub file was generated by pyright.
"""
import mlx.nn as nn
class DoRALinear(nn.Module):
@staticmethod
def from_base(
linear: nn.Linear, r: int = ..., dropout: float = ..., scale: float = ...
): # -> DoRALinear:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedLinear | Linear:
...
def __init__(
self,
input_dims: int,
output_dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
bias: bool = ...,
) -> None: ...
def set_linear(self, linear): # -> None:
"""
Set the self.linear layer and recompute self.m.
"""
...
def __call__(self, x): ...
class DoRAEmbedding(nn.Module):
def from_base(
embedding: nn.Embedding, r: int = ..., dropout: float = ..., scale: float = ...
): # -> DoRAEmbedding:
...
def fuse(self, dequantize: bool = ...): # -> Embedding:
...
def __init__(
self,
num_embeddings: int,
dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
) -> None: ...
def set_embedding(self, embedding: nn.Module): # -> None:
...
def __call__(self, x): ...
def as_linear(self, x): ...
+66
View File
@@ -0,0 +1,66 @@
"""
This type stub file was generated by pyright.
"""
import mlx.nn as nn
class LoRALinear(nn.Module):
@staticmethod
def from_base(
linear: nn.Linear, r: int = ..., dropout: float = ..., scale: float = ...
): # -> LoRALinear:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedLinear | Linear:
...
def __init__(
self,
input_dims: int,
output_dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
bias: bool = ...,
) -> None: ...
def __call__(self, x): # -> array:
...
class LoRASwitchLinear(nn.Module):
@staticmethod
def from_base(
linear: nn.Module, r: int = ..., dropout: float = ..., scale: float = ...
): # -> LoRASwitchLinear:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedSwitchLinear | SwitchLinear:
...
def __init__(
self,
input_dims: int,
output_dims: int,
num_experts: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
bias: bool = ...,
) -> None: ...
def __call__(self, x, indices, sorted_indices=...): ...
class LoRAEmbedding(nn.Module):
@staticmethod
def from_base(
embedding: nn.Embedding, r: int = ..., dropout: float = ..., scale: float = ...
): # -> LoRAEmbedding:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedEmbedding | Embedding:
...
def __init__(
self,
num_embeddings: int,
dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
) -> None: ...
def __call__(self, x): # -> array:
...
def as_linear(self, x): # -> array:
...
+57
View File
@@ -0,0 +1,57 @@
"""
This type stub file was generated by pyright.
"""
import mlx.nn as nn
from typing import Dict
def build_schedule(schedule_config: Dict): # -> Any:
"""
Build a learning rate schedule from the given config.
"""
...
def linear_to_lora_layers(
model: nn.Module, num_layers: int, config: Dict, use_dora: bool = ...
): # -> None:
"""
Convert some of the models linear layers to lora layers.
Args:
model (nn.Module): The neural network model.
num_layers (int): The number of blocks to convert to lora layers
starting from the last layer.
config (dict): More configuration parameters for LoRA, including the
rank, scale, and optional layer keys.
use_dora (bool): If True, uses DoRA instead of LoRA.
Default: ``False``
"""
...
def load_adapters(model: nn.Module, adapter_path: str) -> nn.Module:
"""
Load any fine-tuned adapters / layers.
Args:
model (nn.Module): The neural network model.
adapter_path (str): Path to the adapter configuration file.
Returns:
nn.Module: The updated model with LoRA layers applied.
"""
...
def remove_lora_layers(model: nn.Module) -> nn.Module:
"""
Remove the LoRA layers from the model.
Args:
model (nn.Module): The model with LoRA layers.
Returns:
nn.Module: The model without LoRA layers.
"""
...
def print_trainable_parameters(model): # -> None:
...
View File
Whitespace-only changes.
+12
View File
@@ -0,0 +1,12 @@
from typing import Any
def get_message_json(
model_name: str,
prompt: str,
role: str = "user",
skip_image_token: bool = False,
skip_audio_token: bool = False,
num_images: int = 0,
num_audios: int = 0,
**kwargs: Any,
) -> dict[str, Any]: ...
+15
View File
@@ -0,0 +1,15 @@
from pathlib import Path
from typing import Any
class ImageProcessor:
def preprocess(
self, images: list[dict[str, Any]], **kwargs: Any
) -> dict[str, Any]: ...
def __call__(self, **kwargs: Any) -> dict[str, Any]: ...
def load_image_processor(
model_path: str | Path, **kwargs: Any
) -> ImageProcessor | None: ...
def load_processor(
model_path: str | Path, add_detokenizer: bool = ..., **kwargs: Any
) -> ImageProcessor: ...
+8
View File
@@ -0,0 +1,8 @@
from typing import Any, Self
class safe_open:
def __init__(self, filename: str, framework: str = "pt") -> None: ...
def __enter__(self) -> Self: ...
def __exit__(self, *args: Any) -> None: ...
def keys(self) -> list[str]: ...
def get_tensor(self, name: str) -> Any: ...
+2 -1
View File
@@ -1,7 +1,8 @@
{
"recommendations": [
"detachhead.basedpyright",
"ms-python.python"
"ms-python.python",
"jnoortheen.nix-ide"
],
"unwantedRecommendations": [
"ms-python.vscode-pylance",
+30 -1
View File
@@ -1,3 +1,32 @@
{
"basedpyright.importStrategy": "fromEnvironment"
"files.associations": {
"*.nix": "nix",
},
"nix.enableLanguageServer": true,
"nix.serverPath": "nixd",
"nix.serverSettings": {
"nixd": {
"formatting": {
"command": ["nixpkgs-fmt"]
},
"nixpkgs": {
"expr": "(builtins.getFlake \"path:${workspaceFolder}\").currentSystem.config._module.args.pkgs"
},
"options": {
"flake-parts": {
"expr": "(builtins.getFlake \"path:${workspaceFolder}\").debug.options"
},
"flake-parts-perSystem": {
"expr": "(builtins.getFlake \"path:${workspaceFolder}\").currentSystem.options"
}
}
}
},
"[nix]": {
"editor.defaultFormatter": "jnoortheen.nix-ide"
},
"python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
"basedpyright.analysis.configFilePath": "${workspaceFolder}/pyproject.toml",
"basedpyright.importStrategy": "fromEnvironment",
}
+2 -2
View File
@@ -19,8 +19,8 @@ To run EXO from source:
- [macmon](https://github.com/vladkens/macmon) (for hardware monitoring on Apple Silicon)
Use the pinned fork revision used by this repo instead of Homebrew `macmon`.
```bash
cargo install --git https://github.com/swiftraccoon/macmon \
--rev 9154d234f763fbeffdcb4135d0bbbaf80609699b \
cargo install --git https://github.com/vladkens/macmon \
--rev a1cd06b6cc0d5e61db24fd8832e74cd992097a7d \
macmon \
--force
```
+10 -6
View File
@@ -112,8 +112,8 @@ Then restart the Nix daemon: `sudo launchctl kickstart -k system/org.nixos.nix-d
Homebrew `macmon 0.6.1` still crashes on Apple M5.
```bash
cargo install --git https://github.com/swiftraccoon/macmon \
--rev 9154d234f763fbeffdcb4135d0bbbaf80609699b \
cargo install --git https://github.com/vladkens/macmon \
--rev a1cd06b6cc0d5e61db24fd8832e74cd992097a7d \
macmon \
--force
```
@@ -295,8 +295,9 @@ exo supports several environment variables for configuration:
| Variable | Description | Default |
|----------|-------------|---------|
| `EXO_MODELS_PATH` | Colon-separated paths to search for pre-downloaded models (e.g., on NFS mounts or shared storage) | None |
| `EXO_MODELS_DIR` | Directory where exo downloads and stores models | `~/.local/share/exo/models` (Linux) or `~/.exo/models` (macOS) |
| `EXO_DEFAULT_MODELS_DIR` | Default directory for model downloads and caches. Always first in the writable dirs list. | `~/.local/share/exo/models` (Linux) or `~/.exo/models` (macOS) |
| `EXO_MODELS_DIRS` | Colon-separated additional writable directories for model downloads. Checked in order after the default; first with enough free space is used. | None |
| `EXO_MODELS_READ_ONLY_DIRS` | Colon-separated read-only directories to search for pre-downloaded models (e.g., NFS mounts, shared storage). Models here cannot be deleted. | None |
| `EXO_OFFLINE` | Run without internet connection (uses only local models) | `false` |
| `EXO_ENABLE_IMAGE_MODELS` | Enable image model support | `false` |
| `EXO_LIBP2P_NAMESPACE` | Custom namespace for cluster isolation | None |
@@ -306,8 +307,11 @@ exo supports several environment variables for configuration:
**Example usage:**
```bash
# Use pre-downloaded models from NFS mount
EXO_MODELS_PATH=/mnt/nfs/models:/opt/ai-models uv run exo
# Use pre-downloaded models from NFS mount (read-only)
EXO_MODELS_READ_ONLY_DIRS=/mnt/nfs/models:/opt/ai-models uv run exo
# Download models to an external SSD (falls back to default dir if full)
EXO_MODELS_DIRS=/Volumes/ExternalSSD/exo-models uv run exo
# Run in offline mode
EXO_OFFLINE=true uv run exo
+8 -218
View File
@@ -16,22 +16,13 @@ struct ContentView: View {
@EnvironmentObject private var updater: SparkleUpdater
@EnvironmentObject private var thunderboltBridgeService: ThunderboltBridgeService
@EnvironmentObject private var settingsWindowController: SettingsWindowController
@EnvironmentObject private var bugReportWindowController: BugReportWindowController
@State private var focusedNode: NodeViewModel?
@State private var deletingInstanceIDs: Set<String> = []
@State private var showAllNodes = false
@State private var showAllInstances = false
@State private var baseURLCopied = false
@State private var showAdvanced = false
@State private var showDebugInfo = false
private enum BugReportPhase: Equatable {
case idle
case prompting
case sending(String)
case success(String)
case failure(String)
}
@State private var bugReportPhase: BugReportPhase = .idle
@State private var bugReportUserDescription: String = ""
@State private var uninstallInProgress = false
@State private var pendingNamespace: String = ""
@State private var pendingHFToken: String = ""
@@ -294,6 +285,13 @@ struct ContentView: View {
) {
updater.checkForUpdates()
}
HoverButton(
title: "Share Bug Report…",
tint: .primary,
trailingSystemImage: "ladybug"
) {
bugReportWindowController.open()
}
.padding(.bottom, 8)
HoverButton(title: "Quit", tint: .secondary) {
controller.stop()
@@ -477,40 +475,6 @@ struct ContentView: View {
}
}
private var debugSection: some View {
VStack(alignment: .leading, spacing: 4) {
HoverButton(
title: "Debug Info",
tint: .primary,
trailingSystemImage: showDebugInfo ? "chevron.up" : "chevron.down",
small: true
) {
showDebugInfo.toggle()
}
if showDebugInfo {
VStack(alignment: .leading, spacing: 4) {
Text("Version: \(buildTag)")
.font(.caption2)
.foregroundColor(.secondary)
Text("Commit: \(buildCommit)")
.font(.caption2)
.foregroundColor(.secondary)
Text(thunderboltStatusText)
.font(.caption2)
.foregroundColor(thunderboltStatusColor)
clusterThunderboltBridgeView
interfaceIpList
rdmaStatusView
sendBugReportButton
.padding(.top, 6)
}
.padding(.leading, 8)
.transition(.opacity)
}
}
.animation(.easeInOut(duration: 0.25), value: showDebugInfo)
}
private var rdmaStatusView: some View {
let rdmaStatuses = stateService.latestSnapshot?.nodeRdmaCtl ?? [:]
let localNodeId = stateService.localNodeId
@@ -559,118 +523,6 @@ struct ContentView: View {
}
}
private var sendBugReportButton: some View {
VStack(alignment: .leading, spacing: 6) {
switch bugReportPhase {
case .idle:
Button {
bugReportPhase = .prompting
bugReportUserDescription = ""
} label: {
HStack {
Text("Send Bug Report")
.font(.caption)
.fontWeight(.semibold)
Spacer()
}
.padding(.vertical, 6)
.padding(.horizontal, 8)
.background(
RoundedRectangle(cornerRadius: 6)
.fill(Color.accentColor.opacity(0.12))
)
}
.buttonStyle(.plain)
case .prompting:
VStack(alignment: .leading, spacing: 6) {
Text("What's the issue? (optional)")
.font(.caption2)
.foregroundColor(.secondary)
TextEditor(text: $bugReportUserDescription)
.font(.caption2)
.frame(height: 60)
.overlay(
RoundedRectangle(cornerRadius: 4)
.stroke(Color.secondary.opacity(0.3), lineWidth: 1)
)
HStack(spacing: 8) {
Button("Send") {
Task {
await sendBugReport()
}
}
.font(.caption2)
.buttonStyle(.borderedProminent)
.controlSize(.small)
Button("Cancel") {
bugReportPhase = .idle
}
.font(.caption2)
.buttonStyle(.bordered)
.controlSize(.small)
}
}
.padding(8)
.background(
RoundedRectangle(cornerRadius: 6)
.fill(Color.accentColor.opacity(0.06))
)
case .sending(let message):
HStack(spacing: 6) {
ProgressView()
.scaleEffect(0.6)
Text(message)
.font(.caption2)
.foregroundColor(.secondary)
}
case .success(let message):
VStack(alignment: .leading, spacing: 6) {
Text(message)
.font(.caption2)
.foregroundColor(.secondary)
.fixedSize(horizontal: false, vertical: true)
Button {
openGitHubIssue()
} label: {
HStack(spacing: 4) {
Image(systemName: "arrow.up.right.square")
.imageScale(.small)
Text("Create GitHub Issue")
.font(.caption2)
}
}
.buttonStyle(.bordered)
.controlSize(.small)
Button("Done") {
bugReportPhase = .idle
bugReportUserDescription = ""
}
.font(.caption2)
.buttonStyle(.plain)
.foregroundColor(.secondary)
}
case .failure(let message):
VStack(alignment: .leading, spacing: 4) {
Text(message)
.font(.caption2)
.foregroundColor(.red)
.fixedSize(horizontal: false, vertical: true)
Button("Dismiss") {
bugReportPhase = .idle
}
.font(.caption2)
.buttonStyle(.plain)
.foregroundColor(.secondary)
}
}
}
.animation(.easeInOut(duration: 0.2), value: bugReportPhase)
}
private var processToggleBinding: Binding<Bool> {
Binding(
get: {
@@ -711,61 +563,6 @@ struct ContentView: View {
)
}
private func sendBugReport() async {
bugReportPhase = .sending("Collecting logs...")
let service = BugReportService()
let description = bugReportUserDescription.trimmingCharacters(in: .whitespacesAndNewlines)
do {
let outcome = try await service.sendReport(
isManual: true,
userDescription: description.isEmpty ? nil : description
)
if outcome.success {
bugReportPhase = .success(outcome.message)
} else {
bugReportPhase = .failure(outcome.message)
}
} catch {
bugReportPhase = .failure(error.localizedDescription)
}
}
private func openGitHubIssue() {
let description = bugReportUserDescription.trimmingCharacters(in: .whitespacesAndNewlines)
var bodyParts: [String] = []
bodyParts.append("## Describe the bug")
bodyParts.append("")
if !description.isEmpty {
bodyParts.append(description)
} else {
bodyParts.append("A clear and concise description of what the bug is.")
}
bodyParts.append("")
bodyParts.append("## Environment")
bodyParts.append("")
bodyParts.append("- macOS Version: \(ProcessInfo.processInfo.operatingSystemVersionString)")
bodyParts.append("- EXO Version: \(buildTag) (\(buildCommit))")
bodyParts.append("")
bodyParts.append("## Additional context")
bodyParts.append("")
bodyParts.append("A bug report with diagnostic logs was submitted via the app.")
let body = bodyParts.joined(separator: "\n")
var components = URLComponents(string: "https://github.com/exo-explore/exo/issues/new")!
components.queryItems = [
URLQueryItem(name: "template", value: "bug_report.md"),
URLQueryItem(name: "title", value: "[BUG] "),
URLQueryItem(name: "body", value: body),
URLQueryItem(name: "labels", value: "bug"),
]
if let url = components.url {
NSWorkspace.shared.open(url)
}
}
private func showUninstallConfirmationAlert() {
let alert = NSAlert()
alert.messageText = "Uninstall EXO"
@@ -848,13 +645,6 @@ struct ContentView: View {
}
}
private var buildTag: String {
Bundle.main.infoDictionary?["EXOBuildTag"] as? String ?? "unknown"
}
private var buildCommit: String {
Bundle.main.infoDictionary?["EXOBuildCommit"] as? String ?? "unknown"
}
}
private struct HoverButton: View {
+3
View File
@@ -22,6 +22,7 @@ struct EXOApp: App {
@StateObject private var updater: SparkleUpdater
@StateObject private var thunderboltBridgeService: ThunderboltBridgeService
@StateObject private var settingsWindowController: SettingsWindowController
@StateObject private var bugReportWindowController: BugReportWindowController
private let terminationObserver: TerminationObserver
private let firstLaunchPopout = FirstLaunchPopout()
private let ciContext = CIContext(options: nil)
@@ -46,6 +47,7 @@ struct EXOApp: App {
let thunderboltBridge = ThunderboltBridgeService(clusterStateService: service)
_thunderboltBridgeService = StateObject(wrappedValue: thunderboltBridge)
_settingsWindowController = StateObject(wrappedValue: SettingsWindowController())
_bugReportWindowController = StateObject(wrappedValue: BugReportWindowController())
enableLaunchAtLoginIfNeeded()
// Install LaunchDaemon to disable Thunderbolt Bridge on startup (prevents network loops)
NetworkSetupHelper.promptAndInstallIfNeeded()
@@ -66,6 +68,7 @@ struct EXOApp: App {
.environmentObject(updater)
.environmentObject(thunderboltBridgeService)
.environmentObject(settingsWindowController)
.environmentObject(bugReportWindowController)
} label: {
menuBarIcon
.onReceive(controller.$isFirstLaunchReady) { ready in
+98
View File
@@ -7,7 +7,27 @@ private let hfTokenKey = "EXOHFToken"
private let hfEndpointKey = "EXOHFEndpoint"
private let enableImageModelsKey = "EXOEnableImageModels"
private let offlineModeKey = "EXOOfflineMode"
private let fastSynchEnabledKey = "EXOFastSynchEnabled"
private let onboardingCompletedKey = "EXOOnboardingCompleted"
private let defaultModelsDirKey = "EXODefaultModelsDir"
private let additionalModelsDirsKey = "EXOAdditionalModelsDirs"
private let readOnlyModelsDirsKey = "EXOReadOnlyModelsDirs"
private let customEnvironmentVariablesKey = "EXOCustomEnvironmentVariables"
/// A user-defined environment variable that is injected into the exo child
/// process at launch. Used as an escape hatch for env vars that don't have
/// first-class typed UI in Settings.
struct CustomEnvironmentVariable: Codable, Identifiable, Equatable {
var id: UUID
var key: String
var value: String
init(id: UUID = UUID(), key: String = "", value: String = "") {
self.id = id
self.key = key
self.value = value
}
}
@MainActor
final class ExoProcessController: ObservableObject {
@@ -78,6 +98,60 @@ final class ExoProcessController: ObservableObject {
UserDefaults.standard.set(offlineMode, forKey: offlineModeKey)
}
}
@Published var fastSynchEnabled: Bool = {
if UserDefaults.standard.object(forKey: fastSynchEnabledKey) == nil {
return true
}
return UserDefaults.standard.bool(forKey: fastSynchEnabledKey)
}()
{
didSet {
UserDefaults.standard.set(fastSynchEnabled, forKey: fastSynchEnabledKey)
}
}
@Published var defaultModelsDir: String = {
return UserDefaults.standard.string(forKey: defaultModelsDirKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(defaultModelsDir, forKey: defaultModelsDirKey)
}
}
@Published var additionalModelsDirs: String = {
return UserDefaults.standard.string(forKey: additionalModelsDirsKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(additionalModelsDirs, forKey: additionalModelsDirsKey)
}
}
@Published var readOnlyModelsDirs: String = {
return UserDefaults.standard.string(forKey: readOnlyModelsDirsKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(readOnlyModelsDirs, forKey: readOnlyModelsDirsKey)
}
}
@Published var customEnvironmentVariables: [CustomEnvironmentVariable] = {
guard
let data = UserDefaults.standard.data(forKey: customEnvironmentVariablesKey),
let decoded = try? JSONDecoder().decode(
[CustomEnvironmentVariable].self, from: data
)
else {
return []
}
return decoded
}()
{
didSet {
guard let data = try? JSONEncoder().encode(customEnvironmentVariables) else {
return
}
UserDefaults.standard.set(data, forKey: customEnvironmentVariablesKey)
}
}
/// Fires once when EXO transitions to `.running` for the very first time (fresh install).
@Published private(set) var isFirstLaunchReady = false
@@ -291,6 +365,7 @@ final class ExoProcessController: ObservableObject {
if offlineMode {
environment["EXO_OFFLINE"] = "true"
}
environment["EXO_FAST_SYNCH"] = fastSynchEnabled ? "true" : "false"
var paths: [String] = []
if let existing = environment["PATH"], !existing.isEmpty {
@@ -315,6 +390,29 @@ final class ExoProcessController: ObservableObject {
}
environment["PATH"] = paths.joined(separator: ":")
let trimmedDefaultModelsDir = defaultModelsDir.trimmingCharacters(in: .whitespaces)
if !trimmedDefaultModelsDir.isEmpty {
environment["EXO_DEFAULT_MODELS_DIR"] = trimmedDefaultModelsDir
}
let trimmedAdditionalModelsDirs = additionalModelsDirs.trimmingCharacters(in: .whitespaces)
if !trimmedAdditionalModelsDirs.isEmpty {
environment["EXO_MODELS_DIRS"] = trimmedAdditionalModelsDirs
}
let trimmedReadOnlyModelsDirs = readOnlyModelsDirs.trimmingCharacters(in: .whitespaces)
if !trimmedReadOnlyModelsDirs.isEmpty {
environment["EXO_MODELS_READ_ONLY_DIRS"] = trimmedReadOnlyModelsDirs
}
// Apply user-defined arbitrary environment variables last so that
// power users can override any of the typed fields above when
// necessary. Empty keys are ignored.
for variable in customEnvironmentVariables {
let trimmedKey = variable.key.trimmingCharacters(in: .whitespaces)
guard !trimmedKey.isEmpty else { continue }
environment[trimmedKey] = variable.value
}
return environment
}
+1 -1
View File
@@ -9,7 +9,7 @@
<key>EXOBuildCommit</key>
<string>$(EXO_BUILD_COMMIT)</string>
<key>EXOBugReportPresignedUrlEndpoint</key>
<string>$(EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT)</string>
<string>https://reports.exolabs.net/presigned-urls</string>
<key>NSLocalNetworkUsageDescription</key>
<string>EXO needs local network access to discover and connect to other devices in your cluster for distributed AI inference.</string>
<key>NSBonjourServices</key>
+18 -1
View File
@@ -17,7 +17,7 @@ final class ClusterStateService: ObservableObject {
init(
baseURL: URL = URL(string: "http://127.0.0.1:52415")!,
session: URLSession = .shared
session: URLSession = ClusterStateService.makeNonCachingSession()
) {
self.baseURL = baseURL
self.endpoint = baseURL.appendingPathComponent("state")
@@ -27,6 +27,23 @@ final class ClusterStateService: ObservableObject {
self.decoder = decoder
}
/// `URLSession.shared` carries an on-disk `URLCache` that persists every
/// response body under `~/Library/Caches/exolabs.EXO/`. We poll `/state`
/// at 2 Hz from `startPolling`, so leaving the shared cache attached
/// dirties ~500620 KB/sec of file-backed memory and trips macOS's
/// per-process `disk writes` resource limit (microstackshot reports
/// observed on M3 Ultra producing GBs of cached responses per hour).
/// Cluster-state polling responses are time-sensitive and small; they
/// gain nothing from being cached on disk. Use an ephemeral session
/// with `urlCache = nil` so neither response bodies nor metadata
/// touch disk.
private static func makeNonCachingSession() -> URLSession {
let config = URLSessionConfiguration.ephemeral
config.urlCache = nil
config.requestCachePolicy = .reloadIgnoringLocalCacheData
return URLSession(configuration: config)
}
func startPolling(interval: TimeInterval = 0.5) {
stopPolling()
Task {
@@ -0,0 +1,242 @@
import AppKit
import SwiftUI
/// Manages a standalone window for the bug-report flow.
/// Ensures only one instance exists and brings it to front on repeated opens.
@MainActor
final class BugReportWindowController: ObservableObject {
private var window: NSWindow?
func open() {
if let existing = window, existing.isVisible {
existing.makeKeyAndOrderFront(nil)
NSApp.activate()
return
}
let view = BugReportView(onDismiss: { [weak self] in
self?.window?.close()
})
let hostingController = NSHostingController(rootView: view)
hostingController.sizingOptions = [.preferredContentSize, .minSize]
let newWindow = NSWindow(contentViewController: hostingController)
newWindow.styleMask = [.titled, .closable, .resizable]
newWindow.title = "Send a Bug Report"
newWindow.center()
newWindow.setFrameAutosaveName("ExoBugReportWindow")
newWindow.isReleasedWhenClosed = false
newWindow.makeKeyAndOrderFront(nil)
NSApp.activate()
window = newWindow
}
}
private struct BugReportView: View {
fileprivate enum Phase: Equatable {
case prompting
case sending(String)
case success(String)
case failure(String)
}
let onDismiss: () -> Void
@State private var phase: Phase = .prompting
@State private var userDescription: String = ""
@FocusState private var descriptionFocused: Bool
var body: some View {
VStack(alignment: .leading, spacing: 12) {
switch phase {
case .prompting:
promptingView
case .sending(let message):
sendingView(message: message)
case .success(let message):
successView(message: message)
case .failure(let message):
failureView(message: message)
}
}
.padding(16)
.frame(minWidth: 380)
.animation(.easeInOut(duration: 0.2), value: phase)
.onAppear { descriptionFocused = true }
}
private var promptingView: some View {
VStack(alignment: .leading, spacing: 8) {
Text("Description (optional)")
.font(.subheadline)
.foregroundColor(.secondary)
ZStack(alignment: .topLeading) {
if userDescription.isEmpty {
Text("What were you doing when it broke?")
.font(.body)
.foregroundColor(Color(nsColor: .placeholderTextColor))
.padding(.horizontal, 10)
.padding(.vertical, 8)
.allowsHitTesting(false)
}
TextEditor(text: $userDescription)
.font(.body)
.scrollContentBackground(.hidden)
.padding(4)
.frame(height: 72)
.focused($descriptionFocused)
}
.background(
RoundedRectangle(cornerRadius: 6)
.fill(Color(nsColor: .textBackgroundColor))
)
.overlay(
RoundedRectangle(cornerRadius: 6)
.strokeBorder(Color(nsColor: .separatorColor), lineWidth: 1)
)
Text("Diagnostic logs will be uploaded with your report.")
.font(.caption)
.foregroundColor(.secondary)
HStack {
Spacer()
Button("Cancel") { onDismiss() }
.keyboardShortcut(.cancelAction)
Button("Send") {
Task { await send() }
}
.keyboardShortcut(.defaultAction)
}
.padding(.top, 4)
}
}
private func sendingView(message: String) -> some View {
VStack(alignment: .leading, spacing: 12) {
HStack(spacing: 10) {
ProgressView().controlSize(.small)
Text(message)
.foregroundColor(.secondary)
}
HStack {
Spacer()
Button("Cancel") { onDismiss() }
.keyboardShortcut(.cancelAction)
.disabled(true)
Button("Send") {}
.disabled(true)
}
}
}
private func successView(message: String) -> some View {
VStack(alignment: .leading, spacing: 12) {
HStack(alignment: .top, spacing: 10) {
Image(systemName: "checkmark.circle.fill")
.foregroundColor(.green)
.font(.title2)
Text(message)
.fixedSize(horizontal: false, vertical: true)
}
HStack {
Button {
openGitHubIssue()
} label: {
HStack(spacing: 4) {
Image(systemName: "arrow.up.right.square")
Text("Open GitHub Issue")
}
}
Spacer()
Button("Done") { onDismiss() }
.keyboardShortcut(.defaultAction)
}
}
}
private func failureView(message: String) -> some View {
VStack(alignment: .leading, spacing: 12) {
HStack(alignment: .top, spacing: 10) {
Image(systemName: "exclamationmark.triangle.fill")
.foregroundColor(.orange)
.font(.title2)
Text(message)
.fixedSize(horizontal: false, vertical: true)
}
HStack {
Spacer()
Button("Try Again") {
phase = .prompting
}
Button("Close") { onDismiss() }
.keyboardShortcut(.defaultAction)
}
}
}
private func send() async {
phase = .sending("Collecting logs and uploading…")
let service = BugReportService()
let description = userDescription.trimmingCharacters(in: .whitespacesAndNewlines)
do {
let outcome = try await service.sendReport(
isManual: true,
userDescription: description.isEmpty ? nil : description
)
if outcome.success {
phase = .success(outcome.message)
} else {
phase = .failure(outcome.message)
}
} catch {
phase = .failure(error.localizedDescription)
}
}
private func openGitHubIssue() {
let description = userDescription.trimmingCharacters(in: .whitespacesAndNewlines)
var bodyParts: [String] = []
bodyParts.append("## Describe the bug")
bodyParts.append("")
if !description.isEmpty {
bodyParts.append(description)
} else {
bodyParts.append("A clear and concise description of what the bug is.")
}
bodyParts.append("")
bodyParts.append("## Environment")
bodyParts.append("")
bodyParts.append("- macOS Version: \(ProcessInfo.processInfo.operatingSystemVersionString)")
bodyParts.append("- EXO Version: \(buildTag) (\(buildCommit))")
bodyParts.append("")
bodyParts.append("## Additional context")
bodyParts.append("")
bodyParts.append("A bug report with diagnostic logs was submitted via the app.")
let body = bodyParts.joined(separator: "\n")
var components = URLComponents(string: "https://github.com/exo-explore/exo/issues/new")!
components.queryItems = [
URLQueryItem(name: "template", value: "bug_report.md"),
URLQueryItem(name: "title", value: "[BUG] "),
URLQueryItem(name: "body", value: body),
URLQueryItem(name: "labels", value: "bug"),
]
if let url = components.url {
NSWorkspace.shared.open(url)
}
}
private var buildTag: String {
Bundle.main.infoDictionary?["EXOBuildTag"] as? String ?? "unknown"
}
private var buildCommit: String {
Bundle.main.infoDictionary?["EXOBuildCommit"] as? String ?? "unknown"
}
}
+270 -56
View File
@@ -15,9 +15,12 @@ struct SettingsView: View {
@State private var pendingHFEndpoint: String = ""
@State private var pendingEnableImageModels = false
@State private var pendingOfflineMode = false
@State private var pendingFastSynchEnabled = false
@State private var pendingDefaultModelsDir: String = ""
@State private var pendingAdditionalModelsDirs: String = ""
@State private var pendingReadOnlyModelsDirs: String = ""
@State private var pendingCustomEnvironmentVariables: [CustomEnvironmentVariable] = []
@State private var needsRestart = false
@State private var bugReportInFlight = false
@State private var bugReportMessage: String?
@State private var uninstallInProgress = false
var body: some View {
@@ -34,18 +37,27 @@ struct SettingsView: View {
.tabItem {
Label("Advanced", systemImage: "wrench.and.screwdriver")
}
environmentTab
.tabItem {
Label("Environment", systemImage: "terminal")
}
aboutTab
.tabItem {
Label("About", systemImage: "info.circle")
}
}
.frame(width: 450, height: 400)
.frame(width: 640, height: 560)
.onAppear {
pendingNamespace = controller.customNamespace
pendingHFToken = controller.hfToken
pendingHFEndpoint = controller.hfEndpoint
pendingEnableImageModels = controller.enableImageModels
pendingOfflineMode = controller.offlineMode
pendingFastSynchEnabled = controller.fastSynchEnabled
pendingDefaultModelsDir = controller.defaultModelsDir
pendingAdditionalModelsDirs = controller.additionalModelsDirs
pendingReadOnlyModelsDirs = controller.readOnlyModelsDirs
pendingCustomEnvironmentVariables = controller.customEnvironmentVariables
needsRestart = false
}
}
@@ -56,9 +68,9 @@ struct SettingsView: View {
Form {
Section {
LabeledContent("Cluster Namespace") {
TextField("default", text: $pendingNamespace)
TextField("", text: $pendingNamespace, prompt: Text("default"))
.textFieldStyle(.roundedBorder)
.frame(width: 200)
.frame(width: 260)
}
Text("Nodes with the same namespace form a cluster. Leave empty for default.")
.font(.caption)
@@ -67,9 +79,9 @@ struct SettingsView: View {
Section {
LabeledContent("HuggingFace Token") {
SecureField("optional", text: $pendingHFToken)
SecureField("", text: $pendingHFToken, prompt: Text("optional"))
.textFieldStyle(.roundedBorder)
.frame(width: 200)
.frame(width: 260)
}
Text("Required for gated models. Get yours at huggingface.co/settings/tokens")
.font(.caption)
@@ -78,9 +90,9 @@ struct SettingsView: View {
Section {
LabeledContent("HuggingFace Endpoint") {
TextField("default", text: $pendingHFEndpoint)
TextField("", text: $pendingHFEndpoint, prompt: Text("default"))
.textFieldStyle(.roundedBorder)
.frame(width: 200)
.frame(width: 260)
}
Text("Defaults to huggingface.co. Use a mirror (e.g. hf-mirror.com) for China.")
.font(.caption)
@@ -137,6 +149,23 @@ struct SettingsView: View {
private var advancedTab: some View {
Form {
Section("Performance") {
Toggle("Fast Synch Enabled", isOn: $pendingFastSynchEnabled)
Text(
"Experimental: enables fast CPU to GPU synchronization. Can sometimes cause a \"GPU lock\" where inference hangs for ~10 seconds before starting. Necessary for low latency with RDMA and Tensor Parallelism."
)
.font(.caption)
.foregroundColor(.secondary)
HStack {
Spacer()
Button("Save & Restart") {
applyAdvancedSettings()
}
.disabled(!hasAdvancedChanges)
}
}
Section("Onboarding") {
HStack {
VStack(alignment: .leading) {
@@ -171,8 +200,6 @@ struct SettingsView: View {
VStack(alignment: .leading, spacing: 2) {
rdmaStatusView
}
sendBugReportButton
}
Section("Danger Zone") {
@@ -193,6 +220,128 @@ struct SettingsView: View {
.padding()
}
// MARK: - Environment Tab
private var environmentTab: some View {
Form {
Section("Models Directories") {
LabeledContent("Default Models Directory") {
TextField(
"",
text: $pendingDefaultModelsDir,
prompt: Text("~/.exo/models")
)
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
.frame(width: 260)
}
Text("Sets EXO_DEFAULT_MODELS_DIR. Where models are downloaded.")
.font(.caption)
.foregroundColor(.secondary)
LabeledContent("Additional Directories") {
TextField(
"",
text: $pendingAdditionalModelsDirs,
prompt: Text("optional, colon-separated")
)
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
.frame(width: 260)
}
Text("Sets EXO_MODELS_DIRS. Extra writable model directories.")
.font(.caption)
.foregroundColor(.secondary)
LabeledContent("Read-Only Directories") {
TextField(
"",
text: $pendingReadOnlyModelsDirs,
prompt: Text("optional, colon-separated")
)
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
.frame(width: 260)
}
Text("Sets EXO_MODELS_READ_ONLY_DIRS. Never written to.")
.font(.caption)
.foregroundColor(.secondary)
}
Section("Custom Environment Variables") {
Text(
"Escape hatch for env vars that don't have typed fields above. "
+ "Values here override the typed fields on conflict."
)
.font(.caption)
.foregroundColor(.secondary)
if pendingCustomEnvironmentVariables.isEmpty {
Text("No custom variables.")
.font(.caption)
.foregroundColor(.secondary)
} else {
ForEach($pendingCustomEnvironmentVariables) { $variable in
HStack(alignment: .center, spacing: 8) {
VStack(spacing: 4) {
TextField("key", text: $variable.key)
.labelsHidden()
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
TextField("value", text: $variable.value)
.labelsHidden()
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
}
VStack(spacing: 4) {
Button {
pendingCustomEnvironmentVariables.removeAll {
$0.id == variable.id
}
} label: {
Image(systemName: "minus.circle")
}
.buttonStyle(.borderless)
.help("Remove variable")
if !isValidEnvironmentVariableName(variable.key) {
Image(systemName: "exclamationmark.triangle.fill")
.foregroundColor(.orange)
.help(
"Invalid environment variable name. "
+ "Must match [A-Za-z_][A-Za-z0-9_]*."
)
}
}
}
}
}
HStack {
Button {
pendingCustomEnvironmentVariables.append(
CustomEnvironmentVariable()
)
} label: {
Label("Add Variable", systemImage: "plus")
}
Spacer()
}
}
Section {
HStack {
Spacer()
Button("Save & Restart") {
applyEnvironmentSettings()
}
.disabled(!hasEnvironmentChanges)
}
}
}
.formStyle(.grouped)
.padding()
}
// MARK: - About Tab
private var aboutTab: some View {
@@ -351,63 +500,30 @@ struct SettingsView: View {
}
}
private var sendBugReportButton: some View {
VStack(alignment: .leading, spacing: 4) {
Button {
Task {
await sendBugReport()
}
} label: {
HStack {
if bugReportInFlight {
ProgressView()
.scaleEffect(0.6)
}
Text("Send Bug Report")
.font(.caption)
.fontWeight(.semibold)
Spacer()
}
}
.disabled(bugReportInFlight)
if let message = bugReportMessage {
Text(message)
.font(.caption2)
.foregroundColor(.secondary)
.fixedSize(horizontal: false, vertical: true)
}
}
}
// MARK: - Actions
private func sendBugReport() async {
bugReportInFlight = true
bugReportMessage = "Collecting logs..."
let service = BugReportService()
do {
let outcome = try await service.sendReport(isManual: true)
bugReportMessage = outcome.message
} catch {
bugReportMessage = error.localizedDescription
}
bugReportInFlight = false
}
private func showUninstallConfirmationAlert() {
let alert = NSAlert()
alert.messageText = "Uninstall EXO"
alert.informativeText = """
This will remove EXO and all its system components:
This will remove EXO and all its components:
• Network configuration daemon
• Launch at login registration
• EXO network location
• EXO data directory (~/.exo)
The app will be moved to Trash.
"""
alert.alertStyle = .warning
let checkbox = NSButton(
checkboxWithTitle: "Keep downloaded models (~/.exo/models)",
target: nil, action: nil)
checkbox.state = .off
checkbox.sizeToFit()
alert.accessoryView = checkbox
alert.addButton(withTitle: "Uninstall")
alert.addButton(withTitle: "Cancel")
@@ -417,11 +533,11 @@ struct SettingsView: View {
let response = alert.runModal()
if response == .alertFirstButtonReturn {
performUninstall()
performUninstall(keepModels: checkbox.state == .on)
}
}
private func performUninstall() {
private func performUninstall(keepModels: Bool) {
uninstallInProgress = true
controller.cancelPendingLaunch()
@@ -431,6 +547,7 @@ struct SettingsView: View {
DispatchQueue.global(qos: .utility).async {
do {
try NetworkSetupHelper.uninstall()
try Self.removeExoDirectory(keepModels: keepModels)
DispatchQueue.main.async {
LaunchAtLoginHelper.disable()
@@ -454,6 +571,23 @@ struct SettingsView: View {
}
}
private static func removeExoDirectory(keepModels: Bool) throws {
let fm = FileManager.default
let exoDir = ExoProcessController.exoDirectoryURL
guard fm.fileExists(atPath: exoDir.path) else { return }
if !keepModels {
try fm.removeItem(at: exoDir)
return
}
let contents = try fm.contentsOfDirectory(
at: exoDir, includingPropertiesForKeys: nil, options: [])
for entry in contents where entry.lastPathComponent != "models" {
try? fm.removeItem(at: entry)
}
}
private func moveAppToTrash() {
guard let appURL = Bundle.main.bundleURL as URL? else { return }
do {
@@ -475,6 +609,17 @@ struct SettingsView: View {
pendingEnableImageModels != controller.enableImageModels
}
private var hasAdvancedChanges: Bool {
pendingFastSynchEnabled != controller.fastSynchEnabled
}
private var hasEnvironmentChanges: Bool {
pendingDefaultModelsDir != controller.defaultModelsDir
|| pendingAdditionalModelsDirs != controller.additionalModelsDirs
|| pendingReadOnlyModelsDirs != controller.readOnlyModelsDirs
|| pendingCustomEnvironmentVariables != controller.customEnvironmentVariables
}
private func applyGeneralSettings() {
controller.customNamespace = pendingNamespace
controller.hfToken = pendingHFToken
@@ -488,6 +633,75 @@ struct SettingsView: View {
restartIfRunning()
}
private func applyAdvancedSettings() {
controller.fastSynchEnabled = pendingFastSynchEnabled
restartIfRunning()
}
private func applyEnvironmentSettings() {
controller.defaultModelsDir = pendingDefaultModelsDir.trimmingCharacters(
in: .whitespaces)
controller.additionalModelsDirs = pendingAdditionalModelsDirs.trimmingCharacters(
in: .whitespaces)
controller.readOnlyModelsDirs = pendingReadOnlyModelsDirs.trimmingCharacters(
in: .whitespaces)
pendingDefaultModelsDir = controller.defaultModelsDir
pendingAdditionalModelsDirs = controller.additionalModelsDirs
pendingReadOnlyModelsDirs = controller.readOnlyModelsDirs
// Trim whitespace from keys and drop empty ones so that the stored
// form matches what is actually injected into the child process and
// hasEnvironmentChanges doesn't show a stale diff after save.
let trimmed: [CustomEnvironmentVariable] =
pendingCustomEnvironmentVariables.compactMap { variable in
let key = variable.key.trimmingCharacters(in: .whitespaces)
guard !key.isEmpty else { return nil }
return CustomEnvironmentVariable(
id: variable.id, key: key, value: variable.value
)
}
// De-duplicate keys, keeping the last occurrence. This matches the
// effective semantics of the dictionary assignment in
// ExoProcessController.makeEnvironment and avoids silently losing
// visible rows after save.
var seenKeys = Set<String>()
var deduplicatedReversed: [CustomEnvironmentVariable] = []
for variable in trimmed.reversed() {
if seenKeys.insert(variable.key).inserted {
deduplicatedReversed.append(variable)
}
}
let sanitized = Array(deduplicatedReversed.reversed())
pendingCustomEnvironmentVariables = sanitized
controller.customEnvironmentVariables = sanitized
restartIfRunning()
}
/// Validates a POSIX-style environment variable name:
/// `[A-Za-z_][A-Za-z0-9_]*`. Uses an ASCII-only charset so that
/// Unicode letters (e.g. `ñ`, Cyrillic) are rejected in line with what
/// the help tooltip advertises. Empty strings are treated as valid
/// here so that a freshly added blank row does not immediately look
/// broken; the save step filters empty keys out instead.
private func isValidEnvironmentVariableName(_ key: String) -> Bool {
if key.isEmpty { return true }
let headAllowed = CharacterSet(
charactersIn: "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz_"
)
let tailAllowed = headAllowed.union(CharacterSet(charactersIn: "0123456789"))
guard let first = key.unicodeScalars.first, headAllowed.contains(first) else {
return false
}
for scalar in key.unicodeScalars.dropFirst() {
if !tailAllowed.contains(scalar) { return false }
}
return true
}
private func restartIfRunning() {
if controller.status == .running || controller.status == .starting {
controller.restart()
@@ -30,7 +30,7 @@ final class SettingsWindowController: ObservableObject {
let hostingView = NSHostingView(rootView: settingsView)
let newWindow = NSWindow(
contentRect: NSRect(x: 0, y: 0, width: 450, height: 400),
contentRect: NSRect(x: 0, y: 0, width: 640, height: 560),
styleMask: [.titled, .closable],
backing: .buffered,
defer: false
+63 -7
View File
@@ -3,25 +3,55 @@
# EXO Uninstaller Script
#
# This script removes all EXO system components that persist after deleting the app.
# Run with: sudo ./uninstall-exo.sh
# Run with: sudo ./uninstall-exo.sh [--keep-models]
#
# Options:
# --keep-models Preserve ~/.exo/models when removing the EXO data directory.
#
# Components removed:
# - LaunchDaemon: /Library/LaunchDaemons/io.exo.networksetup.plist
# - Network script: /Library/Application Support/EXO/
# - Log files: /var/log/io.exo.networksetup.*
# - Network location: "exo"
# - EXO data directory: ~/.exo (or all of ~/.exo except models/ when --keep-models is set)
# - Launch at login registration
#
set -euo pipefail
KEEP_MODELS=0
for arg in "$@"; do
case "$arg" in
--keep-models)
KEEP_MODELS=1
;;
-h | --help)
echo "Usage: sudo ./uninstall-exo.sh [--keep-models]"
echo " --keep-models Preserve ~/.exo/models when removing the EXO data directory."
exit 0
;;
*)
echo "Unknown argument: $arg" >&2
echo "Usage: sudo ./uninstall-exo.sh [--keep-models]" >&2
exit 2
;;
esac
done
LABEL="io.exo.networksetup"
SCRIPT_DEST="/Library/Application Support/EXO/disable_bridge_enable_dhcp.sh"
# Current script path. Older installs used a different filename; keep the
# legacy path here so a fresh uninstall still cleans up upgraded machines.
CURRENT_SCRIPT_DEST="/Library/Application Support/EXO/disable_bridge.sh"
LEGACY_SCRIPT_DEST="/Library/Application Support/EXO/disable_bridge_enable_dhcp.sh"
PLIST_DEST="/Library/LaunchDaemons/io.exo.networksetup.plist"
LOG_OUT="/var/log/${LABEL}.log"
LOG_ERR="/var/log/${LABEL}.err.log"
APP_BUNDLE_ID="io.exo.EXO"
# Resolve the invoking user's home, even when run via sudo.
USER_HOME="$(eval echo "~${SUDO_USER:-$USER}")"
EXO_DIR="$USER_HOME/.exo"
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
@@ -69,11 +99,17 @@ else
echo_warn "LaunchDaemon plist not found (already removed?)"
fi
# Remove the script and parent directory
if [[ -f $SCRIPT_DEST ]]; then
rm -f "$SCRIPT_DEST"
echo_info "Removed network setup script"
else
# Remove the script (current and legacy filenames) — backwards-compatible:
# tolerate either, both, or neither being present.
removed_any_script=0
for script in "$CURRENT_SCRIPT_DEST" "$LEGACY_SCRIPT_DEST"; do
if [[ -f $script ]]; then
rm -f "$script"
echo_info "Removed network setup script: $script"
removed_any_script=1
fi
done
if [[ $removed_any_script -eq 0 ]]; then
echo_warn "Network setup script not found (already removed?)"
fi
@@ -115,6 +151,22 @@ if networksetup -listnetworkservices 2>/dev/null | grep -q "Thunderbolt Bridge";
echo_info "Re-enabled Thunderbolt Bridge"
fi
# Remove EXO data directory (~/.exo)
EXO_DIR_REMOVED=""
if [[ -d $EXO_DIR ]]; then
if [[ $KEEP_MODELS == "1" && -d "$EXO_DIR/models" ]]; then
find "$EXO_DIR" -mindepth 1 -maxdepth 1 ! -name models -exec rm -rf {} +
EXO_DIR_REMOVED="kept_models"
echo_info "Removed ~/.exo (preserved models/)"
else
rm -rf "$EXO_DIR"
EXO_DIR_REMOVED="full"
echo_info "Removed ~/.exo"
fi
else
echo_warn "~/.exo not found (already removed?)"
fi
# Note about launch at login registration
# SMAppService-based login items cannot be removed from a shell script.
# They can only be unregistered from within the app itself or manually via System Settings.
@@ -144,6 +196,10 @@ echo " • Network setup LaunchDaemon"
echo " • Network configuration script"
echo " • Log files"
echo " • 'exo' network location"
case "$EXO_DIR_REMOVED" in
full) echo " • EXO data directory (~/.exo)" ;;
kept_models) echo " • EXO data directory (~/.exo, models preserved)" ;;
esac
echo ""
echo "Your network has been restored to use the 'Automatic' location."
echo "Thunderbolt Bridge has been re-enabled (if present)."
+160
View File
@@ -0,0 +1,160 @@
# Exo-Bench — Methodology
exo bench measures inference throughput and resource consumption of an exo cluster under controlled conditions. It sends prompts to the `/bench/chat/completions` endpoint, collects server-reported timing statistics, and records system-level metrics (power, GPU utilisation, temperature) throughout each run.
The goal is to have accurate, transparent and reproducible numbers to compare speed and scaling across different models and different setups, and to be able to track these results as optimizations and features are added to EXO.
Below is a technical summary of how Exo-Bench works. While the methodology and benchmark may change over time, this document will be kept up to date whenever this happens. If you find an issue with the methodology, or would like a feature to be added, please open a GitHub issue!
---
## Prompt Construction
Benchmarks need prompts of an exact token length. Unfortunately, we do not have direct access to the model but just the chat completion endpoint. To get around this fact, we create a request that will tokenise to a certain prompt length.
This is achieved by:
1. Tokenising a sample message through the model's `apply_chat_template()` to measure overhead (system tokens, special tokens, chat formatting).
2. Binary-searching over a repeated atom string (default `"a "`) to find the content length that produces exactly the target number of tokens after template expansion.
3. Returning both the content string and the verified token count.
The actual token count is recorded in every result row as `pp_tokens`, so downstream analysis can confirm the prompt hit its target.
Chat template formatting means that it may be impossible to attain very small pp benchmarks. e.g. pp=32 may not work. This tradeoff was made because the result of such a small prompt does not seem very interesting or useful for any real-world use cases.
---
## Bench Endpoint
When a request reaches the server via the `/bench/chat/completions` endpoint, three things change compared to a normal chat completion:
- **KV prefix cache is disabled by default**. Every request starts from a cold cache, ensuring prefill timing is not affected by prior requests. See [Prefix Cache Mode](#prefix-cache-mode) for the `--use-prefix-cache` option.
- **EOS tokens are banned**. A logits processor suppresses all end-of-sequence tokens, forcing the model to generate exactly `max_tokens` tokens. This guarantees consistent generation length for fair TPS comparison — the model cannot short-circuit a run by stopping early.
- **No model output parsing**. The bench collection path concatenates raw token text without any model-specific post-processing (thinking tag extraction, structured output handling, etc.). This is to avoid model outputs such as tool parsing or any structural mistakes from breaking the benchmark - we are testing for speed; see Exo-Eval for performance metrics.
---
## Timing
### Prefill TPS
Measured server-side per task.
```
prefill_tps = num_prompt_tokens / prefill_wall_seconds
```
### Generation TPS
Measured server-side per task. Each task records wall-clock timestamps as tokens arrive:
- First generated token: timestamp recorded
- Every subsequent token: timestamp updated
When generation completes:
```
gen_span = last_token_time - first_token_time
generation_tps = (completion_tokens - 1) / gen_span
```
The first token is excluded from the numerator because the rate measures inter-token throughput — the time between the first and last token divided by the number of intervals.
This does mean that tg=1 will not work.
---
## Concurrency
### Single Request
The client records wall-clock `elapsed_s` around the HTTP round-trip (network latency + server prefill + generation + response serialisation). This is a convenience metric for end-to-end latency. The authoritative TPS numbers come from the server-side per-task timing in the `generation_stats` response.
### Concurrent Requests
When `--concurrency N` is set with N > 1, all N requests must hit the server at the same instant. The mechanism:
1. The prompt is built once and shared across all threads.
2. Each thread gets its own HTTP connection.
3. A thread barrier blocks all threads until every thread is ready.
4. The first thread past the barrier records the batch start time and signals the others.
5. All threads use the same start time as their reference, then fire their HTTP request.
6. Each thread's `elapsed_s` is measured from the shared start time to its own response completion.
**Batch wall time** is the maximum `elapsed_s` across all N requests — the time until the last request finishes.
### Aggregate TPS
```
per_req_tps = max(generation_tps across N concurrent requests)
agg_gen_tps = per_req_tps * concurrency
```
`max` is used instead of `mean` because all requests run in parallel against the same model. The fastest request's generation rate represents the system's per-stream throughput capacity; multiplying by concurrency gives aggregate throughput.
---
## Prefix Cache Mode
When `--use-prefix-cache` is passed, the KV prefix cache remains active during benchmarking. This speeds up repeated runs by skipping redundant prefill work, which is useful when prompt processing is not the focus of the benchmark (e.g. when measuring generation throughput or power consumption across many configurations).
Each response includes a `prefix_cache_hit` field (`"none"`, `"partial"`, or `"exact"`):
- **none**: Cold prefill — no cached KV state was available. The reported `prompt_tps` is the real prefill throughput.
- **partial**: A prefix of the prompt was found in cache. Only the remaining tokens were prefilled. The reported `prompt_tps` reflects the real throughput on the uncached portion. This occurs when multiple ascending `--pp` values share a common prefix (e.g. `--pp 1000,5000` — the 5000-token prompt reuses the 1000-token cache entry and prefills the remaining 4000 tokens).
- **exact**: The entire prompt was found in cache (e.g. same `--pp` value on a `--repeat`). No prefill work was done. The reported `prompt_tps` is the TPS from when the cache entry was originally created, not a new measurement.
**Prompt TPS is approximate in this mode.** Exact-hit runs report the stored TPS from the original cold/partial prefill rather than a freshly measured value. For accurate cold prefill numbers, run without `--use-prefix-cache`.
Ascending `--pp` order (e.g. `--pp 1000,5000,10000`) gives the most useful data: each size gets a meaningful partial hit except the first which is cold. Descending order produces exact hits with approximate TPS from a longer prompt's original run.
---
## Warmup
Before measurement begins, `--warmup N` (default: 0) discarded requests are sent using the first pp/tg pair. Warmup results are not included in the output.
---
## System Metrics
A background thread polls each node at 1 Hz, collecting:
- GPU utilisation (%)
- Temperature (C)
- System power draw (W)
- CPU cluster usage (performance and efficiency cores)
**Energy** is computed via trapezoidal integration of the power samples over each inference window (the wall-clock span of each benchmark request or concurrent batch). Average power is `total_joules / total_inference_seconds`.
---
## Output Format
Results are written as JSON with three top-level keys:
- **`runs`**: Array of per-request result objects, each containing:
- `elapsed_s`, `output_text_preview` (first 200 chars)
- `stats`: `{ prompt_tps, generation_tps, prompt_tokens, generation_tokens, peak_memory_usage }`
- Placement metadata: `model_id`, `placement_sharding`, `placement_instance_meta`, `placement_nodes`
- Run metadata: `pp_tokens`, `tg`, `repeat_index`, `concurrency`, `concurrent_index`
- `download_duration_s` (if model was freshly downloaded)
- **`cluster`**: Cluster state snapshot at time of benchmarking.
- **`system_metrics`**: Per-node time-series samples (GPU, power, temperature).
---
## Reproducing Results
```bash
cd bench && uv run python exo_bench.py \
--model "mlx-community/Qwen3.5-27B-4bit" \
--instance-meta jaccl \
--sharding tensor \
--min-nodes 2 --max-nodes 2 \
--pp 512 4096 --tg 128 \
--repeat 3 \
--warmup 1
```
Run --help for all the available flags.
+77 -22
View File
@@ -7,7 +7,7 @@
# name, patterns, reasoning
#
# Optional per-model overrides (CLI flags take priority over these):
# temperature, top_p, max_tokens, reasoning_effort
# temperature, top_p, max_tokens, reasoning_effort, enable_thinking
#
# Fallback defaults (when no per-model config):
# reasoning: temperature=1.0, max_tokens=131072, reasoning_effort="high"
@@ -18,10 +18,9 @@
# ─── Qwen3.5 (Feb 2026) ─────────────────────────────────────────────
# Source: HuggingFace model cards (Qwen/Qwen3.5-*)
# 35B-A3B thinking general: temp=1.0, top_p=0.95, top_k=20
# 397B thinking: temp=0.6, top_p=0.95, top_k=20
# Non-thinking: temp=0.7, top_p=0.8, top_k=20
# max_tokens: 32768 general, 81920 for complex math/code
# Model card recommends: temp=0.6, top_p=0.95, top_k=20
# We omit top_k to match vllm eval (which doesn't set it).
# max_tokens=121072 to match vllm eval (131072 context - 10000 safety margin).
[[model]]
name = "Qwen3.5 2B"
@@ -29,7 +28,8 @@ patterns = ["Qwen3.5-2B"]
reasoning = true
temperature = 0.6
top_p = 0.95
max_tokens = 81920
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 9B"
@@ -37,7 +37,8 @@ patterns = ["Qwen3.5-9B"]
reasoning = true
temperature = 0.6
top_p = 0.95
max_tokens = 81920
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 27B"
@@ -45,15 +46,17 @@ patterns = ["Qwen3.5-27B"]
reasoning = true
temperature = 0.6
top_p = 0.95
max_tokens = 81920
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 35B A3B"
patterns = ["Qwen3.5-35B-A3B"]
reasoning = true
temperature = 1.0
temperature = 0.6
top_p = 0.95
max_tokens = 81920
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 122B A10B"
@@ -61,7 +64,8 @@ patterns = ["Qwen3.5-122B-A10B"]
reasoning = true
temperature = 0.6
top_p = 0.95
max_tokens = 81920
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 397B A17B"
@@ -69,12 +73,14 @@ patterns = ["Qwen3.5-397B-A17B"]
reasoning = true
temperature = 0.6
top_p = 0.95
max_tokens = 81920
enable_thinking = true
max_tokens = 121072
# ─── Qwen3 (Apr 2025) ───────────────────────────────────────────────
# Source: HuggingFace model cards (Qwen/Qwen3-*)
# Thinking: temp=0.6, top_p=0.95, top_k=20
# Non-thinking: temp=0.7, top_p=0.8, top_k=20
# Model card recommends: temp=0.6, top_p=0.95, top_k=20
# We omit top_k to match vllm eval (which doesn't set it).
# Non-thinking: temp=0.7, top_p=0.8
# max_tokens: 32768 general, 38912 for complex math/code
[[model]]
@@ -83,6 +89,7 @@ patterns = ["Qwen3-0.6B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
@@ -91,6 +98,7 @@ patterns = ["Qwen3-30B-A3B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
@@ -99,6 +107,7 @@ patterns = ["Qwen3-235B-A22B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
@@ -107,6 +116,7 @@ patterns = ["Qwen3-Next-80B-A3B-Thinking"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
@@ -129,9 +139,9 @@ max_tokens = 16384
name = "Qwen3 Coder Next"
patterns = ["Qwen3-Coder-Next"]
reasoning = false
temperature = 0.7
top_p = 0.8
max_tokens = 16384
temperature = 1.0
top_p = 0.95
max_tokens = 121072
# ─── GPT-OSS (OpenAI) ───────────────────────────────────────────────
# Source: OpenAI GitHub README + HuggingFace discussion #21
@@ -165,10 +175,38 @@ patterns = ["DeepSeek-V3.1"]
reasoning = true
temperature = 0.0
[[model]]
name = "DeepSeek V3.2"
patterns = ["DeepSeek-V3.2"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
# ─── NVIDIA Nemotron ───────────────────────────────────────────────────
# Source: HuggingFace model cards
# All variants: temp=1.0, top_p=0.95, enable_thinking=true
[[model]]
name = "Nemotron Cascade 2 30B A3B"
patterns = ["Nemotron-Cascade-2-30B-A3B"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
[[model]]
name = "Nemotron 3 Super 120B A12B"
patterns = ["Nemotron-3-Super-120B-A12B", "NVIDIA-Nemotron-3-Super-120B-A12B"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
# ─── GLM (ZhipuAI / THUDM) ──────────────────────────────────────────
# Source: HuggingFace model cards + generation_config.json + docs.z.ai
# GLM 4.5+: temp=1.0, top_p=0.95
# Reasoning tasks: 131072 max_tokens; coding/SWE tasks: temp=0.7
# max_tokens=121072 to match vllm eval (131072 context - 10000 safety margin)
[[model]]
name = "GLM-5"
@@ -176,7 +214,8 @@ patterns = ["GLM-5"]
reasoning = true
temperature = 1.0
top_p = 0.95
max_tokens = 131072
enable_thinking = true
max_tokens = 121072
[[model]]
name = "GLM 4.5 Air"
@@ -191,7 +230,8 @@ patterns = ["GLM-4.7-"]
reasoning = true
temperature = 1.0
top_p = 0.95
max_tokens = 131072
enable_thinking = true
max_tokens = 121072
# Note: matches both GLM-4.7 and GLM-4.7-Flash
# ─── Kimi (Moonshot AI) ─────────────────────────────────────────────
@@ -213,7 +253,8 @@ patterns = ["Kimi-K2.5"]
reasoning = true
temperature = 1.0
top_p = 0.95
max_tokens = 131072
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Kimi K2 Instruct"
@@ -223,7 +264,17 @@ temperature = 0.6
# ─── MiniMax ─────────────────────────────────────────────────────────
# Source: HuggingFace model cards + generation_config.json
# All models: temp=1.0, top_p=0.95, top_k=40
# All models: temp=1.0, top_p=0.95
# max_tokens=90000 to match vllm eval (100000 context - 10000 safety margin)
[[model]]
name = "MiniMax M2.7"
patterns = ["MiniMax-M2.7"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 90000
[[model]]
name = "MiniMax M2.5"
@@ -231,6 +282,8 @@ patterns = ["MiniMax-M2.5"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 90000
[[model]]
name = "MiniMax M2.1"
@@ -251,6 +304,8 @@ patterns = ["Step-3.5-Flash"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 121072
# ─── Llama (Meta) ───────────────────────────────────────────────────
# Source: generation_config.json + meta-llama/llama-models generation.py
+89 -39
View File
@@ -3,11 +3,13 @@ from __future__ import annotations
import argparse
import contextlib
import io
import json
import os
import sys
import time
import tomllib
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Literal
@@ -17,6 +19,7 @@ from harness import (
ExoClient,
ExoHttpError,
add_common_instance_args,
capture_cluster_snapshot,
instance_id_from_instance,
nodes_used_in_instance,
resolve_model_short_id,
@@ -208,7 +211,7 @@ def _openai_build_request(
"model": model,
"messages": messages,
"tools": tools,
"max_tokens": 16384,
"max_tokens": 4096,
"temperature": 0.0,
}
return "/v1/chat/completions", body
@@ -275,7 +278,7 @@ def _openai_build_followup(
"model": model,
"messages": followup_messages,
"tools": tools,
"max_tokens": 16384,
"max_tokens": 4096,
"temperature": 0.0,
}
return "/v1/chat/completions", body
@@ -378,7 +381,7 @@ def _claude_build_request(
"model": model,
"messages": claude_messages,
"tools": claude_tools,
"max_tokens": 16384,
"max_tokens": 4096,
"temperature": 0.0,
}
if system_content is not None:
@@ -488,7 +491,7 @@ def _claude_build_followup(
"model": model,
"messages": claude_messages,
"tools": claude_tools,
"max_tokens": 16384,
"max_tokens": 4096,
"temperature": 0.0,
}
if system_content is not None:
@@ -912,6 +915,12 @@ Examples:
default=1,
help="Repeat each scenario N times (default: 1)",
)
parser.add_argument(
"--concurrency",
type=int,
default=1,
help="Run up to N scenarios in parallel against the same instance (default: 1)",
)
parser.add_argument(
"--scenarios",
nargs="*",
@@ -934,6 +943,13 @@ Examples:
)
args = parser.parse_args()
if args.concurrency < 1:
print(
f"--concurrency must be >= 1 (got {args.concurrency})",
file=sys.stderr,
)
sys.exit(2)
all_scenarios = load_scenarios(SCENARIOS_PATH)
if args.scenarios:
scenarios = [s for s in all_scenarios if s.name in args.scenarios]
@@ -1006,44 +1022,75 @@ Examples:
sys.exit(1)
time.sleep(1)
cluster_snapshot = capture_cluster_snapshot(exo)
all_results: list[ScenarioResult] = []
tasks: list[tuple[int, Scenario, ApiName]] = [
(run_idx, scenario, api_name)
for run_idx in range(args.repeat)
for scenario in scenarios
for api_name in api_names
]
def _run_one(
http_client: httpx.Client,
task: tuple[int, Scenario, ApiName],
) -> tuple[tuple[int, Scenario, ApiName], list[ScenarioResult], str]:
run_idx, scenario, api_name = task
buf = io.StringIO()
run_tag = f"[run {run_idx + 1}/{args.repeat}]" if args.repeat > 1 else ""
print(
f"\n {run_tag}[{api_name:>9}] {scenario.name}: {scenario.description}",
file=buf,
)
scenario_results = run_scenario(
http_client,
args.host,
args.port,
full_model_id,
scenario,
api_name,
args.timeout,
args.verbose,
)
for r in scenario_results:
status = "PASS" if r.passed else "FAIL"
print(
f" [{r.phase:>10}] {status} ({r.latency_ms:.0f}ms)",
file=buf,
)
for check_name, check_ok in r.checks.items():
mark = "+" if check_ok else "-"
print(f" {mark} {check_name}", file=buf)
if r.error:
print(f" ! {r.error}", file=buf)
return task, scenario_results, buf.getvalue()
try:
with httpx.Client() as http_client:
for run_idx in range(args.repeat):
if args.repeat > 1:
print(f"\n--- Run {run_idx + 1}/{args.repeat} ---", file=log)
for scenario in scenarios:
for api_name in api_names:
print(
f"\n [{api_name:>9}] {scenario.name}: {scenario.description}",
file=log,
)
scenario_results = run_scenario(
http_client,
args.host,
args.port,
full_model_id,
scenario,
api_name,
args.timeout,
args.verbose,
)
if args.concurrency == 1:
current_run = -1
for task in tasks:
run_idx = task[0]
if args.repeat > 1 and run_idx != current_run:
print(f"\n--- Run {run_idx + 1}/{args.repeat} ---", file=log)
current_run = run_idx
_, scenario_results, buffered = _run_one(http_client, task)
all_results.extend(scenario_results)
log.write(buffered)
log.flush()
else:
print(
f"Running {len(tasks)} tasks with concurrency={args.concurrency}",
file=log,
)
with ThreadPoolExecutor(max_workers=args.concurrency) as pool:
futures = [pool.submit(_run_one, http_client, t) for t in tasks]
for fut in as_completed(futures):
_, scenario_results, buffered = fut.result()
all_results.extend(scenario_results)
for r in scenario_results:
status = "PASS" if r.passed else "FAIL"
print(
f" [{r.phase:>10}] {status} ({r.latency_ms:.0f}ms)",
file=log,
)
for check_name, check_ok in r.checks.items():
mark = "+" if check_ok else "-"
print(f" {mark} {check_name}", file=log)
if r.error:
print(f" ! {r.error}", file=log)
log.write(buffered)
log.flush()
finally:
try:
exo.request_json("DELETE", f"/instance/{instance_id}")
@@ -1084,16 +1131,19 @@ Examples:
print(f" - {r.name} [{r.api}/{r.phase}]: {r.error}", file=log)
json_results = [result_to_dict(r) for r in all_results]
output: dict[str, Any] = {"results": json_results}
if cluster_snapshot:
output["cluster"] = cluster_snapshot
if args.stdout:
print(json.dumps(json_results, indent=2))
print(json.dumps(output, indent=2))
else:
json_path = args.json_out
parent = os.path.dirname(json_path)
if parent:
os.makedirs(parent, exist_ok=True)
with open(json_path, "w") as f:
json.dump(json_results, f, indent=2)
json.dump(output, f, indent=2)
f.write("\n")
print(f"\nJSON results written to {json_path}", file=log)
+459 -101
View File
@@ -22,6 +22,7 @@ import contextlib
import itertools
import json
import sys
import threading
import time
from collections.abc import Callable
from concurrent.futures import ThreadPoolExecutor, as_completed
@@ -33,7 +34,10 @@ from harness import (
ExoClient,
ExoHttpError,
add_common_instance_args,
capture_cluster_snapshot,
find_existing_instance,
instance_id_from_instance,
node_ids_from_instance,
nodes_used_in_instance,
resolve_model_short_id,
run_planning_phase,
@@ -76,7 +80,7 @@ def load_tokenizer_for_bench(model_id: str) -> Any:
model_path = Path(
snapshot_download(
model_id,
allow_patterns=["*.json", "*.py", "*.tiktoken", "*.model"],
allow_patterns=["*.json", "*.py", "*.tiktoken", "*.model", "*.jinja"],
)
)
@@ -119,8 +123,48 @@ def load_tokenizer_for_bench(model_id: str) -> Any:
return hf_tokenizer
# Default: use AutoTokenizer
return AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
# TODO: Change back to using only transformers
try:
return AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
except (AttributeError, ValueError):
from huggingface_hub import snapshot_download
from transformers import PretrainedConfig
model_path = Path(
snapshot_download(
model_id,
allow_patterns=[
"*.json",
"*.py",
"tokenizer.model",
"*.tiktoken",
"tiktoken.model",
"*.txt",
"*.jsonl",
"*.jinja",
],
)
)
stub_kwargs: dict[str, Any] = {}
config_file = model_path / "config.json"
if config_file.exists():
with open(config_file) as f:
raw = json.load(f)
for key in (
"model_type",
"max_position_embeddings",
"vocab_size",
"bos_token_id",
"eos_token_id",
"pad_token_id",
):
if key in raw:
stub_kwargs[key] = raw[key]
return AutoTokenizer.from_pretrained(
str(model_path),
config=PretrainedConfig(**stub_kwargs),
trust_remote_code=True,
)
def format_peak_memory(b: float) -> str:
@@ -131,6 +175,91 @@ def format_peak_memory(b: float) -> str:
raise ValueError("You're using petabytes of memory. Something went wrong...")
_SAMPLER_METRICS = ("gpuUsage", "temp", "sysPower", "pcpuUsage", "ecpuUsage")
class SystemMetricsSampler:
def __init__(self, client: ExoClient, node_ids: list[str], interval_s: float = 1.0):
self._client = client
self._node_ids = node_ids
self._interval_s = interval_s
self._samples: dict[str, list[tuple[float, dict[str, float]]]] = {
nid: [] for nid in node_ids
}
self._stop = threading.Event()
self._thread: threading.Thread | None = None
def start(self) -> None:
self._stop.clear()
self._thread = threading.Thread(target=self._poll_loop, daemon=True)
self._thread.start()
def stop(self) -> None:
self._stop.set()
if self._thread:
self._thread.join(timeout=5)
def _poll_loop(self) -> None:
while not self._stop.is_set():
t = time.monotonic()
for nid in self._node_ids:
try:
data = self._client.get_node_system(nid)
if data:
self._samples[nid].append(
(t, {k: data.get(k, 0.0) for k in _SAMPLER_METRICS})
)
except Exception:
pass
self._stop.wait(self._interval_s)
def energy_between(self, t0: float, t1: float) -> float:
total_joules = 0.0
for _nid, samples in self._samples.items():
window = [(t, s["sysPower"]) for t, s in samples if t0 <= t <= t1]
if len(window) >= 2:
for i in range(1, len(window)):
dt = window[i][0] - window[i - 1][0]
avg_power = (window[i][1] + window[i - 1][1]) / 2
total_joules += avg_power * dt
elif len(window) == 1:
total_joules += window[0][1] * (t1 - t0)
return total_joules
def summarize(self) -> dict[str, dict[str, dict[str, float]]]:
result: dict[str, dict[str, dict[str, float]]] = {}
for nid, samples in self._samples.items():
if not samples:
continue
metrics: dict[str, dict[str, float]] = {}
for key in _SAMPLER_METRICS:
values = [s[key] for t, s in samples]
metrics[key] = {
"min": round(min(values), 2),
"max": round(max(values), 2),
"mean": round(mean(values), 2),
"samples": len(values),
}
result[nid] = metrics
return result
def print_summary(self, placement_label: str) -> None:
summary = self.summarize()
if not summary:
return
logger.info(f"--- System Metrics ({placement_label}) ---")
for nid, metrics in summary.items():
gpu = metrics.get("gpuUsage", {})
temp = metrics.get("temp", {})
power = metrics.get("sysPower", {})
logger.info(
f" {nid}: "
f"GPU {gpu.get('mean', 0) * 100:.0f}% avg ({gpu.get('min', 0) * 100:.0f}{gpu.get('max', 0) * 100:.0f}%) | "
f"{temp.get('mean', 0):.1f}°C avg | "
f"{power.get('mean', 0):.1f}W avg"
)
def parse_int_list(values: list[str]) -> list[int]:
items: list[int] = []
for v in values:
@@ -142,27 +271,79 @@ def parse_int_list(values: list[str]) -> list[int]:
def run_one_completion(
client: ExoClient, model_id: str, pp_hint: int, tg: int, prompt_sizer: PromptSizer
client: ExoClient,
model_id: str,
pp_hint: int,
tg: int,
prompt_sizer: PromptSizer,
*,
use_prefix_cache: bool = False,
stream: bool = False,
) -> tuple[dict[str, Any], int]:
content, pp_tokens = prompt_sizer.build(pp_hint)
payload: dict[str, Any] = {
"model": model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"max_tokens": tg,
"logprobs": False,
"use_prefix_cache": use_prefix_cache,
}
t0 = time.perf_counter()
out = client.post_bench_chat_completions(payload)
elapsed = time.perf_counter() - t0
if not stream:
payload["stream"] = False
t0 = time.perf_counter()
out = client.post_bench_chat_completions(payload)
elapsed = time.perf_counter() - t0
stats = out.get("generation_stats")
stats = out.get("generation_stats")
choices = out.get("choices") or [{}]
message = choices[0].get("message", {}) if choices else {}
content = message.get("content") or ""
preview = content[:200] if content else ""
else:
tokens = 0
first_token_time = None
t0 = time.perf_counter()
text_parts: list[str] = []
stats = None
# Extract preview, handling None content (common for thinking models)
choices = out.get("choices") or [{}]
message = choices[0].get("message", {}) if choices else {}
content = message.get("content") or ""
preview = content[:200] if content else ""
for raw_line in client.stream_bench_chat_completions(payload):
line = raw_line.strip()
if line.startswith(": generation_stats "):
with contextlib.suppress(json.JSONDecodeError):
stats = json.loads(line[len(": generation_stats ") :])
continue
if not line.startswith("data: "):
continue
data = line[6:]
if data == "[DONE]":
break
try:
chunk = json.loads(data)
delta = chunk.get("choices", [{}])[0].get("delta", {})
if delta.get("content"):
if first_token_time is None:
first_token_time = time.perf_counter()
tokens += 1
text_parts.append(delta["content"])
except json.JSONDecodeError:
pass
elapsed = time.perf_counter() - t0
preview = "".join(text_parts)[:200]
if not stats:
ttft = (first_token_time - t0) if first_token_time else elapsed
gen_time = elapsed - ttft if tokens > 1 else elapsed
gen_tps = (tokens - 1) / gen_time if tokens > 1 and gen_time > 0 else 0.0
prompt_tps = pp_tokens / ttft if ttft > 0 else 0.0
stats = {
"prompt_tokens": pp_tokens,
"generation_tokens": tokens,
"prompt_tps": round(prompt_tps, 2),
"generation_tps": round(gen_tps, 2),
"peak_memory_usage": {"inBytes": 0},
}
return {
"elapsed_s": elapsed,
@@ -182,9 +363,19 @@ class PromptSizer:
def _make_counter(tokenizer: Any) -> Callable[[str], int]:
def count_fn(user_content: str) -> int:
messages = [{"role": "user", "content": user_content}]
ids = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True
)
try:
ids = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True
)
except ValueError:
# Models without a Jinja chat template (e.g. DeepSeek V4 which
# ships its own Python encoder). Use the exo-side V4 encoder.
from exo.worker.engines.mlx.deepseek_v4_encoding import (
encode_messages as encode_v4,
)
prompt = encode_v4(messages, thinking_mode="thinking")
ids = tokenizer.encode(prompt, add_special_tokens=False)
# Fix for transformers 5.x
if hasattr(ids, "input_ids"):
ids = ids.input_ids
@@ -279,6 +470,27 @@ def main() -> int:
action="store_true",
help="Force all pp×tg combinations (cartesian product) even when lists have equal length.",
)
ap.add_argument(
"--stream",
action="store_true",
help="Use /bench/chat/completions with streaming SSE response (bench=True still applies: no EOS detection, no KV cache).",
)
ap.add_argument(
"--no-system-metrics",
action="store_true",
help="Disable GPU utilization, temperature, and power collection during inference.",
)
ap.add_argument(
"--metrics-interval",
type=float,
default=1.0,
help="System metrics polling interval in seconds (default: 1.0).",
)
ap.add_argument(
"--use-prefix-cache",
action="store_true",
help="Enable KV prefix cache during bench (default: disabled for cold-cache measurements).",
)
args = ap.parse_args()
pp_list = parse_int_list(args.pp)
@@ -294,6 +506,15 @@ def main() -> int:
logger.error("--concurrency values must be >= 1")
return 2
if args.use_prefix_cache:
logger.warning(
"--use-prefix-cache: prompt TPS will be approximate. See METHODOLOGY.md for details."
)
if pp_list != sorted(pp_list):
logger.warning(
"--pp values are not in ascending order: prompt TPS will be less accurate. Use ascending --pp for best results."
)
# Log pairing mode
use_combinations = args.all_combinations or len(pp_list) != len(tg_list)
if use_combinations:
@@ -319,82 +540,146 @@ def main() -> int:
logger.error("[exo-bench] tokenizer usable but prompt sizing failed")
raise
selected = settle_and_fetch_placements(
client, full_model_id, args, settle_timeout=args.settle_timeout
)
# Optionally reuse a running instance for this model
reused_instance_id: str | None = None
if args.reuse_instance:
existing = find_existing_instance(client, full_model_id)
if existing:
reused_instance_id = existing
logger.info(f"Reusing existing instance {reused_instance_id}")
else:
logger.warning(
"--reuse-instance: no existing instance found, creating a new one"
)
if not selected:
logger.error("No valid placements matched your filters.")
return 1
selected.sort(
key=lambda p: (
str(p.get("instance_meta", "")),
str(p.get("sharding", "")),
-nodes_used_in_instance(p["instance"]),
),
reverse=True,
)
logger.debug(f"exo-bench model: short_id={short_id} full_id={full_model_id}")
logger.info(f"placements: {len(selected)}")
for p in selected:
logger.info(
f" - {p['sharding']} / {p['instance_meta']} / nodes={nodes_used_in_instance(p['instance'])}"
if reused_instance_id is not None:
# Use the existing instance directly — skip placement iteration
selected = []
download_duration_s = None
else:
selected = settle_and_fetch_placements(
client, full_model_id, args, settle_timeout=args.settle_timeout
)
if args.dry_run:
return 0
if not selected:
logger.error("No valid placements matched your filters.")
return 1
settle_deadline = (
time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
)
selected.sort(
key=lambda p: (
str(p.get("instance_meta", "")),
str(p.get("sharding", "")),
nodes_used_in_instance(p["instance"]),
),
reverse=True,
)
logger.info("Planning phase: checking downloads...")
download_duration_s = run_planning_phase(
client,
full_model_id,
selected[0],
args.danger_delete_downloads,
args.timeout,
settle_deadline,
)
if download_duration_s is not None:
logger.info(f"Download: {download_duration_s:.1f}s (freshly downloaded)")
else:
logger.info("Download: model already cached")
logger.debug(f"exo-bench model: short_id={short_id} full_id={full_model_id}")
logger.info(f"placements: {len(selected)}")
for p in selected:
logger.info(
f" - {p['sharding']} / {p['instance_meta']} / nodes={nodes_used_in_instance(p['instance'])}"
)
if args.dry_run:
return 0
settle_deadline = (
time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
)
logger.info("Planning phase: checking downloads...")
download_duration_s = run_planning_phase(
client,
full_model_id,
selected[0],
args.danger_delete_downloads,
args.timeout,
settle_deadline,
)
if download_duration_s is not None:
logger.info(f"Download: {download_duration_s:.1f}s (freshly downloaded)")
else:
logger.info("Download: model already cached")
cluster_snapshot = capture_cluster_snapshot(client)
all_rows: list[dict[str, Any]] = []
all_system_metrics: dict[str, dict[str, dict[str, float]]] = {}
# If reusing an existing instance, run a single benchmark pass against it
if reused_instance_id is not None:
selected = [None]
for preview in selected:
instance = preview["instance"]
instance_id = instance_id_from_instance(instance)
created_instance = False
if preview is not None:
instance = preview["instance"]
instance_id = instance_id_from_instance(instance)
sharding = str(preview["sharding"])
instance_meta = str(preview["instance_meta"])
n_nodes = nodes_used_in_instance(instance)
sharding = str(preview["sharding"])
instance_meta = str(preview["instance_meta"])
n_nodes = nodes_used_in_instance(instance)
logger.info("=" * 80)
logger.info(
f"PLACEMENT: {sharding} / {instance_meta} / nodes={n_nodes} / instance_id={instance_id}"
)
logger.info("=" * 80)
logger.info(
f"PLACEMENT: {sharding} / {instance_meta} / nodes={n_nodes} / instance_id={instance_id}"
)
client.request_json("POST", "/instance", body={"instance": instance})
try:
wait_for_instance_ready(client, instance_id)
except (RuntimeError, TimeoutError) as e:
logger.error(f"Failed to initialize placement: {e}")
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{instance_id}")
continue
# Delete any existing instances to free resources before placing
try:
state = client.request_json("GET", "/state")
for old_id in list(state.get("instances", {}).keys()):
logger.info(f"Deleting stale instance {old_id}")
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{old_id}")
if state.get("instances"):
time.sleep(2)
except Exception as e:
logger.warning(f"Failed to clean up stale instances: {e}")
time.sleep(1)
client.request_json("POST", "/instance", body={"instance": instance})
try:
wait_for_instance_ready(client, instance_id)
except (RuntimeError, TimeoutError) as e:
logger.error(f"Failed to initialize placement: {e}")
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{instance_id}")
continue
time.sleep(1)
created_instance = True
else:
instance_id = reused_instance_id
sharding = "reused"
instance_meta = "reused"
n_nodes = 0
logger.info("=" * 80)
logger.info(f"Using existing instance {instance_id}")
sampler: SystemMetricsSampler | None = None
if not args.no_system_metrics and preview is not None:
nids = node_ids_from_instance(instance)
sampler = SystemMetricsSampler(
ExoClient(args.host, args.port, timeout_s=30),
nids,
interval_s=args.metrics_interval,
)
sampler.start()
def _do_one(c: ExoClient, pp: int, tg: int) -> tuple[dict[str, Any], int]:
return run_one_completion(
c,
full_model_id,
pp,
tg,
prompt_sizer,
use_prefix_cache=args.use_prefix_cache,
stream=args.stream,
)
try:
for i in range(args.warmup):
run_one_completion(
client, full_model_id, pp_list[0], tg_list[0], prompt_sizer
)
_do_one(client, pp_list[0], tg_list[0])
logger.debug(f" warmup {i + 1}/{args.warmup} done")
# If pp and tg lists have same length, run in tandem (zip)
@@ -408,15 +693,16 @@ def main() -> int:
for concurrency in concurrency_list:
logger.info(f"--- pp={pp} tg={tg} concurrency={concurrency} ---")
runs: list[dict[str, Any]] = []
inference_windows: list[tuple[float, float]] = []
for r in range(args.repeat):
time.sleep(3)
if concurrency <= 1:
# Sequential: single request
try:
row, actual_pp_tokens = run_one_completion(
client, full_model_id, pp, tg, prompt_sizer
)
inf_t0 = time.monotonic()
row, actual_pp_tokens = _do_one(client, pp, tg)
inference_windows.append((inf_t0, time.monotonic()))
except Exception as e:
logger.error(e)
continue
@@ -443,20 +729,54 @@ def main() -> int:
all_rows.append(row)
else:
# Concurrent: fire N requests in parallel
# Each thread gets its own ExoClient (separate HTTP connection)
# Pre-build prompt once, barrier ensures simultaneous dispatch
content, actual_pp = prompt_sizer.build(pp)
pre_built_payload: dict[str, Any] = {
"model": full_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"max_tokens": tg,
"logprobs": False,
"use_prefix_cache": args.use_prefix_cache,
}
barrier = threading.Barrier(concurrency)
batch_start = threading.Event()
batch_t0: float = 0.0
batch_results: list[tuple[dict[str, Any], int]] = []
batch_errors = 0
def _run_concurrent(
idx: int, *, _pp: int = pp, _tg: int = tg
idx: int,
_barrier: threading.Barrier = barrier,
_batch_start: threading.Event = batch_start,
_payload: dict[str, Any] = pre_built_payload,
_actual_pp: int = actual_pp,
) -> tuple[dict[str, Any], int]:
nonlocal batch_t0
c = ExoClient(
args.host, args.port, timeout_s=args.timeout
)
return run_one_completion(
c, full_model_id, _pp, _tg, prompt_sizer
if _barrier.wait() == 0:
batch_t0 = time.perf_counter()
_batch_start.set()
else:
_batch_start.wait()
t0 = batch_t0
out = c.post_bench_chat_completions(_payload)
elapsed = time.perf_counter() - t0
stats = out.get("generation_stats")
choices = out.get("choices") or [{}]
message = (
choices[0].get("message", {}) if choices else {}
)
text = message.get("content") or ""
return {
"elapsed_s": elapsed,
"output_text_preview": text[:200],
"stats": stats,
}, _actual_pp
inf_t0 = time.monotonic()
with ThreadPoolExecutor(max_workers=concurrency) as pool:
futures = {
pool.submit(_run_concurrent, i): i
@@ -468,6 +788,12 @@ def main() -> int:
except Exception as e:
logger.error(f"Concurrent request failed: {e}")
batch_errors += 1
batch_wall_s = (
max(x["elapsed_s"] for x, _ in batch_results)
if batch_results
else time.perf_counter() - batch_t0
)
inference_windows.append((inf_t0, time.monotonic()))
for idx, (row, actual_pp_tokens) in enumerate(
batch_results
@@ -502,48 +828,80 @@ def main() -> int:
if x["stats"]["generation_tps"] > 0
]
per_req_tps = (
mean(valid_gen_tps) if valid_gen_tps else 0.0
max(valid_gen_tps) if valid_gen_tps else 0.0
)
agg_gen_tps = per_req_tps * concurrency
logger.info(
f"[concurrent {concurrency}x] "
f"agg_gen_tps={agg_gen_tps:.2f} "
f"per_req_tps={per_req_tps:.2f} "
f"wall_s={batch_wall_s:.2f} "
f"errors={batch_errors}"
)
if runs:
prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
per_req_tps = mean(x["stats"]["generation_tps"] for x in runs)
valid_gen = [
x["stats"]["generation_tps"]
for x in runs
if x["stats"]["generation_tps"] > 0
]
per_req_tps = max(valid_gen) if valid_gen else 0.0
gen_tps = per_req_tps * concurrency
ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
gtok = mean(x["stats"]["generation_tokens"] for x in runs)
peak = mean(
x["stats"]["peak_memory_usage"]["inBytes"] for x in runs
)
logger.info(
def _peak_bytes(s: dict[str, Any]) -> float:
pm = s["peak_memory_usage"]
return pm.get("inBytes") or pm.get("in_bytes", 0)
peak = mean(_peak_bytes(x["stats"]) for x in runs)
summary = (
f"prompt_tps={prompt_tps:.2f} gen_tps={gen_tps:.2f} "
f"prompt_tokens={ptok} gen_tokens={gtok} "
f"peak_memory={format_peak_memory(peak)}\n"
f"peak_memory={format_peak_memory(peak)}"
)
if sampler and inference_windows:
joules = sum(
sampler.energy_between(t0, t1)
for t0, t1 in inference_windows
)
inf_seconds = sum(t1 - t0 for t0, t1 in inference_windows)
avg_watts = joules / inf_seconds if inf_seconds > 0 else 0
summary += f" energy={joules:.1f}J ({avg_watts:.1f}W avg over {inf_seconds:.1f}s inference)"
logger.info(f"{summary}\n")
time.sleep(2)
finally:
try:
client.request_json("DELETE", f"/instance/{instance_id}")
except ExoHttpError as e:
if e.status != 404:
raise
wait_for_instance_gone(client, instance_id)
logger.debug(f"Deleted instance {instance_id}")
if sampler:
sampler.stop()
placement_label = f"{sharding}/{instance_meta}/{n_nodes} nodes"
sampler.print_summary(placement_label)
placement_metrics = sampler.summarize()
if placement_metrics:
all_system_metrics.update(placement_metrics)
time.sleep(5)
if created_instance and instance_id is not None:
try:
client.request_json("DELETE", f"/instance/{instance_id}")
except ExoHttpError as e:
if e.status != 404:
raise
wait_for_instance_gone(client, instance_id)
logger.debug(f"Deleted instance {instance_id}")
time.sleep(5)
output: dict[str, Any] = {"runs": all_rows}
if cluster_snapshot:
output["cluster"] = cluster_snapshot
if all_system_metrics:
output["system_metrics"] = all_system_metrics
if args.stdout:
json.dump(all_rows, sys.stdout, indent=2, ensure_ascii=False)
json.dump(output, sys.stdout, indent=2, ensure_ascii=False)
elif args.json_out:
with open(args.json_out, "w", encoding="utf-8") as f:
json.dump(all_rows, f, indent=2, ensure_ascii=False)
json.dump(output, f, indent=2, ensure_ascii=False)
logger.debug(f"\nWrote results JSON: {args.json_out}")
return 0
+433 -55
View File
@@ -46,6 +46,8 @@ from harness import (
ExoClient,
ExoHttpError,
add_common_instance_args,
capture_cluster_snapshot,
find_existing_instance,
instance_id_from_instance,
nodes_used_in_instance,
resolve_model_short_id,
@@ -61,6 +63,15 @@ from loguru import logger
# ---------------------------------------------------------------------------
MAX_RETRIES = 30
INSTANCE_HEALTH_CHECK_AFTER = (
3 # Check instance health after this many consecutive failures
)
class InstanceFailedError(RuntimeError):
"""Raised when the exo instance is detected as failed/gone."""
DEFAULT_MAX_TOKENS = 16_384
REASONING_MAX_TOKENS = 131_072
TEMPERATURE_NON_REASONING = 0.0
@@ -270,7 +281,7 @@ def run_humaneval_test(
@dataclass
class QuestionResult:
question_id: int
question_id: int | str
prompt: str
response: str
extracted_answer: str | None
@@ -280,7 +291,11 @@ class QuestionResult:
prompt_tokens: int = 0
completion_tokens: int = 0
reasoning_tokens: int = 0
reasoning_content: str = ""
finish_reason: str = ""
elapsed_s: float = 0.0
power_watts: float = 0.0
energy_joules: float = 0.0
@dataclass
@@ -516,6 +531,10 @@ class ApiResult:
prompt_tokens: int
completion_tokens: int
reasoning_tokens: int
reasoning_content: str = ""
finish_reason: str = ""
power_watts: float = 0.0
energy_joules: float = 0.0
async def _call_api(
@@ -529,6 +548,9 @@ async def _call_api(
system_message: str | None = None,
reasoning_effort: str | None = None,
top_p: float | None = None,
top_k: int | None = None,
min_p: float | None = None,
enable_thinking: bool | None = None,
) -> ApiResult:
messages = []
if system_message:
@@ -545,6 +567,12 @@ async def _call_api(
body["reasoning_effort"] = reasoning_effort
if top_p is not None:
body["top_p"] = top_p
if top_k is not None:
body["top_k"] = top_k
if min_p is not None:
body["min_p"] = min_p
if enable_thinking is not None:
body["enable_thinking"] = enable_thinking
resp = await client.post(
f"{base_url}/v1/chat/completions",
@@ -553,19 +581,40 @@ async def _call_api(
)
resp.raise_for_status()
data = resp.json()
content = data["choices"][0]["message"]["content"]
if not content or not content.strip():
choice = data["choices"][0]
message = choice["message"]
content = message.get("content") or ""
reasoning_content = message.get("reasoning_content") or ""
finish_reason = choice.get("finish_reason") or ""
# For thinking models, empty content is expected when finish_reason is "length"
if not content.strip() and finish_reason != "length" and not reasoning_content:
raise ValueError("Empty response from model")
usage = data.get("usage", {})
details = usage.get("completion_tokens_details", {})
power = data.get("power_usage") or {}
return ApiResult(
content=content,
prompt_tokens=usage.get("prompt_tokens", 0),
completion_tokens=usage.get("completion_tokens", 0),
reasoning_tokens=details.get("reasoning_tokens", 0) if details else 0,
reasoning_content=reasoning_content,
finish_reason=finish_reason,
power_watts=power.get("total_avg_sys_power_watts", 0.0),
energy_joules=power.get("total_energy_joules", 0.0),
)
async def _check_instance_health(base_url: str) -> bool:
"""Return True if the exo instance is still reachable."""
try:
async with httpx.AsyncClient() as c:
resp = await c.get(f"{base_url}/models", timeout=5.0)
return resp.status_code == 200
except Exception:
return False
async def call_with_retries(
client: httpx.AsyncClient,
base_url: str,
@@ -577,8 +626,14 @@ async def call_with_retries(
system_message: str | None = None,
reasoning_effort: str | None = None,
top_p: float | None = None,
top_k: int | None = None,
min_p: float | None = None,
enable_thinking: bool | None = None,
instance_failed: asyncio.Event | None = None,
) -> ApiResult | None:
for attempt in range(MAX_RETRIES):
if instance_failed and instance_failed.is_set():
raise InstanceFailedError("Instance already marked as failed")
try:
return await _call_api(
client,
@@ -591,8 +646,30 @@ async def call_with_retries(
system_message,
reasoning_effort,
top_p,
top_k,
min_p,
enable_thinking,
)
except Exception as e:
is_conn_error = isinstance(
e,
(
httpx.ConnectError,
httpx.RemoteProtocolError,
ConnectionRefusedError,
OSError,
),
)
if (
is_conn_error
and attempt >= INSTANCE_HEALTH_CHECK_AFTER
and not await _check_instance_health(base_url)
):
if instance_failed:
instance_failed.set()
raise InstanceFailedError(
f"Instance is down after {attempt + 1} failures: {e}"
) from e
if attempt < MAX_RETRIES - 1:
wait = min(2**attempt, 60)
logger.warning(
@@ -617,10 +694,16 @@ async def evaluate_benchmark(
max_tokens: int,
concurrency: int = 1,
limit: int | None = None,
offset: int = 0,
timeout: float | None = None,
reasoning_effort: str | None = None,
top_p: float | None = None,
top_k: int | None = None,
min_p: float | None = None,
enable_thinking: bool | None = None,
difficulty: str | None = None,
checkpoint_path: Path | None = None,
release_version: str | None = None,
) -> list[QuestionResult]:
"""Run a benchmark. Returns per-question results."""
import datasets
@@ -651,7 +734,21 @@ async def evaluate_benchmark(
ds = ds.filter(lambda x: x["difficulty"] == difficulty)
logger.info(f"Filtered to {len(ds)} {difficulty} problems")
if release_version and "release_version" in ds.column_names:
ds = ds.filter(lambda x: x["release_version"] == release_version)
logger.info(
f"Filtered to {len(ds)} problems with release_version={release_version}"
)
# Sort by question_id to match LCB runner ordering (scenario_router.py:60).
# This ensures [offset:offset+limit] slices select the same problems as vllm.
if "question_id" in ds.column_names:
ds = ds.sort("question_id")
total = len(ds)
if offset > 0:
ds = ds.select(range(min(offset, total), total))
total = len(ds)
if limit and limit < total:
ds = ds.select(range(limit))
total = limit
@@ -659,6 +756,13 @@ async def evaluate_benchmark(
logger.info(
f"Evaluating {benchmark_name}: {total} questions, concurrency={concurrency}, "
f"temperature={temperature}, max_tokens={max_tokens}"
+ (f", top_k={top_k}" if top_k is not None else "")
+ (f", min_p={min_p}" if min_p is not None else "")
+ (
f", enable_thinking={enable_thinking}"
if enable_thinking is not None
else ""
)
)
if config.kind == "code":
@@ -666,16 +770,64 @@ async def evaluate_benchmark(
"Code benchmarks execute model-generated code. Use a sandboxed environment."
)
# Load checkpoint for resume
checkpoint_data: dict[str | int, dict[str, Any]] = {}
if checkpoint_path and checkpoint_path.exists():
with open(checkpoint_path) as f:
for line in f:
entry = json.loads(line)
checkpoint_data[entry["question_id"]] = entry
logger.info(f"Loaded {len(checkpoint_data)} checkpointed results")
semaphore = asyncio.Semaphore(concurrency)
instance_failed = asyncio.Event()
results: list[QuestionResult | None] = [None] * total
completed = 0
lock = asyncio.Lock()
def _get_question_id(idx: int, doc: dict) -> str | int:
"""Get a stable question ID for checkpointing."""
if benchmark_name == "livecodebench":
return doc.get("question_id", idx)
elif benchmark_name == "humaneval":
return doc.get("task_id", idx)
return idx
async def process_question(
idx: int, doc: dict, http_client: httpx.AsyncClient
) -> None:
nonlocal completed
system_msg = None
question_id = _get_question_id(idx, doc)
# Bail out early if instance is already dead
if instance_failed.is_set():
return
# Check checkpoint
if question_id in checkpoint_data:
cached = checkpoint_data[question_id]
results[idx] = QuestionResult(
question_id=question_id,
prompt=cached.get("prompt", ""),
response=cached.get("response", ""),
extracted_answer=cached.get("extracted_answer"),
gold_answer=cached.get("gold_answer", ""),
correct=cached.get("correct", False),
error=cached.get("error"),
prompt_tokens=cached.get("prompt_tokens", 0),
completion_tokens=cached.get("completion_tokens", 0),
reasoning_tokens=cached.get("reasoning_tokens", 0),
reasoning_content=cached.get("reasoning_content", ""),
finish_reason=cached.get("finish_reason", ""),
elapsed_s=cached.get("elapsed_s", 0.0),
power_watts=cached.get("power_watts", 0.0),
energy_joules=cached.get("energy_joules", 0.0),
)
async with lock:
completed += 1
logger.info(f" [{completed}/{total}] {question_id} (cached)")
return
if benchmark_name == "gpqa_diamond":
prompt, gold = format_gpqa_question(doc, idx)
@@ -696,24 +848,50 @@ async def evaluate_benchmark(
raise ValueError(f"Unknown benchmark: {benchmark_name}")
async with semaphore:
if instance_failed.is_set():
return
t0 = time.monotonic()
api_result = await call_with_retries(
http_client,
base_url,
model,
prompt,
temperature,
max_tokens,
timeout,
system_message=system_msg,
reasoning_effort=reasoning_effort,
top_p=top_p,
)
try:
# Race the API call against the instance_failed event
api_task = asyncio.create_task(
call_with_retries(
http_client,
base_url,
model,
prompt,
temperature,
max_tokens,
timeout,
system_message=system_msg,
reasoning_effort=reasoning_effort,
top_p=top_p,
top_k=top_k,
min_p=min_p,
enable_thinking=enable_thinking,
instance_failed=instance_failed,
)
)
failed_waiter = asyncio.create_task(instance_failed.wait())
done, pending = await asyncio.wait(
[api_task, failed_waiter],
return_when=asyncio.FIRST_COMPLETED,
)
for p in pending:
p.cancel()
with contextlib.suppress(asyncio.CancelledError):
await p
if instance_failed.is_set() and api_task not in done:
logger.error(f"Instance failed, aborting {question_id}")
return
api_result = api_task.result()
except InstanceFailedError:
logger.error(f"Instance failed, skipping {question_id}")
return
elapsed = time.monotonic() - t0
if api_result is None:
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response="",
extracted_answer=None,
@@ -728,13 +906,17 @@ async def evaluate_benchmark(
"prompt_tokens": api_result.prompt_tokens,
"completion_tokens": api_result.completion_tokens,
"reasoning_tokens": api_result.reasoning_tokens,
"reasoning_content": api_result.reasoning_content,
"finish_reason": api_result.finish_reason,
"elapsed_s": elapsed,
"power_watts": api_result.power_watts,
"energy_joules": api_result.energy_joules,
}
if config.kind == "mc":
extracted = extract_mc_answer(response, valid_letters)
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer=extracted,
@@ -748,7 +930,7 @@ async def evaluate_benchmark(
check_aime_answer(extracted, int(gold)) if extracted else False
)
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer=extracted,
@@ -762,7 +944,7 @@ async def evaluate_benchmark(
code = extract_code_block(response, preserve_indent=keep_indent)
if code is None:
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer=None,
@@ -777,7 +959,7 @@ async def evaluate_benchmark(
code,
)
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer="pass" if passed else "fail",
@@ -792,7 +974,7 @@ async def evaluate_benchmark(
exec_meta["sample"],
)
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer="pass" if passed else "fail",
@@ -803,7 +985,7 @@ async def evaluate_benchmark(
)
else:
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer=None,
@@ -814,7 +996,7 @@ async def evaluate_benchmark(
)
else:
result = QuestionResult(
question_id=idx,
question_id=question_id,
prompt=prompt,
response=response,
extracted_answer=None,
@@ -826,24 +1008,82 @@ async def evaluate_benchmark(
results[idx] = result
# Write checkpoint (skip infra failures so they get retried on resume,
# but keep wrong answers — they are legitimate results)
if checkpoint_path is not None and result.response:
_write_checkpoint(checkpoint_path, result)
async with lock:
completed += 1
n = completed
if n % max(1, total // 20) == 0 or n == total:
correct_so_far = sum(1 for r in results if r is not None and r.correct)
answered = sum(1 for r in results if r is not None)
logger.info(
f" [{n}/{total}] {correct_so_far}/{answered} correct "
f"({correct_so_far / max(answered, 1):.1%})"
)
# Log progress
thinking_info = ""
if result.reasoning_content:
thinking_info = f", {len(result.reasoning_content)} chars thinking"
logger.info(
f" [{n}/{total}] {question_id}: {len(result.response)} chars{thinking_info}, "
f"tokens: {result.prompt_tokens}+{result.completion_tokens} "
f"[{result.finish_reason}]"
+ (f" {result.extracted_answer}" if result.extracted_answer else "")
)
async def _health_monitor() -> None:
"""Periodically check if the instance is still alive."""
# Wait a bit before first check to let things start
await asyncio.sleep(10)
while not instance_failed.is_set():
if not await _check_instance_health(base_url):
# Double-check to avoid false positives
await asyncio.sleep(2)
if not await _check_instance_health(base_url):
logger.error("Health monitor: instance is down!")
instance_failed.set()
return
await asyncio.sleep(5)
async with httpx.AsyncClient() as http_client:
monitor = asyncio.create_task(_health_monitor())
tasks = [process_question(i, doc, http_client) for i, doc in enumerate(ds)]
await asyncio.gather(*tasks)
monitor.cancel()
with contextlib.suppress(asyncio.CancelledError):
await monitor
if instance_failed.is_set():
completed_count = sum(1 for r in results if r is not None)
logger.error(
f"Instance failed! Completed {completed_count}/{total} problems. "
f"Checkpoint saved — restart to resume remaining problems."
)
raise InstanceFailedError("Instance failed during evaluation")
return [r for r in results if r is not None]
def _write_checkpoint(path: Path, result: QuestionResult) -> None:
"""Append a single result to the JSONL checkpoint file."""
entry = {
"question_id": result.question_id,
"prompt": result.prompt,
"response": result.response,
"extracted_answer": result.extracted_answer,
"gold_answer": result.gold_answer,
"correct": result.correct,
"error": result.error,
"prompt_tokens": result.prompt_tokens,
"completion_tokens": result.completion_tokens,
"reasoning_tokens": result.reasoning_tokens,
"reasoning_content": result.reasoning_content,
"finish_reason": result.finish_reason,
"elapsed_s": round(result.elapsed_s, 2),
"power_watts": round(result.power_watts, 2),
"energy_joules": round(result.energy_joules, 2),
}
with open(path, "a") as f:
f.write(json.dumps(entry) + "\n")
# ---------------------------------------------------------------------------
# Results display
# ---------------------------------------------------------------------------
@@ -866,6 +1106,8 @@ def print_results(
total_elapsed = sum(r.elapsed_s for r in results)
wall_clock = max(r.elapsed_s for r in results) if results else 0.0
avg_gen_tps = total_completion_tokens / total_elapsed if total_elapsed > 0 else 0.0
total_energy = sum(r.energy_joules for r in results)
avg_power = sum(r.power_watts for r in results) / max(total, 1)
label = f"[c={concurrency}] " if concurrency is not None else ""
print(f"\n{label}{benchmark_name}: {correct}/{total} ({accuracy:.1%})")
@@ -877,6 +1119,10 @@ def print_results(
f" | total time: {total_elapsed:.1f}s wall clock: {wall_clock:.1f}s"
)
print(tok_line)
if total_energy > 0:
print(
f" power: avg {avg_power:.1f}W | total energy: {total_energy:.1f}J ({total_energy / 3600:.2f}Wh)"
)
if errors:
print(f" API errors: {errors}")
if no_extract:
@@ -895,6 +1141,8 @@ def print_results(
"total_elapsed_s": total_elapsed,
"wall_clock_s": wall_clock,
"avg_gen_tps": avg_gen_tps,
"avg_power_watts": avg_power,
"total_energy_joules": total_energy,
}
@@ -1027,16 +1275,18 @@ def save_results(
concurrency: int,
results: list[QuestionResult],
scores: dict[str, Any],
cluster: dict[str, Any] | None = None,
) -> Path:
out_dir = Path(results_dir) / model.replace("/", "_") / benchmark_name
out_dir.mkdir(parents=True, exist_ok=True)
ts = time.strftime("%Y%m%d_%H%M%S")
path = out_dir / f"c{concurrency}_{ts}.json"
data = {
data: dict[str, Any] = {
"benchmark": benchmark_name,
"model": model,
"concurrency": concurrency,
**({"cluster": cluster} if cluster else {}),
"scores": scores,
"results": [
{
@@ -1050,7 +1300,11 @@ def save_results(
"prompt_tokens": r.prompt_tokens,
"completion_tokens": r.completion_tokens,
"reasoning_tokens": r.reasoning_tokens,
"reasoning_content": r.reasoning_content,
"finish_reason": r.finish_reason,
"elapsed_s": round(r.elapsed_s, 2),
"power_watts": round(r.power_watts, 2),
"energy_joules": round(r.energy_joules, 2),
}
for r in results
],
@@ -1066,6 +1320,15 @@ def save_results(
# ---------------------------------------------------------------------------
def _checkpoint_path(
results_dir: str, benchmark: str, model: str, concurrency: int
) -> Path:
"""Return the JSONL checkpoint path for a benchmark run."""
out_dir = Path(results_dir) / model.replace("/", "_") / benchmark
out_dir.mkdir(parents=True, exist_ok=True)
return out_dir / f"c{concurrency}.checkpoint.jsonl"
def parse_int_list(values: list[str]) -> list[int]:
items: list[int] = []
for v in values:
@@ -1093,6 +1356,12 @@ def main() -> int:
default=None,
help="Max questions per benchmark (for fast iteration).",
)
ap.add_argument(
"--offset",
type=int,
default=0,
help="Skip first N questions (0-based).",
)
reasoning_group = ap.add_mutually_exclusive_group()
reasoning_group.add_argument(
@@ -1112,6 +1381,8 @@ def main() -> int:
"--temperature", type=float, default=None, help="Override temperature."
)
ap.add_argument("--top-p", type=float, default=None, help="Override top_p.")
ap.add_argument("--top-k", type=int, default=None, help="Override top_k.")
ap.add_argument("--min-p", type=float, default=None, help="Override min_p.")
ap.add_argument(
"--max-tokens", type=int, default=None, help="Override max output tokens."
)
@@ -1145,15 +1416,31 @@ def main() -> int:
choices=["easy", "medium", "hard"],
help="Filter by difficulty (livecodebench only). E.g. --difficulty hard",
)
ap.add_argument(
"--release-version",
default=None,
help="LCB dataset release version (livecodebench only). E.g. release_v5",
)
ap.add_argument(
"--results-dir",
default="eval_results",
help="Directory for result JSON files (default: eval_results).",
)
ap.add_argument(
"--skip-instance-setup",
"--enable-thinking",
type=lambda v: v.lower() in ("true", "1", "yes"),
default=None,
help="Enable thinking mode for models that support it.",
)
ap.add_argument(
"--force",
action="store_true",
help="Skip exo instance management (assumes model is already running).",
help="Discard any existing checkpoint and run from scratch.",
)
ap.add_argument(
"--keep-instance",
action="store_true",
help="Skip deleting the instance after eval (for chaining runs).",
)
args, _ = ap.parse_known_args()
@@ -1174,13 +1461,26 @@ def main() -> int:
# Instance management
client = ExoClient(args.host, args.port, timeout_s=args.timeout)
instance_id: str | None = None
created_instance = False
if not args.skip_instance_setup:
short_id, full_model_id = resolve_model_short_id(
client,
args.model,
force_download=args.force_download,
)
_short_id, full_model_id = resolve_model_short_id(
client,
args.model,
force_download=args.force_download,
)
# Optionally reuse a running instance for this model
if args.reuse_instance:
existing = find_existing_instance(client, full_model_id)
if existing:
instance_id = existing
logger.info(f"Reusing existing instance {instance_id}")
else:
logger.warning(
"--reuse-instance: no existing instance found, creating a new one"
)
if instance_id is None:
selected = settle_and_fetch_placements(
client,
full_model_id,
@@ -1195,7 +1495,7 @@ def main() -> int:
key=lambda p: (
str(p.get("instance_meta", "")),
str(p.get("sharding", "")),
-nodes_used_in_instance(p["instance"]),
nodes_used_in_instance(p["instance"]),
),
reverse=True,
)
@@ -1222,6 +1522,18 @@ def main() -> int:
if download_duration is not None:
logger.info(f"Download: {download_duration:.1f}s")
# Delete any existing instances to free resources before placing
try:
state = client.request_json("GET", "/state")
for old_id in list(state.get("instances", {}).keys()):
logger.info(f"Deleting stale instance {old_id}")
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{old_id}")
if state.get("instances"):
time.sleep(2)
except Exception as e:
logger.warning(f"Failed to clean up stale instances: {e}")
client.request_json("POST", "/instance", body={"instance": instance})
try:
wait_for_instance_ready(client, instance_id)
@@ -1231,8 +1543,9 @@ def main() -> int:
client.request_json("DELETE", f"/instance/{instance_id}")
return 1
time.sleep(1)
else:
full_model_id = args.model
created_instance = True
cluster_snapshot = capture_cluster_snapshot(client)
# Auto-detect reasoning from model config
model_config = load_model_config(full_model_id)
@@ -1286,16 +1599,57 @@ def main() -> int:
reasoning_effort = str(cfg["reasoning_effort"])
else:
reasoning_effort = "high" if is_reasoning else None
if args.top_k is not None:
top_k: int | None = args.top_k
elif "top_k" in cfg:
top_k = int(cfg["top_k"])
else:
top_k = None
if args.min_p is not None:
min_p: float | None = args.min_p
elif "min_p" in cfg:
min_p = float(cfg["min_p"])
else:
min_p = None
if args.enable_thinking is not None:
enable_thinking: bool | None = args.enable_thinking
elif "enable_thinking" in cfg:
enable_thinking = bool(cfg["enable_thinking"])
else:
enable_thinking = None
base_url = f"http://{args.host}:{args.port}"
logger.info(f"Model: {full_model_id}")
logger.info(
f"Settings: temperature={temperature}, max_tokens={max_tokens}, "
+ (f"top_p={top_p}, " if top_p is not None else "")
+ (f"top_k={top_k}, " if top_k is not None else "")
+ (f"min_p={min_p}, " if min_p is not None else "")
+ f"reasoning={'yes' if is_reasoning else 'no'}"
+ (f", reasoning_effort={reasoning_effort}" if reasoning_effort else "")
+ (
f", enable_thinking={enable_thinking}"
if enable_thinking is not None
else ""
)
)
# Common kwargs for evaluate_benchmark
eval_kwargs: dict[str, Any] = {
"reasoning_effort": reasoning_effort,
"top_p": top_p,
"top_k": top_k,
"min_p": min_p,
"enable_thinking": enable_thinking,
"difficulty": args.difficulty,
"offset": args.offset,
"release_version": args.release_version,
}
try:
if args.compare_concurrency:
concurrency_levels = parse_int_list(args.compare_concurrency)
@@ -1304,6 +1658,11 @@ def main() -> int:
for c in concurrency_levels:
logger.info(f"\n{'=' * 50}")
logger.info(f"Running {task_name} at concurrency={c}")
checkpoint_path = _checkpoint_path(
args.results_dir, task_name, full_model_id, c
)
if args.force and checkpoint_path.exists():
checkpoint_path.unlink()
results = asyncio.run(
evaluate_benchmark(
task_name,
@@ -1314,9 +1673,8 @@ def main() -> int:
concurrency=c,
limit=args.limit,
timeout=args.request_timeout,
reasoning_effort=reasoning_effort,
top_p=top_p,
difficulty=args.difficulty,
checkpoint_path=checkpoint_path,
**eval_kwargs,
)
)
if results:
@@ -1328,12 +1686,21 @@ def main() -> int:
c,
results,
scores,
cluster=cluster_snapshot,
)
results_by_c[c] = results
# Clean up checkpoint on success
if checkpoint_path.exists():
checkpoint_path.unlink()
if len(results_by_c) >= 2:
print_comparison(task_name, results_by_c)
else:
for task_name in task_names:
checkpoint_path = _checkpoint_path(
args.results_dir, task_name, full_model_id, args.num_concurrent
)
if args.force and checkpoint_path.exists():
checkpoint_path.unlink()
results = asyncio.run(
evaluate_benchmark(
task_name,
@@ -1344,9 +1711,8 @@ def main() -> int:
concurrency=args.num_concurrent,
limit=args.limit,
timeout=args.request_timeout,
reasoning_effort=reasoning_effort,
top_p=top_p,
difficulty=args.difficulty,
checkpoint_path=checkpoint_path,
**eval_kwargs,
)
)
if results:
@@ -1358,15 +1724,27 @@ def main() -> int:
args.num_concurrent,
results,
scores,
cluster=cluster_snapshot,
)
# Clean up checkpoint on success
if checkpoint_path.exists():
checkpoint_path.unlink()
finally:
if instance_id is not None:
try:
client.request_json("DELETE", f"/instance/{instance_id}")
except ExoHttpError as e:
if e.status != 404:
raise
wait_for_instance_gone(client, instance_id)
if created_instance and instance_id is not None:
if args.keep_instance:
logger.info(f"Keeping instance {instance_id} (--keep-instance)")
else:
try:
client.request_json("DELETE", f"/instance/{instance_id}")
except ExoHttpError as e:
if e.status != 404:
raise
try:
wait_for_instance_gone(client, instance_id)
except TimeoutError:
logger.warning(
f"Timed out waiting for instance {instance_id} to be deleted"
)
return 0
+158 -39
View File
@@ -6,6 +6,7 @@ import http.client
import json
import os
import time
from collections.abc import Iterator
from typing import Any
from urllib.parse import urlencode
@@ -69,6 +70,59 @@ class ExoClient:
def post_bench_chat_completions(self, payload: dict[str, Any]) -> dict[str, Any]:
return self.request_json("POST", "/bench/chat/completions", body=payload)
def stream_bench_chat_completions(self, payload: dict[str, Any]) -> Iterator[str]:
"""POST /bench/chat/completions with stream=True, yielding raw SSE lines."""
payload = {**payload, "stream": True}
data = json.dumps(payload).encode("utf-8")
conn = http.client.HTTPConnection(self.host, self.port, timeout=self.timeout_s)
try:
conn.request(
"POST",
"/bench/chat/completions",
body=data,
headers={
"Content-Type": "application/json",
"Accept": "text/event-stream",
},
)
resp = conn.getresponse()
if resp.status >= 400:
raw = resp.read().decode("utf-8", errors="replace")
raise ExoHttpError(resp.status, resp.reason, raw[:300])
for line in resp:
yield line.decode("utf-8", errors="replace")
finally:
conn.close()
def get_state_path(self, path: str) -> Any:
try:
return self.request_json("GET", f"/state/{path}")
except ExoHttpError as e:
if e.status == 404:
return None
raise
def get_instance(self, instance_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"instances/{instance_id}")
def get_runner(self, runner_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"runners/{runner_id}")
def get_node_downloads(self, node_id: str) -> list[dict[str, Any]] | None:
return self.get_state_path(f"downloads/{node_id}")
def get_node_disk(self, node_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"nodeDisk/{node_id}")
def get_node_system(self, node_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"nodeSystem/{node_id}")
def get_node_identities(self) -> dict[str, Any] | None:
return self.get_state_path("nodeIdentities")
def get_topology(self) -> dict[str, Any] | None:
return self.get_state_path("topology")
def unwrap_instance(instance: dict[str, Any]) -> dict[str, Any]:
if len(instance) != 1:
@@ -97,6 +151,11 @@ def runner_ids_from_instance(instance: dict[str, Any]) -> list[str]:
return list(runner_to_shard.keys())
def node_ids_from_instance(instance: dict[str, Any]) -> list[str]:
inner = unwrap_instance(instance)
return list(inner["shardAssignments"]["nodeToRunner"].keys())
def runner_ready(runner: dict[str, Any]) -> bool:
return "RunnerReady" in runner
@@ -116,13 +175,12 @@ def wait_for_instance_ready(
) -> None:
start_time = time.time()
instance_existed = False
last_loaded: dict[str, int] = {}
while time.time() - start_time < timeout:
state = client.request_json("GET", "/state")
instances = state.get("instances", {})
instance = client.get_instance(instance_id)
if instance_id not in instances:
if instance is None:
if instance_existed:
# Instance was deleted after being created - likely due to runner failure
raise RuntimeError(
f"Instance {instance_id} was deleted (runner may have failed)"
)
@@ -130,18 +188,25 @@ def wait_for_instance_ready(
continue
instance_existed = True
instance = instances[instance_id]
runner_ids = runner_ids_from_instance(instance)
runners = state.get("runners", {})
rids = runner_ids_from_instance(instance)
# Check for failed runners first
for rid in runner_ids:
runner = runners.get(rid, {})
all_ready = True
for rid in rids:
runner = client.get_runner(rid) or {}
if runner_failed(runner):
error_msg = get_runner_failed_message(runner) or "Unknown error"
raise RuntimeError(f"Runner {rid} failed: {error_msg}")
if "RunnerLoading" in runner:
loading = runner["RunnerLoading"]
loaded = loading.get("layersLoaded", 0)
total = loading.get("totalLayers", 0)
if total > 0 and last_loaded.get(rid) != loaded:
last_loaded[rid] = loaded
logger.debug(f"Runner {rid}: loading layers {loaded}/{total}")
if not runner_ready(runner):
all_ready = False
if all(runner_ready(runners.get(rid, {})) for rid in runner_ids):
if all_ready:
return
time.sleep(0.1)
@@ -165,6 +230,23 @@ def wait_for_instance_gone(
raise TimeoutError(f"Instance {instance_id} did not get deleted within {timeout=}")
def capture_cluster_snapshot(client: ExoClient) -> dict[str, Any]:
snapshot: dict[str, Any] = {}
identities = client.get_node_identities()
if identities:
snapshot["nodeIdentities"] = identities
topology = client.get_topology()
if topology:
snapshot["topology"] = topology
node_memory = client.get_state_path("nodeMemory")
if node_memory:
snapshot["nodeMemory"] = node_memory
node_system = client.get_state_path("nodeSystem")
if node_system:
snapshot["nodeSystem"] = node_system
return snapshot
def resolve_model_short_id(
client: ExoClient, model_arg: str, *, force_download: bool = False
) -> tuple[str, str]:
@@ -211,11 +293,15 @@ def sharding_filter(sharding: str, wanted: str) -> bool:
def fetch_and_filter_placements(
client: ExoClient, full_model_id: str, args: argparse.Namespace
client: ExoClient,
full_model_id: str,
args: argparse.Namespace,
node_id: str | None = None,
) -> list[dict[str, Any]]:
previews_resp = client.request_json(
"GET", "/instance/previews", params={"model_id": full_model_id}
)
params: dict[str, str] = {"model_id": full_model_id}
if node_id is not None:
params["node_ids"] = node_id
previews_resp = client.request_json("GET", "/instance/previews", params=params)
previews = previews_resp.get("previews") or []
selected: list[dict[str, Any]] = []
@@ -275,8 +361,9 @@ def settle_and_fetch_placements(
full_model_id: str,
args: argparse.Namespace,
settle_timeout: float = 0,
node_id: str | None = None,
) -> list[dict[str, Any]]:
selected = fetch_and_filter_placements(client, full_model_id, args)
selected = fetch_and_filter_placements(client, full_model_id, args, node_id=node_id)
if not selected and settle_timeout > 0:
backoff = _SETTLE_INITIAL_BACKOFF_S
@@ -289,7 +376,9 @@ def settle_and_fetch_placements(
)
time.sleep(min(backoff, remaining))
backoff = min(backoff * _SETTLE_BACKOFF_MULTIPLIER, _SETTLE_MAX_BACKOFF_S)
selected = fetch_and_filter_placements(client, full_model_id, args)
selected = fetch_and_filter_placements(
client, full_model_id, args, node_id=node_id
)
return selected
@@ -326,16 +415,11 @@ def run_planning_phase(
node_ids = list(inner["shardAssignments"]["nodeToRunner"].keys())
runner_to_shard = inner["shardAssignments"]["runnerToShard"]
state = client.request_json("GET", "/state")
downloads = state.get("downloads", {})
node_disk = state.get("nodeDisk", {})
needs_download = False
for node_id in node_ids:
node_downloads = downloads.get(node_id, [])
node_downloads = client.get_node_downloads(node_id) or []
# Check if model already downloaded on this node
already_downloaded = any(
"DownloadCompleted" in p
and unwrap_instance(p["DownloadCompleted"]["shardMetadata"])["modelCard"][
@@ -349,8 +433,7 @@ def run_planning_phase(
needs_download = True
# Wait for disk info if settle_deadline is set
disk_info = node_disk.get(node_id, {})
disk_info = client.get_node_disk(node_id) or {}
backoff = _SETTLE_INITIAL_BACKOFF_S
while not disk_info and settle_deadline and time.monotonic() < settle_deadline:
remaining = settle_deadline - time.monotonic()
@@ -359,9 +442,7 @@ def run_planning_phase(
)
time.sleep(min(backoff, remaining))
backoff = min(backoff * _SETTLE_BACKOFF_MULTIPLIER, _SETTLE_MAX_BACKOFF_S)
state = client.request_json("GET", "/state")
node_disk = state.get("nodeDisk", {})
disk_info = node_disk.get(node_id, {})
disk_info = client.get_node_disk(node_id) or {}
if not disk_info:
logger.warning(f"No disk info for {node_id}, skipping space check")
@@ -377,7 +458,6 @@ def run_planning_phase(
f"have {avail // (1024**3)}GB. Use --danger-delete-downloads to free space."
)
# Delete from smallest to largest (skip read-only models from EXO_MODELS_PATH)
completed = [
(
unwrap_instance(p["DownloadCompleted"]["shardMetadata"])["modelCard"][
@@ -414,24 +494,22 @@ def run_planning_phase(
)
logger.info(f"Started download on {node_id}")
# Wait for downloads
start = time.time()
while time.time() - start < timeout:
state = client.request_json("GET", "/state")
downloads = state.get("downloads", {})
# Wait for downloads (no timeout — poll until complete or failed)
while True:
all_done = True
for node_id in node_ids:
node_downloads = client.get_node_downloads(node_id) or []
done = any(
"DownloadCompleted" in p
and unwrap_instance(p["DownloadCompleted"]["shardMetadata"])[
"modelCard"
]["modelId"]
== full_model_id
for p in downloads.get(node_id, [])
for p in node_downloads
)
failed = [
p["DownloadFailed"]["errorMessage"]
for p in downloads.get(node_id, [])
for p in node_downloads
if "DownloadFailed" in p
and unwrap_instance(p["DownloadFailed"]["shardMetadata"])["modelCard"][
"modelId"
@@ -442,13 +520,49 @@ def run_planning_phase(
raise RuntimeError(f"Download failed on {node_id}: {failed[0]}")
if not done:
all_done = False
ongoing = [
p
for p in node_downloads
if "DownloadOngoing" in p
and unwrap_instance(p["DownloadOngoing"]["shardMetadata"])[
"modelCard"
]["modelId"]
== full_model_id
]
if ongoing:
prog = ongoing[0]["DownloadOngoing"]["downloadProgress"]
speed_mb = prog.get("speed", 0) / (1024 * 1024)
eta_s = prog.get("etaMs", 0) / 1000
dl_bytes = prog.get("downloaded", {}).get("inBytes", 0)
total_bytes = prog.get("total", {}).get("inBytes", 0)
pct = (dl_bytes / total_bytes * 100) if total_bytes else 0
logger.info(
f"Downloading on {node_id}: {pct:.1f}% @ {speed_mb:.1f} MB/s, "
f"ETA {eta_s:.0f}s "
f"({prog.get('completedFiles', 0)}/{prog.get('totalFiles', 0)} files)"
)
if all_done:
if download_t0 is not None:
return time.perf_counter() - download_t0
return None
time.sleep(1)
time.sleep(10)
raise TimeoutError("Downloads did not complete in time")
def find_existing_instance(client: ExoClient, model_id: str) -> str | None:
"""Find an existing running instance for the given model."""
try:
state = client.request_json("GET", "/state")
except Exception:
return None
for inst_id, inst in state.get("instances", {}).items():
# Instance structure is nested: {"MlxJacclInstance": {"shardAssignments": {"modelId": ...}}}
for _inst_type, inner in inst.items():
if not isinstance(inner, dict):
continue
sa = inner.get("shardAssignments", {})
if sa.get("modelId") == model_id:
return inst_id
return None
def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
@@ -496,7 +610,7 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
ap.add_argument(
"--settle-timeout",
type=float,
default=0,
default=60.0,
help="Max seconds to wait for the cluster to produce valid placements (0 = try once).",
)
ap.add_argument(
@@ -504,3 +618,8 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
action="store_true",
help="Delete existing models from smallest to largest to make room for benchmark model.",
)
ap.add_argument(
"--reuse-instance",
action="store_true",
help="Reuse an existing running instance for this model instead of creating a new one.",
)
+36
View File
@@ -0,0 +1,36 @@
# Prefill/Decode disaggregation benchmark config.
#
# Top-level keys are bench-wide. [prefill] and [decode] sections set per-side
# placement filters and (optionally) per-side model.
#
# Example:
# uv run python bench/prefill_decode_bench.py --config bench/prefill-decode.toml
host = "james"
port = 52415
timeout = 7200.0
settle_timeout = 60.0
# Workload
pp = [4096]
tg = [512]
repeat = 1
warmup = 0
json_out = "bench/prefill_decode_results.json"
[prefill]
model = "mlx-community/gpt-oss-20b-MXFP4-Q8"
node = "mike"
instance_meta = "ring"
sharding = "pipeline"
min_nodes = 1
max_nodes = 1
[decode]
model = "mlx-community/gpt-oss-20b-MXFP4-Q8"
node = "james"
instance_meta = "ring"
sharding = "pipeline"
min_nodes = 1
max_nodes = 1
+785
View File
@@ -0,0 +1,785 @@
# type: ignore
#!/usr/bin/env python3
"""Disaggregated prefill-decode benchmark for exo (MLX → MLX).
Spins up two MLX instances on the cluster, marks one as Prefill source and
the other as Decode target via /v1/instance-links, then sends chat
completions to the API. The master routes the request to the decode
instance and stamps `prefill_endpoint` pointing at the prefill instance —
the worker decides per-request whether to ship prefill remotely
(uncached_count > REMOTE_PREFILL_MIN_TOKENS).
Usage:
uv run python bench/prefill_decode_bench.py --model <id> --pp 2048,8192 --tg 128
uv run python bench/prefill_decode_bench.py --model <id> --pp 4096 --tg 128 --repeat 3
uv run python bench/prefill_decode_bench.py --model <id> --pp 2048 --tg 128 --dry-run
"""
from __future__ import annotations
import argparse
import contextlib
import copy
import itertools
import json
import sys
import time
import tomllib
from pathlib import Path
from statistics import mean
from typing import Any
from exo_bench import (
PromptSizer,
format_peak_memory,
load_tokenizer_for_bench,
parse_int_list,
)
from harness import (
ExoClient,
ExoHttpError,
add_common_instance_args,
instance_id_from_instance,
node_ids_from_instance,
nodes_used_in_instance,
resolve_model_short_id,
run_planning_phase,
settle_and_fetch_placements,
unwrap_instance,
wait_for_instance_gone,
wait_for_instance_ready,
)
from loguru import logger
def _node_id_to_friendly(client: ExoClient) -> dict[str, str]:
identities = client.get_node_identities() or {}
out: dict[str, str] = {}
for node_id, identity in identities.items():
if isinstance(identity, dict):
name = identity.get("friendlyName") or identity.get("friendly_name")
if isinstance(name, str):
out[str(node_id)] = name
return out
def _placement_node_friendly_names(
placement: dict[str, Any], id_to_friendly: dict[str, str]
) -> list[str]:
instance = placement["instance"]
return [id_to_friendly.get(nid, nid) for nid in node_ids_from_instance(instance)]
def _filter_by_node(
placements: list[dict[str, Any]],
friendly_name: str,
id_to_friendly: dict[str, str],
) -> list[dict[str, Any]]:
target = friendly_name.lower()
matched: list[dict[str, Any]] = []
for p in placements:
names = [n.lower() for n in _placement_node_friendly_names(p, id_to_friendly)]
if any(target == n or target in n for n in names):
matched.append(p)
return matched
def _node_id_by_friendly(id_to_friendly: dict[str, str], target: str) -> str | None:
target_lc = target.lower()
for nid, name in id_to_friendly.items():
if target_lc == name.lower() or target_lc in name.lower():
return nid
return None
def _load_toml(path: str) -> dict[str, Any]:
with Path(path).open("rb") as f:
return tomllib.load(f)
_TOP_LEVEL_TOML_KEYS = {
"host",
"port",
"timeout",
"settle_timeout",
"model",
"pp",
"tg",
"repeat",
"warmup",
"json_out",
"instance_meta",
"sharding",
"min_nodes",
"max_nodes",
"force_download",
"danger_delete_downloads",
"all_combinations",
}
def _inject_toml_into_argv() -> None:
"""If --config X is in sys.argv, pre-load it and inject required CLI args
(--model, --pp, --tg) so argparse's required=True checks pass."""
argv = sys.argv
if "--config" not in argv:
return
idx = argv.index("--config")
if idx + 1 >= len(argv):
return
cfg_path = argv[idx + 1]
cfg = _load_toml(cfg_path)
decode = cfg.get("decode", {})
def _has(flag: str) -> bool:
return any(a == flag or a.startswith(flag + "=") for a in argv)
# --model: prefer top-level, then [decode].model
if not _has("--model"):
model = cfg.get("model") or decode.get("model")
if model:
argv += ["--model", str(model)]
if not _has("--pp"):
pp = cfg.get("pp")
if pp:
argv += (
["--pp", *(str(x) for x in pp)]
if isinstance(pp, list)
else [
"--pp",
str(pp),
]
)
if not _has("--tg"):
tg = cfg.get("tg")
if tg:
argv += (
["--tg", *(str(x) for x in tg)]
if isinstance(tg, list)
else [
"--tg",
str(tg),
]
)
def _merge_toml_into_args(args: argparse.Namespace, cfg: dict[str, Any]) -> None:
"""Apply top-level toml keys onto args namespace where args has a default."""
for key, value in cfg.items():
if key in {"prefill", "decode"}:
continue
if key not in _TOP_LEVEL_TOML_KEYS:
continue
attr = key
current = getattr(args, attr, None)
if current in (None, [], False):
setattr(args, attr, value)
def _side_args(
base: argparse.Namespace, overrides: dict[str, Any]
) -> argparse.Namespace:
out = copy.copy(base)
for k in (
"instance_meta",
"sharding",
"min_nodes",
"max_nodes",
"skip_pipeline_jaccl",
"skip_tensor_ring",
):
if k in overrides:
setattr(out, k, overrides[k])
return out
def _pick_two_distinct_placements(
placements: list[dict[str, Any]],
) -> tuple[dict[str, Any], dict[str, Any]] | None:
if len(placements) < 2:
return None
seen_nodes: set[tuple[str, ...]] = set()
chosen: list[dict[str, Any]] = []
for p in placements:
nodes = tuple(sorted(str(n) for n in p.get("nodes", [])))
if nodes in seen_nodes:
continue
seen_nodes.add(nodes)
chosen.append(p)
if len(chosen) == 2:
return chosen[0], chosen[1]
return None
def _create_instance_link(
client: ExoClient,
prefill_instance_id: str,
decode_instance_id: str,
) -> str:
out = client.request_json(
"POST",
"/v1/instance-links",
body={
"prefill_instances": [prefill_instance_id],
"decode_instances": [decode_instance_id],
},
)
return str(out.get("commandId", ""))
def _list_instance_links(client: ExoClient) -> list[dict[str, Any]]:
out = client.request_json("GET", "/v1/instance-links")
return out if isinstance(out, list) else []
def _delete_instance_link(client: ExoClient, link_id: str) -> None:
client.request_json("DELETE", f"/v1/instance-links/{link_id}")
def run_one(
client: ExoClient,
model_id: str,
pp_hint: int,
tg: int,
prompt_sizer: PromptSizer,
) -> tuple[dict[str, Any], int]:
content, pp_tokens = prompt_sizer.build(pp_hint)
payload: dict[str, Any] = {
"model": model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"max_tokens": tg,
}
t0 = time.perf_counter()
out = client.post_bench_chat_completions(payload)
elapsed = time.perf_counter() - t0
stats = out.get("generation_stats")
choices = out.get("choices") or [{}]
message = choices[0].get("message", {}) if choices else {}
text = message.get("content") or ""
preview = text[:200] if text else ""
return {
"elapsed_s": elapsed,
"output_text_preview": preview,
"stats": stats,
}, pp_tokens
def _run_phase(
*,
client: ExoClient,
label: str,
pp_tg_pairs: list[tuple[int, int]],
model_id: str,
prompt_sizer: PromptSizer,
warmup: int,
repeat: int,
common_meta: dict[str, Any],
) -> list[dict[str, Any]]:
logger.info(f"=== phase: {label} (model={model_id}) ===")
rows: list[dict[str, Any]] = []
for i in range(warmup):
run_one(client, model_id, pp_tg_pairs[0][0], pp_tg_pairs[0][1], prompt_sizer)
logger.debug(f" warmup {i + 1}/{warmup} done")
for pp, tg in pp_tg_pairs:
logger.info(f"--- {label}: pp={pp} tg={tg} ---")
runs: list[dict[str, Any]] = []
for r in range(repeat):
time.sleep(2)
try:
row, actual_pp_tokens = run_one(client, model_id, pp, tg, prompt_sizer)
except Exception as e:
logger.error(e)
continue
row.update(common_meta)
row.update(
{
"phase": label,
"phase_model_id": model_id,
"pp_tokens": actual_pp_tokens,
"tg": tg,
"repeat_index": r,
}
)
runs.append(row)
rows.append(row)
if runs:
prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
gen_tps = mean(x["stats"]["generation_tps"] for x in runs)
ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
gtok = mean(x["stats"]["generation_tokens"] for x in runs)
peak = mean(x["stats"]["peak_memory_usage"]["inBytes"] for x in runs)
avg_elapsed = mean(x["elapsed_s"] for x in runs)
logger.info(
f"[{label}] prompt_tps={prompt_tps:.2f} gen_tps={gen_tps:.2f} "
f"prompt_tokens={ptok} gen_tokens={gtok} "
f"peak_memory={format_peak_memory(peak)} "
f"avg_elapsed={avg_elapsed:.2f}s"
)
time.sleep(2)
return rows
def _summarise(rows: list[dict[str, Any]]) -> dict[tuple[int, int], dict[str, float]]:
grouped: dict[tuple[int, int], list[dict[str, Any]]] = {}
for r in rows:
key = (int(r["pp_tokens"]), int(r["tg"]))
grouped.setdefault(key, []).append(r)
out: dict[tuple[int, int], dict[str, float]] = {}
for key, runs in grouped.items():
out[key] = {
"prompt_tps": mean(x["stats"]["prompt_tps"] for x in runs),
"gen_tps": mean(x["stats"]["generation_tps"] for x in runs),
"elapsed_s": mean(x["elapsed_s"] for x in runs),
}
return out
def _print_diff(
disagg_rows: list[dict[str, Any]],
decode_alone_rows: list[dict[str, Any]],
prefill_alone_rows: list[dict[str, Any]],
) -> None:
disagg = _summarise(disagg_rows)
decode_alone = _summarise(decode_alone_rows)
prefill_alone = _summarise(prefill_alone_rows)
keys = set(disagg.keys()) | set(decode_alone.keys()) | set(prefill_alone.keys())
width = 64
for key in sorted(keys):
pp, tg = key
logger.info("" * width)
logger.info(f" pp={pp} tg={tg}")
logger.info("" * width)
logger.info(
f" {'phase':<16} {'elapsed':>10} {'prompt_tps':>11} {'gen_tps':>9}"
)
for label, summary in (
("disaggregated", disagg.get(key)),
("decode_alone", decode_alone.get(key)),
("prefill_alone", prefill_alone.get(key)),
):
if summary is None:
logger.info(f" {label:<16} {'':>10} {'':>11} {'':>9}")
continue
logger.info(
f" {label:<16} "
f"{summary['elapsed_s']:>9.2f}s "
f"{summary['prompt_tps']:>11.1f} "
f"{summary['gen_tps']:>9.2f}"
)
d = disagg.get(key)
da = decode_alone.get(key)
pa = prefill_alone.get(key)
if d and da and d["elapsed_s"] > 0:
logger.info(
f" speedup vs decode_alone: {da['elapsed_s'] / d['elapsed_s']:.2f}x"
)
if d and pa and d["elapsed_s"] > 0:
logger.info(
f" speedup vs prefill_alone: {pa['elapsed_s'] / d['elapsed_s']:.2f}x"
)
logger.info("" * width)
def main() -> int:
_inject_toml_into_argv()
ap = argparse.ArgumentParser(
prog="prefill-decode-bench",
description="Benchmark MLX-MLX disaggregated prefill/decode via instance links.",
)
add_common_instance_args(ap)
ap.add_argument(
"--pp",
nargs="+",
required=True,
help="Prompt-size hints (ints, must be >1000). Accepts commas.",
)
ap.add_argument(
"--tg",
nargs="+",
required=True,
help="Generation lengths (ints). Accepts commas.",
)
ap.add_argument(
"--repeat", type=int, default=1, help="Repetitions per (pp,tg) pair."
)
ap.add_argument(
"--warmup",
type=int,
default=0,
help="Warmup runs (uses first pp/tg).",
)
ap.add_argument(
"--json-out",
default="bench/prefill_decode_results.json",
help="Write raw per-run results JSON to this path.",
)
ap.add_argument("--stdout", action="store_true", help="Write results to stdout")
ap.add_argument(
"--dry-run", action="store_true", help="List selected placements and exit."
)
ap.add_argument(
"--all-combinations",
action="store_true",
help="Force all pp×tg combinations even when lists have equal length.",
)
ap.add_argument(
"--prefill-model",
default=None,
help="Model id for the prefill instance. Defaults to --model.",
)
ap.add_argument(
"--prefill-node",
default=None,
help="friendly_name of the node hosting the prefill instance.",
)
ap.add_argument(
"--decode-node",
default=None,
help="friendly_name of the node hosting the decode instance.",
)
ap.add_argument(
"--config",
default=None,
help="TOML config file. CLI flags override toml values.",
)
ap.add_argument(
"--compare-baseline",
action="store_true",
help="Also run each (pp,tg) pair without the prefill/decode link "
"(decode instance does its own prefill) and report the diff.",
)
args = ap.parse_args()
cfg = _load_toml(args.config) if args.config else {}
_merge_toml_into_args(args, cfg)
prefill_overrides = cfg.get("prefill", {}) if cfg else {}
decode_overrides = cfg.get("decode", {}) if cfg else {}
if args.prefill_model is None and "model" in prefill_overrides:
args.prefill_model = prefill_overrides["model"]
if args.prefill_node is None and "node" in prefill_overrides:
args.prefill_node = prefill_overrides["node"]
if args.decode_node is None and "node" in decode_overrides:
args.decode_node = decode_overrides["node"]
if "model" in decode_overrides and not args.model:
args.model = decode_overrides["model"]
pp_list = parse_int_list(args.pp)
tg_list = parse_int_list(args.tg)
if not pp_list or not tg_list:
logger.error("pp and tg lists must be non-empty")
return 2
for pp in pp_list:
if pp <= 1000:
logger.error(
f"pp={pp} must be >1000 (remote prefill triggers when uncached >1000)"
)
return 2
if args.repeat <= 0:
logger.error("--repeat must be >= 1")
return 2
use_combinations = args.all_combinations or len(pp_list) != len(tg_list)
if use_combinations:
logger.info(
f"pp/tg mode: combinations (product) — {len(pp_list) * len(tg_list)} pairs"
)
else:
logger.info(f"pp/tg mode: tandem (zip) — {len(pp_list)} pairs")
client = ExoClient(args.host, args.port, timeout_s=args.timeout)
decode_short_id, decode_full_id = resolve_model_short_id(
client, args.model, force_download=args.force_download
)
if args.prefill_model:
prefill_short_id, prefill_full_id = resolve_model_short_id(
client, args.prefill_model, force_download=args.force_download
)
else:
prefill_short_id, prefill_full_id = decode_short_id, decode_full_id
tokenizer = load_tokenizer_for_bench(decode_full_id)
if tokenizer is None:
raise RuntimeError("[prefill-decode-bench] decode tokenizer load failed")
try:
decode_prompt_sizer = PromptSizer(tokenizer)
except Exception:
logger.error("[prefill-decode-bench] decode prompt sizing failed")
raise
if prefill_full_id == decode_full_id:
prefill_prompt_sizer = decode_prompt_sizer
else:
prefill_tokenizer = load_tokenizer_for_bench(prefill_full_id)
if prefill_tokenizer is None:
raise RuntimeError("[prefill-decode-bench] prefill tokenizer load failed")
prefill_prompt_sizer = PromptSizer(prefill_tokenizer)
id_to_friendly = _node_id_to_friendly(client)
prefill_args = _side_args(args, prefill_overrides)
decode_args = _side_args(args, decode_overrides)
if prefill_full_id == decode_full_id and prefill_overrides == decode_overrides:
placements = settle_and_fetch_placements(
client, decode_full_id, args, settle_timeout=args.settle_timeout
)
prefill_candidates = (
_filter_by_node(placements, args.prefill_node, id_to_friendly)
if args.prefill_node
else placements
)
decode_candidates = (
_filter_by_node(placements, args.decode_node, id_to_friendly)
if args.decode_node
else placements
)
if args.prefill_node and not prefill_candidates:
logger.error(f"No placement on prefill node {args.prefill_node!r}.")
return 1
if args.decode_node and not decode_candidates:
logger.error(f"No placement on decode node {args.decode_node!r}.")
return 1
if args.prefill_node and args.decode_node:
prefill_p = prefill_candidates[0]
decode_p = decode_candidates[0]
else:
pair = _pick_two_distinct_placements(placements)
if pair is None:
logger.error(
"Need at least two distinct-node MLX placements for the same model."
)
return 1
prefill_p, decode_p = pair
if args.prefill_node:
prefill_p = prefill_candidates[0]
if args.decode_node:
decode_p = decode_candidates[0]
else:
prefill_node_id = (
_node_id_by_friendly(id_to_friendly, args.prefill_node)
if args.prefill_node
else None
)
decode_node_id = (
_node_id_by_friendly(id_to_friendly, args.decode_node)
if args.decode_node
else None
)
if args.prefill_node and prefill_node_id is None:
logger.error(f"Unknown node {args.prefill_node!r}.")
return 1
if args.decode_node and decode_node_id is None:
logger.error(f"Unknown node {args.decode_node!r}.")
return 1
prefill_placements = settle_and_fetch_placements(
client,
prefill_full_id,
prefill_args,
settle_timeout=args.settle_timeout,
node_id=prefill_node_id,
)
decode_placements = settle_and_fetch_placements(
client,
decode_full_id,
decode_args,
settle_timeout=args.settle_timeout,
node_id=decode_node_id,
)
if not prefill_placements:
logger.error(
f"No placement found for prefill model {prefill_full_id}"
f"{f' on node {args.prefill_node!r}' if args.prefill_node else ''}."
)
return 1
if not decode_placements:
logger.error(
f"No placement found for decode model {decode_full_id}"
f"{f' on node {args.decode_node!r}' if args.decode_node else ''}."
)
return 1
prefill_p = prefill_placements[0]
decode_p = decode_placements[0]
prefill_node_names = _placement_node_friendly_names(prefill_p, id_to_friendly)
decode_node_names = _placement_node_friendly_names(decode_p, id_to_friendly)
_ = unwrap_instance
prefill_instance = prefill_p["instance"]
decode_instance = decode_p["instance"]
prefill_id = instance_id_from_instance(prefill_instance)
decode_id = instance_id_from_instance(decode_instance)
prefill_meta = str(prefill_p.get("instance_meta", ""))
decode_meta = str(decode_p.get("instance_meta", ""))
prefill_nodes = nodes_used_in_instance(prefill_instance)
decode_nodes = nodes_used_in_instance(decode_instance)
logger.info("=" * 80)
logger.info(
f"PREFILL: {prefill_meta} / nodes={prefill_nodes} ({','.join(prefill_node_names)}) "
f"/ {prefill_short_id} ({prefill_full_id}) / instance_id={prefill_id}"
)
logger.info(
f"DECODE: {decode_meta} / nodes={decode_nodes} ({','.join(decode_node_names)}) "
f"/ {decode_short_id} ({decode_full_id}) / instance_id={decode_id}"
)
if args.dry_run:
return 0
settle_deadline = (
time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
)
logger.info("Planning phase: prefill...")
run_planning_phase(
client,
prefill_full_id,
prefill_p,
args.danger_delete_downloads,
args.timeout,
settle_deadline,
)
logger.info("Planning phase: decode...")
run_planning_phase(
client,
decode_full_id,
decode_p,
args.danger_delete_downloads,
args.timeout,
settle_deadline,
)
if use_combinations:
pp_tg_pairs = list(itertools.product(pp_list, tg_list))
else:
pp_tg_pairs = list(zip(pp_list, tg_list, strict=True))
common_meta = {
"decode_model_short_id": decode_short_id,
"decode_model_id": decode_full_id,
"prefill_model_short_id": prefill_short_id,
"prefill_model_id": prefill_full_id,
"prefill_instance_id": prefill_id,
"prefill_instance_meta": prefill_meta,
"prefill_nodes": prefill_nodes,
"decode_instance_id": decode_id,
"decode_instance_meta": decode_meta,
"decode_nodes": decode_nodes,
}
all_rows: list[dict[str, Any]] = []
disagg_rows: list[dict[str, Any]] = []
decode_alone_rows: list[dict[str, Any]] = []
prefill_alone_rows: list[dict[str, Any]] = []
link_id = ""
prefill_alive = False
decode_alive = False
try:
logger.info("Creating prefill instance...")
client.request_json("POST", "/instance", body={"instance": prefill_instance})
wait_for_instance_ready(client, prefill_id)
prefill_alive = True
logger.info("Prefill instance ready")
if args.compare_baseline:
time.sleep(2)
prefill_alone_rows = _run_phase(
client=client,
label="prefill_alone",
pp_tg_pairs=pp_tg_pairs,
model_id=prefill_full_id,
prompt_sizer=prefill_prompt_sizer,
warmup=args.warmup,
repeat=args.repeat,
common_meta=common_meta,
)
all_rows.extend(prefill_alone_rows)
logger.info("Creating decode instance...")
client.request_json("POST", "/instance", body={"instance": decode_instance})
wait_for_instance_ready(client, decode_id)
decode_alive = True
logger.info("Decode instance ready")
logger.info("Linking instances (prefill → decode)...")
_create_instance_link(client, prefill_id, decode_id)
time.sleep(1)
links = _list_instance_links(client)
if not links:
logger.error("Link did not appear in state.")
return 1
link_id = str(links[-1].get("linkId") or links[-1].get("link_id") or "")
logger.info(f"Link created: {link_id}")
time.sleep(2)
disagg_rows = _run_phase(
client=client,
label="disaggregated",
pp_tg_pairs=pp_tg_pairs,
model_id=decode_full_id,
prompt_sizer=decode_prompt_sizer,
warmup=args.warmup,
repeat=args.repeat,
common_meta=common_meta,
)
all_rows.extend(disagg_rows)
if args.compare_baseline:
logger.info("Removing link and prefill instance to isolate decode_alone.")
with contextlib.suppress(ExoHttpError):
if link_id:
_delete_instance_link(client, link_id)
link_id = ""
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{prefill_id}")
wait_for_instance_gone(client, prefill_id)
prefill_alive = False
time.sleep(2)
decode_alone_rows = _run_phase(
client=client,
label="decode_alone",
pp_tg_pairs=pp_tg_pairs,
model_id=decode_full_id,
prompt_sizer=decode_prompt_sizer,
warmup=args.warmup,
repeat=args.repeat,
common_meta=common_meta,
)
all_rows.extend(decode_alone_rows)
_print_diff(disagg_rows, decode_alone_rows, prefill_alone_rows)
finally:
with contextlib.suppress(ExoHttpError):
if link_id:
_delete_instance_link(client, link_id)
if decode_alive:
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{decode_id}")
wait_for_instance_gone(client, decode_id)
if prefill_alive:
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{prefill_id}")
wait_for_instance_gone(client, prefill_id)
logger.debug("Deleted both instances")
if args.stdout:
json.dump(all_rows, sys.stdout, indent=2, ensure_ascii=False)
elif args.json_out:
with open(args.json_out, "w", encoding="utf-8") as f:
json.dump(all_rows, f, indent=2, ensure_ascii=False)
logger.debug(f"\nWrote results JSON: {args.json_out}")
return 0
if __name__ == "__main__":
sys.exit(main())
+272 -1
View File
@@ -11,7 +11,8 @@
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],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-arm64-musl": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-arm64-musl/-/canvas-linux-arm64-musl-0.1.97.tgz",
"integrity": "sha512-kKmSkQVnWeqg7qdsiXvYxKhAFuHz3tkBjW/zyQv5YKUPhotpaVhpBGv5LqCngzyuRV85SXoe+OFj+Tv0a0QXkQ==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-riscv64-gnu": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-riscv64-gnu/-/canvas-linux-riscv64-gnu-0.1.97.tgz",
"integrity": "sha512-Jc7I3A51jnEOIAXeLsN/M/+Z28LUeakcsXs07FLq9prXc0eYOtVwsDEv913Gr+06IRo34gJJVgT0TXvmz+N2VA==",
"cpu": [
"riscv64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-x64-gnu": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-x64-gnu/-/canvas-linux-x64-gnu-0.1.97.tgz",
"integrity": "sha512-iDUBe7AilfuBSRbSa8/IGX38Mf+iCSBqoVKLSQ5XaY2JLOaqz1TVyPFEyIck7wT6mRQhQt5sN6ogfjIDfi74tg==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-x64-musl": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-x64-musl/-/canvas-linux-x64-musl-0.1.97.tgz",
"integrity": "sha512-AKLFd/v0Z5fvgqBDqhvqtAdx+fHMJ5t9JcUNKq4FIZ5WH+iegGm8HPdj00NFlCSnm83Fp3Ln8I2f7uq1aIiWaA==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-win32-arm64-msvc": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-win32-arm64-msvc/-/canvas-win32-arm64-msvc-0.1.97.tgz",
"integrity": "sha512-u883Yr6A6fO7Vpsy9YE4FVCIxzzo5sO+7pIUjjoDLjS3vQaNMkVzx5bdIpEL+ob+gU88WDK4VcxYMZ6nmnoX9A==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"win32"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-win32-x64-msvc": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-win32-x64-msvc/-/canvas-win32-x64-msvc-0.1.97.tgz",
"integrity": "sha512-sWtD2EE3fV0IzN+iiQUqr/Q1SwqWhs2O1FKItFlxtdDkikpEj5g7DKQpY3x55H/MAOnL8iomnlk3mcEeGiUMoQ==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"win32"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@polka/url": {
"version": "1.0.0-next.29",
"resolved": "https://registry.npmjs.org/@polka/url/-/url-1.0.0-next.29.tgz",
@@ -2635,6 +2886,26 @@
"node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1"
}
},
"node_modules/node-readable-to-web-readable-stream": {
"version": "0.4.2",
"resolved": "https://registry.npmjs.org/node-readable-to-web-readable-stream/-/node-readable-to-web-readable-stream-0.4.2.tgz",
"integrity": "sha512-/cMZNI34v//jUTrI+UIo4ieHAB5EZRY/+7OmXZgBxaWBMcW2tGdceIw06RFxWxrKZ5Jp3sI2i5TsRo+CBhtVLQ==",
"license": "MIT",
"optional": true
},
"node_modules/pdfjs-dist": {
"version": "5.6.205",
"resolved": "https://registry.npmjs.org/pdfjs-dist/-/pdfjs-dist-5.6.205.tgz",
"integrity": "sha512-tlUj+2IDa7G1SbvBNN74UHRLJybZDWYom+k6p5KIZl7huBvsA4APi6mKL+zCxd3tLjN5hOOEE9Tv7VdzO88pfg==",
"license": "Apache-2.0",
"engines": {
"node": ">=20.19.0 || >=22.13.0 || >=24"
},
"optionalDependencies": {
"@napi-rs/canvas": "^0.1.96",
"node-readable-to-web-readable-stream": "^0.4.2"
}
},
"node_modules/picocolors": {
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz",
+2 -1
View File
@@ -31,6 +31,7 @@
"highlight.js": "^11.11.1",
"katex": "^0.16.27",
"marked": "^17.0.1",
"mode-watcher": "^1.1.0"
"mode-watcher": "^1.1.0",
"pdfjs-dist": "^5.6.205"
}
}
@@ -139,6 +139,8 @@
return "🖼";
case "text":
return "📄";
case "pdf":
return "📑";
default:
return "📎";
}
@@ -94,6 +94,12 @@
d="M8.948 8.798v-1.43a6.7 6.7 0 0 1 .424-.018c3.922-.124 6.493 3.374 6.493 3.374s-2.774 3.851-5.75 3.851c-.398 0-.787-.062-1.158-.185v-4.346c1.528.185 1.837.857 2.747 2.385l2.04-1.714s-1.492-1.952-4-1.952a6.016 6.016 0 0 0-.796.035m0-4.735v2.138l.424-.027c5.45-.185 9.01 4.47 9.01 4.47s-4.08 4.964-8.33 4.964c-.37 0-.733-.035-1.095-.097v1.325c.3.035.61.062.91.062 3.957 0 6.82-2.023 9.593-4.408.459.371 2.34 1.263 2.73 1.652-2.633 2.208-8.772 3.984-12.253 3.984-.335 0-.653-.018-.971-.053v1.864H24V4.063zm0 10.326v1.131c-3.657-.654-4.673-4.46-4.673-4.46s1.758-1.944 4.673-2.262v1.237H8.94c-1.528-.186-2.73 1.245-2.73 1.245s.68 2.412 2.739 3.11M2.456 10.9s2.164-3.197 6.5-3.533V6.201C4.153 6.59 0 10.653 0 10.653s2.35 6.802 8.948 7.42v-1.237c-4.84-.6-6.492-5.936-6.492-5.936z"
/>
</svg>
{:else if family === "gemma"}
<svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
<path
d="M12.48 10.92v3.28h7.84c-.24 1.84-.853 3.187-1.787 4.133-1.147 1.147-2.933 2.4-6.053 2.4-4.827 0-8.6-3.893-8.6-8.72s3.773-8.72 8.6-8.72c2.6 0 4.507 1.027 5.907 2.347l2.307-2.307C18.747 1.44 16.133 0 12.48 0 5.867 0 .307 5.387.307 12s5.56 12 12.173 12c3.573 0 6.267-1.173 8.373-3.36 2.16-2.16 2.84-5.213 2.84-7.667 0-.76-.053-1.467-.173-2.053H12.48z"
/>
</svg>
{:else}
<svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
<path
@@ -32,6 +32,7 @@
flux: "FLUX",
"qwen-image": "Qwen Img",
nemotron: "NVIDIA",
gemma: "Google",
};
function getFamilyName(family: string): string {
@@ -42,31 +43,20 @@
</script>
<div
class="flex flex-col gap-1 py-2 px-1 border-r border-exo-yellow/10 bg-exo-medium-gray/30 min-w-[72px] sm:min-w-[64px] overflow-y-auto scrollbar-hide"
class="flex flex-col gap-1 py-2 px-1 border-r border-exo-yellow/10 bg-exo-medium-gray/30 min-w-[80px] sm:min-w-[72px] overflow-y-auto scrollbar-hide"
>
<!-- All models (no filter) -->
<button
type="button"
onclick={() => onSelect(null)}
class="group flex flex-col items-center justify-center p-2 sm:p-2 rounded transition-all duration-200 cursor-pointer min-h-[44px] sm:min-h-0 {selectedFamily ===
class="group flex items-center justify-center px-3 py-2.5 rounded transition-all duration-200 cursor-pointer min-h-[44px] sm:min-h-0 {selectedFamily ===
null
? 'bg-exo-yellow/20 border-l-2 border-exo-yellow'
: 'hover:bg-white/5 border-l-2 border-transparent'}"
title="All models"
>
<svg
class="w-5 h-5 {selectedFamily === null
? 'text-exo-yellow'
: 'text-white/50 group-hover:text-white/70'}"
viewBox="0 0 24 24"
fill="currentColor"
>
<path
d="M4 8h4V4H4v4zm6 12h4v-4h-4v4zm-6 0h4v-4H4v4zm0-6h4v-4H4v4zm6 0h4v-4h-4v4zm6-10v4h4V4h-4zm-6 4h4V4h-4v4zm6 6h4v-4h-4v4zm0 6h4v-4h-4v4z"
/>
</svg>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === null
class="text-[12px] font-mono font-medium {selectedFamily === null
? 'text-exo-yellow'
: 'text-white/40 group-hover:text-white/60'}">All</span
>
@@ -90,7 +80,7 @@
: "text-white/50 group-hover:text-amber-400/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === 'favorites'
class="text-[11px] font-mono mt-0.5 {selectedFamily === 'favorites'
? 'text-amber-400'
: 'text-white/40 group-hover:text-white/60'}">Faves</span
>
@@ -115,7 +105,7 @@
: "text-white/50 group-hover:text-white/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === 'recents'
class="text-[11px] font-mono mt-0.5 {selectedFamily === 'recents'
? 'text-exo-yellow'
: 'text-white/40 group-hover:text-white/60'}">Recent</span
>
@@ -139,7 +129,7 @@
: "text-white/50 group-hover:text-orange-400/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === 'huggingface'
class="text-[11px] font-mono mt-0.5 {selectedFamily === 'huggingface'
? 'text-orange-400'
: 'text-white/40 group-hover:text-white/60'}">Hub</span
>
@@ -165,7 +155,7 @@
: "text-white/50 group-hover:text-white/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 truncate max-w-full {selectedFamily ===
class="text-[11px] font-mono mt-0.5 truncate max-w-full {selectedFamily ===
family
? 'text-exo-yellow'
: 'text-white/40 group-hover:text-white/60'}"
+79 -15
View File
@@ -1,21 +1,41 @@
<script lang="ts">
import { browser } from "$app/environment";
import { featureFlags } from "$lib/stores/app.svelte";
export let showHome = true;
export let onHome: (() => void) | null = null;
export let showSidebarToggle = false;
export let sidebarVisible = true;
export let onToggleSidebar: (() => void) | null = null;
export let showMobileMenuToggle = false;
export let mobileMenuOpen = false;
export let onToggleMobileMenu: (() => void) | null = null;
export let showMobileRightToggle = false;
export let mobileRightOpen = false;
export let onToggleMobileRight: (() => void) | null = null;
export let downloadProgress: {
count: number;
percentage: number;
} | null = null;
const showAdvanced = $derived(featureFlags()["disaggregation"] === true);
interface Props {
showHome?: boolean;
onHome?: (() => void) | null;
showSidebarToggle?: boolean;
sidebarVisible?: boolean;
onToggleSidebar?: (() => void) | null;
showMobileMenuToggle?: boolean;
mobileMenuOpen?: boolean;
onToggleMobileMenu?: (() => void) | null;
showMobileRightToggle?: boolean;
mobileRightOpen?: boolean;
onToggleMobileRight?: (() => void) | null;
downloadProgress?: {
count: number;
percentage: number;
} | null;
}
let {
showHome = true,
onHome = null,
showSidebarToggle = false,
sidebarVisible = true,
onToggleSidebar = null,
showMobileMenuToggle = false,
mobileMenuOpen = false,
onToggleMobileMenu = null,
showMobileRightToggle = false,
mobileRightOpen = false,
onToggleMobileRight = null,
downloadProgress = null,
}: Props = $props();
function handleHome(): void {
if (onHome) {
@@ -259,5 +279,49 @@
{/if}
<span class="hidden sm:inline">Downloads</span>
</a>
<a
href="/#/integrations"
class="text-xs md:text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-1.5 md:gap-2 cursor-pointer"
title="Integration configs for external tools"
>
<svg
class="w-4 h-4"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71" />
<path
d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"
/>
</svg>
<span class="hidden sm:inline">Integrations</span>
</a>
{#if showAdvanced}
<a
href="/#/advanced"
class="text-xs md:text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-1.5 md:gap-2 cursor-pointer"
title="Advanced cluster settings"
>
<svg
class="w-4 h-4"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<circle cx="12" cy="12" r="3" />
<path
d="M19.4 15a1.65 1.65 0 0 0 .33 1.82l.06.06a2 2 0 0 1 0 2.83 2 2 0 0 1-2.83 0l-.06-.06a1.65 1.65 0 0 0-1.82-.33 1.65 1.65 0 0 0-1 1.51V21a2 2 0 0 1-4 0v-.09A1.65 1.65 0 0 0 9 19.4a1.65 1.65 0 0 0-1.82.33l-.06.06a2 2 0 0 1-2.83 0 2 2 0 0 1 0-2.83l.06-.06a1.65 1.65 0 0 0 .33-1.82 1.65 1.65 0 0 0-1.51-1H3a2 2 0 0 1 0-4h.09A1.65 1.65 0 0 0 4.6 9a1.65 1.65 0 0 0-.33-1.82l-.06-.06a2 2 0 0 1 0-2.83 2 2 0 0 1 2.83 0l.06.06a1.65 1.65 0 0 0 1.82.33H9a1.65 1.65 0 0 0 1-1.51V3a2 2 0 0 1 4 0v.09a1.65 1.65 0 0 0 1 1.51 1.65 1.65 0 0 0 1.82-.33l.06-.06a2 2 0 0 1 2.83 0 2 2 0 0 1 0 2.83l-.06.06a1.65 1.65 0 0 0-.33 1.82V9a1.65 1.65 0 0 0 1.51 1H21a2 2 0 0 1 0 4h-.09a1.65 1.65 0 0 0-1.51 1z"
/>
</svg>
<span class="hidden sm:inline">Advanced</span>
</a>
{/if}
</nav>
</header>
@@ -0,0 +1,62 @@
<script lang="ts">
import { copyText } from "$lib/utils/clipboard";
interface Props {
title: string;
subtitle: string;
config: string;
description?: string;
language?: "json" | "bash";
}
let {
title,
subtitle,
config,
description = "",
language = "json",
}: Props = $props();
let copied = $state(false);
let failed = $state(false);
async function copyToClipboard() {
const ok = await copyText(config);
if (ok) {
copied = true;
setTimeout(() => (copied = false), 2000);
} else {
failed = true;
setTimeout(() => (failed = false), 2000);
}
}
</script>
<div
class="border border-exo-light-gray/20 rounded-lg bg-exo-medium-gray/20 overflow-hidden"
>
<div class="flex items-center justify-between px-5 py-4">
<div>
<h3 class="text-white text-sm font-semibold tracking-wide">{title}</h3>
<p class="text-exo-light-gray/60 text-xs mt-0.5 font-mono">{subtitle}</p>
</div>
<button
onclick={copyToClipboard}
class="px-3 py-1.5 text-xs rounded border transition-all duration-200 cursor-pointer
{copied
? 'border-green-500/50 text-green-400 bg-green-500/10'
: failed
? 'border-red-500/50 text-red-400 bg-red-500/10'
: 'border-exo-light-gray/30 text-exo-light-gray hover:border-exo-yellow/50 hover:text-exo-yellow'}"
>
{copied ? "Copied!" : failed ? "Copy failed" : "Copy"}
</button>
</div>
{#if description}
<p class="text-exo-light-gray/70 text-xs px-5 pb-3">{description}</p>
{/if}
<div class="bg-black/30 border-t border-exo-light-gray/10">
<pre
class="text-xs text-exo-light-gray/90 font-mono p-4 overflow-x-auto whitespace-pre">{config}</pre>
</div>
</div>
@@ -73,7 +73,7 @@
<!-- svelte-ignore a11y_no_static_element_interactions -->
<div
class="filter-popover absolute right-0 top-full mt-2 w-64 bg-exo-dark-gray border border-exo-yellow/10 rounded-lg shadow-xl z-10"
class="filter-popover absolute right-0 top-full mt-2 w-64 bg-exo-dark-gray border border-exo-yellow/10 rounded-lg shadow-xl z-20"
transition:fly={{ y: -10, duration: 200, easing: cubicOut }}
onclick={(e) => e.stopPropagation()}
role="dialog"
@@ -457,6 +457,7 @@
"deepseek",
"gpt-oss",
"llama",
"gemma",
"flux",
"qwen-image",
"nemotron",
@@ -0,0 +1,565 @@
<script lang="ts">
import { onMount, onDestroy } from "svelte";
import FamilyLogos from "$lib/components/FamilyLogos.svelte";
import {
instances,
instanceLinks,
nodeIdentities,
refreshState,
createInstanceLink,
updateInstanceLink,
deleteInstanceLink,
type Instance,
} from "$lib/stores/app.svelte";
import { deriveBaseModel, deriveFamily } from "$lib/utils/model_family";
type InstanceWrapper = {
MlxRingInstance?: Instance;
MlxJacclInstance?: Instance;
VllmInstance?: Instance;
};
let interval: ReturnType<typeof setInterval> | null = null;
onMount(() => {
refreshState();
interval = setInterval(refreshState, 3000);
});
onDestroy(() => {
if (interval) clearInterval(interval);
});
type InstanceRow = {
id: string;
modelId: string;
family: string;
baseModel: string;
nodeNames: string[];
nodeCount: number;
};
const instanceRows = $derived.by<InstanceRow[]>(() => {
const rows: InstanceRow[] = [];
const ids = nodeIdentities();
for (const [id, raw] of Object.entries(instances())) {
const wrapper = raw as InstanceWrapper;
const inst =
wrapper.MlxRingInstance ??
wrapper.MlxJacclInstance ??
wrapper.VllmInstance;
const modelId = inst?.shardAssignments?.modelId ?? "";
const nodeToRunner = inst?.shardAssignments?.nodeToRunner ?? {};
const nodeIds = Object.keys(nodeToRunner);
const nodeNames = nodeIds
.map((nodeId) => ids[nodeId]?.friendlyName ?? nodeId.slice(0, 6))
.filter((name) => !!name);
rows.push({
id,
modelId,
family: deriveFamily(modelId),
baseModel: deriveBaseModel(modelId),
nodeNames,
nodeCount: nodeIds.length,
});
}
rows.sort((a, b) => a.modelId.localeCompare(b.modelId));
return rows;
});
const instanceById = $derived(
Object.fromEntries(instanceRows.map((r) => [r.id, r])),
);
type LinkRow = {
linkId: string;
prefill: string[];
decode: string[];
families: string[];
multiNode: boolean;
};
const linkRows = $derived.by<LinkRow[]>(() => {
const rows: LinkRow[] = [];
for (const [, link] of Object.entries(instanceLinks())) {
const fams = new Set<string>();
let multiNode = false;
for (const id of [...link.prefillInstances, ...link.decodeInstances]) {
const r = instanceById[id];
if (r && r.baseModel) fams.add(r.baseModel.toLowerCase());
if (r && r.nodeCount > 1) multiNode = true;
}
rows.push({
linkId: link.linkId,
prefill: link.prefillInstances,
decode: link.decodeInstances,
families: Array.from(fams),
multiNode,
});
}
return rows;
});
let editingLinkId = $state<string | null>(null);
let editingPrefill = $state<Set<string>>(new Set());
let editingDecode = $state<Set<string>>(new Set());
let saving = $state(false);
let errorMessage = $state<string | null>(null);
function startCreate() {
editingLinkId = "new";
editingPrefill = new Set();
editingDecode = new Set();
errorMessage = null;
}
function startEdit(row: LinkRow) {
editingLinkId = row.linkId;
editingPrefill = new Set(row.prefill);
editingDecode = new Set(row.decode);
errorMessage = null;
}
function cancelEdit() {
editingLinkId = null;
editingPrefill = new Set();
editingDecode = new Set();
errorMessage = null;
}
type Role = "prefill" | "decode" | "none";
function roleOf(id: string): Role {
if (editingPrefill.has(id)) return "prefill";
if (editingDecode.has(id)) return "decode";
return "none";
}
function setRole(id: string, role: Role) {
const p = new Set(editingPrefill);
const d = new Set(editingDecode);
p.delete(id);
d.delete(id);
if (role === "prefill") p.add(id);
if (role === "decode") d.add(id);
editingPrefill = p;
editingDecode = d;
}
const editingFamilies = $derived.by<string[]>(() => {
const fams = new Set<string>();
for (const id of [...editingPrefill, ...editingDecode]) {
const r = instanceById[id];
if (r && r.baseModel) fams.add(r.baseModel.toLowerCase());
}
return Array.from(fams);
});
const editingMultiNode = $derived.by<string[]>(() => {
const names: string[] = [];
for (const id of [...editingPrefill, ...editingDecode]) {
const r = instanceById[id];
if (r && r.nodeCount > 1) {
names.push(r.baseModel || r.modelId);
}
}
return names;
});
const editingMismatch = $derived(editingFamilies.length > 1);
const canSave = $derived(
editingLinkId !== null &&
editingPrefill.size > 0 &&
editingDecode.size > 0 &&
!saving,
);
async function save() {
if (editingLinkId === null) return;
saving = true;
errorMessage = null;
try {
const prefill = Array.from(editingPrefill);
const decode = Array.from(editingDecode);
if (editingLinkId === "new") {
await createInstanceLink(prefill, decode);
} else {
await updateInstanceLink(editingLinkId, prefill, decode);
}
cancelEdit();
await refreshState();
} catch (err) {
errorMessage = err instanceof Error ? err.message : String(err);
} finally {
saving = false;
}
}
async function remove(linkId: string) {
if (!confirm("Remove this routing?")) return;
try {
await deleteInstanceLink(linkId);
if (editingLinkId === linkId) cancelEdit();
await refreshState();
} catch (err) {
errorMessage = err instanceof Error ? err.message : String(err);
}
}
</script>
<div class="font-mono text-foreground">
<div class="mb-6 space-y-4">
<details open class="group [&_summary::-webkit-details-marker]:hidden">
<summary
class="cursor-pointer list-none text-exo-yellow text-xs font-mono tracking-widest uppercase flex items-center gap-2 hover:opacity-80 transition-opacity"
>
<span
class="inline-block transition-transform group-open:rotate-90 text-exo-light-gray"
>▶</span
>
Prefill vs Decode
</summary>
<div class="mt-2 text-white/80 text-sm leading-relaxed">
Prefill is the compute-heavy pass that consumes the entire prompt and
builds a KV cache. Decode is the memory-bandwidth-bound loop that emits
tokens sequentially from that cache. The two phases have very different
bottlenecks, so running them on different hardware can be substantially
faster than doing both on one node.
</div>
</details>
<details class="group [&_summary::-webkit-details-marker]:hidden">
<summary
class="cursor-pointer list-none text-exo-yellow text-xs font-mono tracking-widest uppercase flex items-center gap-2 hover:opacity-80 transition-opacity"
>
<span
class="inline-block transition-transform group-open:rotate-90 text-exo-light-gray"
>▶</span
>
Linking Instances
</summary>
<div class="mt-2 text-white/80 text-sm leading-relaxed space-y-2">
<p>
A linked route here tells the cluster: when a request is sent to a
model in that cluster, the decode node (or the least active one if
there are multiple) will handle it. If it decides it must do a lot of
prefill not already cached in the prefix cache, it routes the request
to the prefill node over TCP IP. The prefill node streams the KV cache
back to the decode node which picks up from there.
</p>
<p>
Linked instances must be running the same model family — KV layouts
differ across architectures. More on the <a
class="text-exo-yellow underline underline-offset-2 hover:text-exo-yellow-darker transition-colors"
href="https://blog.exolabs.net/nvidia-dgx-spark/"
target="_blank"
rel="noreferrer noopener">blog</a
>.
</p>
</div>
</details>
</div>
{#if errorMessage}
<div
class="mb-4 px-4 py-3 bg-red-500/10 border border-red-500/40 text-red-300 text-sm"
>
{errorMessage}
</div>
{/if}
<section class="mt-12">
<h2
class="text-exo-yellow text-xs font-mono tracking-widest uppercase m-0 mb-3"
>
Existing routes
</h2>
{#if linkRows.length === 0}
{#if editingLinkId === null}
<div class="flex items-center justify-between">
<p class="text-exo-light-gray italic text-sm m-0">
No routes yet. Create one to enable remote prefill.
</p>
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-yellow/15 border border-exo-yellow/50 text-exo-yellow hover:bg-exo-yellow/25 hover:border-exo-yellow/80 transition-colors"
onclick={startCreate}
>
+ New route
</button>
</div>
{/if}
{:else}
{#if editingLinkId === null}
<div class="flex justify-end mb-3">
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-yellow/15 border border-exo-yellow/50 text-exo-yellow hover:bg-exo-yellow/25 hover:border-exo-yellow/80 transition-colors"
onclick={startCreate}
>
+ New route
</button>
</div>
{/if}
<div
class="bg-exo-dark-gray/60 border border-exo-medium-gray/40 flex flex-col"
>
{#each linkRows as row (row.linkId)}
{#if editingLinkId !== row.linkId}
<article
class="p-4 border-b border-exo-light-gray/25 last:border-b-0"
>
{#if row.multiNode}
<div
class="mb-3 px-3 py-2 bg-red-500/10 border border-red-500/40 text-red-300 text-xs tracking-wide"
>
⚠ Multi-node instance detected. Remote prefill currently only
works on single-node (rank-0) instances. This route will not
function until that's supported.
</div>
{/if}
{#if row.families.length > 1}
<div
class="mb-3 px-3 py-2 bg-amber-500/10 border border-amber-500/40 text-amber-300 text-xs tracking-wide"
>
⚠ Mixed model families: {row.families.join(", ")}
</div>
{/if}
<div
class="grid grid-cols-[1fr_auto_1fr_auto] items-center gap-x-3 gap-y-2"
>
<span
class="inline-block justify-self-start text-[10px] font-mono tracking-widest uppercase px-2 py-0.5 bg-exo-yellow/15 border border-exo-yellow/40 text-exo-yellow"
>Prefill</span
>
<span></span>
<span
class="inline-block justify-self-start text-[10px] font-mono tracking-widest uppercase px-2 py-0.5 bg-exo-medium-gray/40 border border-exo-medium-gray/60 text-foreground"
>Decode</span
>
<span></span>
<div class="min-w-0">
<ul class="list-none p-0 m-0 flex flex-col gap-2">
{#each row.prefill as id (id)}
{@const r = instanceById[id]}
{#if r}
<li
class="flex items-center gap-2 px-2.5 py-2 bg-exo-medium-gray/20 border border-exo-medium-gray/40"
>
<FamilyLogos family={r.family} />
<div class="min-w-0 flex-1">
<div
class="text-exo-yellow text-xs font-mono truncate"
>
{r.baseModel || r.modelId}
</div>
<div
class="text-exo-light-gray text-[11px] truncate"
>
{r.nodeNames.join(", ") || "?"}{r.nodeCount > 1
? ` (${r.nodeCount} nodes)`
: ""}
</div>
<div
class="text-exo-light-gray/40 text-[10px] font-mono truncate"
title={r.id}
>
{r.id.slice(0, 8)}
</div>
</div>
</li>
{/if}
{/each}
</ul>
</div>
<div class="text-exo-yellow/60 text-xl px-2" aria-hidden="true">
</div>
<div class="min-w-0">
<ul class="list-none p-0 m-0 flex flex-col gap-2">
{#each row.decode as id (id)}
{@const r = instanceById[id]}
{#if r}
<li
class="flex items-center gap-2 px-2.5 py-2 bg-exo-medium-gray/20 border border-exo-medium-gray/40"
>
<FamilyLogos family={r.family} />
<div class="min-w-0 flex-1">
<div
class="text-exo-yellow text-xs font-mono truncate"
>
{r.baseModel || r.modelId}
</div>
<div
class="text-exo-light-gray text-[11px] truncate"
>
{r.nodeNames.join(", ") || "?"}{r.nodeCount > 1
? ` (${r.nodeCount} nodes)`
: ""}
</div>
<div
class="text-exo-light-gray/40 text-[10px] font-mono truncate"
title={r.id}
>
{r.id.slice(0, 8)}
</div>
</div>
</li>
{/if}
{/each}
</ul>
</div>
<div class="flex gap-2 pl-3">
<button
class="px-2 py-0.5 text-[11px] font-mono tracking-wider uppercase bg-exo-medium-gray/30 border border-exo-medium-gray/60 rounded text-foreground hover:border-exo-yellow/60 hover:text-exo-yellow disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
onclick={() => startEdit(row)}
disabled={editingLinkId !== null}
>
Edit
</button>
<button
class="px-2 py-0.5 text-[11px] font-mono tracking-wider uppercase bg-red-500/15 border border-red-500/40 rounded text-red-300 hover:bg-red-500/25 transition-colors"
onclick={() => remove(row.linkId)}
>
Remove
</button>
</div>
</div>
</article>
{/if}
{/each}
</div>
{/if}
</section>
{#if editingLinkId !== null && instanceRows.length === 0}
<section
class="mt-6 bg-exo-dark-gray/60 border border-exo-yellow/30 px-4 py-2.5 flex items-center justify-between gap-3"
>
<span class="text-exo-light-gray italic text-sm font-mono"
>No instances available.</span
>
<button
class="px-3 py-1 text-xs font-mono tracking-wider uppercase bg-exo-medium-gray/30 border border-exo-medium-gray/60 rounded text-foreground hover:border-exo-yellow/60 transition-colors"
onclick={cancelEdit}
>
Cancel
</button>
</section>
{:else if editingLinkId !== null}
<section class="mt-6 bg-exo-dark-gray/60 border border-exo-yellow/30 p-5">
<h2
class="text-exo-yellow text-xs font-mono tracking-widest uppercase m-0 mb-3"
>
{editingLinkId === "new" ? "New route" : "Edit route"}
</h2>
{#if editingMismatch}
<div
class="mb-3 px-3 py-2 bg-amber-500/10 border border-amber-500/40 text-amber-300 text-xs tracking-wide"
>
⚠ Selected instances span multiple model families: <strong
>{editingFamilies.join(", ")}</strong
>. Linking across families produces a corrupt KV cache.
</div>
{/if}
{#if editingMultiNode.length > 0}
<div
class="mb-3 px-3 py-2 bg-red-500/10 border border-red-500/40 text-red-300 text-xs tracking-wide"
>
⚠ Multi-node instance(s) selected: <strong
>{editingMultiNode.join(", ")}</strong
>. Remote prefill currently only works on single-node instances. This
route will not function until multi-node support lands.
</div>
{/if}
<p class="text-exo-light-gray text-xs mb-4">
Pick a role for each instance:
<span class="text-exo-yellow">Prefill</span>
serves KV cache,
<span class="text-foreground">Decode</span> consumes it.
</p>
<div
class="grid gap-2.5"
style="grid-template-columns: repeat(auto-fill, minmax(360px, 1fr));"
>
{#each instanceRows as row (row.id)}
{@const role = roleOf(row.id)}
<div
class="border p-3 flex flex-col gap-2.5 transition-colors {role ===
'prefill'
? 'border-exo-yellow/60 bg-exo-dark-gray/60'
: role === 'decode'
? 'border-exo-light-gray/60 bg-exo-dark-gray/60'
: 'border-exo-medium-gray/40 bg-exo-dark-gray/40'}"
>
<div class="flex items-center gap-2">
<FamilyLogos family={row.family} />
<div class="min-w-0 flex-1">
<div class="text-exo-yellow text-xs font-mono truncate">
{row.baseModel || row.modelId}
</div>
<div class="text-exo-light-gray text-[11px] truncate">
{row.nodeNames.join(", ") || "?"}{row.nodeCount > 1
? ` (${row.nodeCount} nodes)`
: ""}
</div>
<div
class="text-exo-light-gray/40 text-[10px] font-mono truncate"
title={row.id}
>
{row.id.slice(0, 8)}
</div>
</div>
{#if row.nodeCount > 1}
<span
class="text-[9px] font-mono tracking-widest uppercase px-1.5 py-0.5 bg-red-500/15 border border-red-500/40 text-red-300"
title="Multi-node instances are not supported by remote prefill yet."
>Unsupported</span
>
{/if}
</div>
<div
class="flex rounded-md overflow-hidden border border-exo-light-gray/40 divide-x divide-exo-light-gray/40"
>
<button
class="flex-1 px-2 py-1 text-[11px] font-mono tracking-wider uppercase transition-colors {role ===
'prefill'
? 'bg-exo-yellow/20 text-exo-yellow'
: 'bg-transparent text-white/80 hover:text-exo-yellow'}"
onclick={() =>
setRole(row.id, role === "prefill" ? "none" : "prefill")}
>Prefill</button
>
<button
class="flex-1 px-2 py-1 text-[11px] font-mono tracking-wider uppercase transition-colors {role ===
'decode'
? 'bg-exo-medium-gray/50 text-foreground'
: 'bg-transparent text-white/80 hover:text-foreground'}"
onclick={() =>
setRole(row.id, role === "decode" ? "none" : "decode")}
>Decode</button
>
</div>
</div>
{/each}
</div>
<div class="flex gap-2 mt-5 justify-end">
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-yellow/15 border border-exo-yellow/50 text-exo-yellow hover:bg-exo-yellow/25 hover:border-exo-yellow/80 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
onclick={save}
disabled={!canSave}
>
{saving ? "Saving..." : "Save route"}
</button>
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-medium-gray/30 border border-exo-medium-gray/60 text-foreground hover:border-exo-yellow/60 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
onclick={cancelEdit}
disabled={saving}
>
Cancel
</button>
</div>
</section>
{/if}
</div>
+1
View File
@@ -13,3 +13,4 @@ export { default as ModelFilterPopover } from "./ModelFilterPopover.svelte";
export { default as ModelPickerGroup } from "./ModelPickerGroup.svelte";
export { default as ModelPickerModal } from "./ModelPickerModal.svelte";
export { default as ChatModelSelector } from "./ChatModelSelector.svelte";
export { default as IntegrationCard } from "./IntegrationCard.svelte";
+277 -24
View File
@@ -74,6 +74,12 @@ export interface Instance {
};
}
export interface RawInstanceLink {
linkId: string;
prefillInstances: string[];
decodeInstances: string[];
}
// Granular node state types from the new state structure
interface RawNodeIdentity {
modelId?: string;
@@ -223,6 +229,7 @@ interface RawStateResponse {
}
>;
runners?: Record<string, unknown>;
instanceLinks?: Record<string, RawInstanceLink>;
downloads?: Record<string, unknown[]>;
// New granular node state fields
nodeIdentities?: Record<string, RawNodeIdentity>;
@@ -257,11 +264,12 @@ interface RawStateResponse {
}
export interface MessageAttachment {
type: "image" | "text" | "file" | "generated-image";
type: "image" | "text" | "file" | "generated-image" | "pdf";
name: string;
content?: string;
preview?: string;
mimeType?: string;
pageImages?: string[];
}
export interface TopLogprob {
@@ -540,6 +548,8 @@ class AppStore {
topologyData = $state<TopologyData | null>(null);
instances = $state<Record<string, unknown>>({});
runners = $state<Record<string, unknown>>({});
instanceLinks = $state<Record<string, RawInstanceLink>>({});
featureFlags = $state<Record<string, boolean>>({});
downloads = $state<Record<string, unknown[]>>({});
nodeDisk = $state<
Record<
@@ -1273,6 +1283,7 @@ class AppStore {
startPolling() {
this.fetchState();
this.fetchFeatureFlags();
this.fetchInterval = setInterval(() => this.fetchState(), 1000);
}
@@ -1284,6 +1295,16 @@ class AppStore {
this.stopPreviewsPolling();
}
async fetchFeatureFlags() {
try {
const response = await fetch("/v1/feature-flags");
if (!response.ok) return;
this.featureFlags = await response.json();
} catch {
// Silently ignore — defaults to all-disabled.
}
}
async fetchState() {
try {
const response = await fetch("/state");
@@ -1309,6 +1330,11 @@ class AppStore {
if (data.runners) {
this.runners = data.runners;
}
if (data.instanceLinks) {
this.instanceLinks = data.instanceLinks;
} else {
this.instanceLinks = {};
}
if (data.downloads) {
this.downloads = data.downloads;
}
@@ -1669,7 +1695,15 @@ class AppStore {
}
}
}
return { role: m.role, content: msgContent };
const out: {
role: string;
content: string;
reasoning_content?: string;
} = { role: m.role, content: msgContent };
if (m.role === "assistant" && m.thinking) {
out.reasoning_content = m.thinking;
}
return out;
}),
];
@@ -1876,7 +1910,15 @@ class AppStore {
const apiMessages = [
systemPrompt,
...targetConversation.messages.slice(0, -1).map((m) => {
return { role: m.role, content: m.content };
const out: {
role: string;
content: string;
reasoning_content?: string;
} = { role: m.role, content: m.content };
if (m.role === "assistant" && m.thinking) {
out.reasoning_content = m.thinking;
}
return out;
}),
];
@@ -2242,6 +2284,7 @@ class AppStore {
type: string;
textContent?: string;
preview?: string;
pageImages?: string[];
}[],
enableThinking?: boolean | null,
): Promise<void> {
@@ -2277,6 +2320,20 @@ class AppStore {
preview: file.preview,
mimeType: file.type,
});
} else if (
file.pageImages ||
(file.textContent && file.type === "application/pdf")
) {
attachments.push({
type: "pdf",
name: file.name,
content: file.textContent,
pageImages: file.pageImages,
mimeType: file.type,
});
if (file.textContent) {
fileContext += `\n\n[File: ${file.name}]\n\`\`\`\n${file.textContent}\n\`\`\``;
}
} else if (file.textContent) {
attachments.push({
type: "text",
@@ -2344,22 +2401,89 @@ class AppStore {
const apiMessages = [
systemPrompt,
...targetConversation.messages.slice(0, -1).map((m) => {
// Build content including any text file attachments
// Check if this message has image or PDF attachments
const visualAttachments = m.attachments?.filter(
(a) =>
(a.type === "image" && a.preview) ||
(a.type === "pdf" && a.pageImages?.length),
);
if (visualAttachments && visualAttachments.length > 0) {
// Build multimodal content array (OpenAI vision format)
const contentParts: Array<
| { type: "text"; text: string }
| { type: "image_url"; image_url: { url: string } }
> = [];
// Add image parts first
for (const att of visualAttachments) {
if (att.type === "image" && att.preview) {
contentParts.push({
type: "image_url",
image_url: { url: att.preview },
});
} else if (att.type === "pdf" && att.pageImages) {
for (const pageImg of att.pageImages) {
contentParts.push({
type: "image_url",
image_url: { url: pageImg },
});
}
}
}
// Build text content including any text/pdf file attachments
let textContent = m.content;
if (m.attachments) {
for (const attachment of m.attachments) {
if (
(attachment.type === "text" || attachment.type === "pdf") &&
attachment.content
) {
textContent += `\n\n[File: ${attachment.name}]\n\`\`\`\n${attachment.content}\n\`\`\``;
}
}
}
if (textContent) {
contentParts.push({ type: "text", text: textContent });
}
const out: {
role: string;
content: typeof contentParts;
reasoning_content?: string;
} = { role: m.role, content: contentParts };
if (m.role === "assistant" && m.thinking) {
out.reasoning_content = m.thinking;
}
return out;
}
// Text-only message (original path)
let msgContent = m.content;
// Add text attachments as context
// Add text/pdf attachments as context
if (m.attachments) {
for (const attachment of m.attachments) {
if (attachment.type === "text" && attachment.content) {
if (
(attachment.type === "text" || attachment.type === "pdf") &&
attachment.content
) {
msgContent += `\n\n[File: ${attachment.name}]\n\`\`\`\n${attachment.content}\n\`\`\``;
}
}
}
return {
role: m.role,
content: msgContent,
};
const out: {
role: string;
content: string;
reasoning_content?: string;
} = { role: m.role, content: msgContent };
if (m.role === "assistant" && m.thinking) {
out.reasoning_content = m.thinking;
}
return out;
}),
];
@@ -2650,6 +2774,9 @@ class AppStore {
this.syncActiveMessagesIfNeeded(targetConversationId);
this.saveConversationsToStorage();
const abortController = new AbortController();
this.currentAbortController = abortController;
try {
// Determine the model to use
const model = this.getModelForRequest(modelId);
@@ -2704,6 +2831,7 @@ class AppStore {
"Content-Type": "application/json",
},
body: JSON.stringify(requestBody),
signal: abortController.signal,
});
if (!response.ok) {
@@ -2843,14 +2971,27 @@ class AppStore {
);
}
} catch (error) {
console.error("Error generating image:", error);
this.handleStreamingError(
error,
targetConversationId,
assistantMessage.id,
"Failed to generate image",
);
if (abortController.signal.aborted) {
this.updateConversationMessage(
targetConversationId,
assistantMessage.id,
(msg) => {
msg.content = "Cancelled";
msg.attachments = [];
},
);
this.syncActiveMessagesIfNeeded(targetConversationId);
} else {
console.error("Error generating image:", error);
this.handleStreamingError(
error,
targetConversationId,
assistantMessage.id,
"Failed to generate image",
);
}
} finally {
this.currentAbortController = null;
this.isLoading = false;
this.saveConversationsToStorage();
}
@@ -2914,6 +3055,9 @@ class AppStore {
// Clear editing state
this.editingImage = null;
const abortController = new AbortController();
this.currentAbortController = abortController;
try {
// Determine the model to use
const model = this.getModelForRequest(modelId);
@@ -2975,6 +3119,7 @@ class AppStore {
const apiResponse = await fetch("/v1/images/edits", {
method: "POST",
body: formData,
signal: abortController.signal,
});
if (!apiResponse.ok) {
@@ -3075,14 +3220,27 @@ class AppStore {
);
}
} catch (error) {
console.error("Error editing image:", error);
this.handleStreamingError(
error,
targetConversationId,
assistantMessage.id,
"Failed to edit image",
);
if (abortController.signal.aborted) {
this.updateConversationMessage(
targetConversationId,
assistantMessage.id,
(msg) => {
msg.content = "Cancelled";
msg.attachments = [];
},
);
this.syncActiveMessagesIfNeeded(targetConversationId);
} else {
console.error("Error editing image:", error);
this.handleStreamingError(
error,
targetConversationId,
assistantMessage.id,
"Failed to edit image",
);
}
} finally {
this.currentAbortController = null;
this.isLoading = false;
this.saveConversationsToStorage();
}
@@ -3149,6 +3307,85 @@ class AppStore {
}
}
/**
* Cancel/pause an active download on a specific node
*/
async cancelDownload(nodeId: string, modelId: string): Promise<void> {
try {
const response = await fetch("/download/cancel", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
targetNodeId: nodeId,
modelId: modelId,
}),
});
if (!response.ok) {
const errorText = await response.text();
throw new Error(
`Failed to cancel download: ${response.status} - ${errorText}`,
);
}
} catch (error) {
console.error("Error cancelling download:", error);
throw error;
}
}
async createInstanceLink(
prefillInstances: string[],
decodeInstances: string[],
): Promise<void> {
const response = await fetch("/v1/instance-links", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
prefill_instances: prefillInstances,
decode_instances: decodeInstances,
}),
});
if (!response.ok) {
throw new Error(
`Failed to create instance link: ${response.status} ${await response.text()}`,
);
}
}
async updateInstanceLink(
linkId: string,
prefillInstances: string[],
decodeInstances: string[],
): Promise<void> {
const response = await fetch(
`/v1/instance-links/${encodeURIComponent(linkId)}`,
{
method: "PUT",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
prefill_instances: prefillInstances,
decode_instances: decodeInstances,
}),
},
);
if (!response.ok) {
throw new Error(
`Failed to update instance link: ${response.status} ${await response.text()}`,
);
}
}
async deleteInstanceLink(linkId: string): Promise<void> {
const response = await fetch(
`/v1/instance-links/${encodeURIComponent(linkId)}`,
{ method: "DELETE" },
);
if (!response.ok) {
throw new Error(
`Failed to delete instance link: ${response.status} ${await response.text()}`,
);
}
}
/**
* Delete a downloaded model from a specific node
*/
@@ -3247,6 +3484,19 @@ export const prefillProgress = () => appStore.prefillProgress;
export const topologyData = () => appStore.topologyData;
export const instances = () => appStore.instances;
export const runners = () => appStore.runners;
export const instanceLinks = () => appStore.instanceLinks;
export const featureFlags = () => appStore.featureFlags;
export const createInstanceLink = (
prefillInstances: string[],
decodeInstances: string[],
) => appStore.createInstanceLink(prefillInstances, decodeInstances);
export const updateInstanceLink = (
linkId: string,
prefillInstances: string[],
decodeInstances: string[],
) => appStore.updateInstanceLink(linkId, prefillInstances, decodeInstances);
export const deleteInstanceLink = (linkId: string) =>
appStore.deleteInstanceLink(linkId);
export const downloads = () => appStore.downloads;
export const nodeDisk = () => appStore.nodeDisk;
export const placementPreviews = () => appStore.placementPreviews;
@@ -3271,6 +3521,7 @@ export const sendMessage = (
type: string;
textContent?: string;
preview?: string;
pageImages?: string[];
}[],
enableThinking?: boolean | null,
) => appStore.sendMessage(content, files, enableThinking);
@@ -3369,6 +3620,8 @@ export const resetImageGenerationParams = () =>
// Download actions
export const startDownload = (nodeId: string, shardMetadata: object) =>
appStore.startDownload(nodeId, shardMetadata);
export const cancelDownload = (nodeId: string, modelId: string) =>
appStore.cancelDownload(nodeId, modelId);
export const deleteDownload = (nodeId: string, modelId: string) =>
appStore.deleteDownload(nodeId, modelId);
+94 -1
View File
@@ -2,6 +2,41 @@
* File attachment types for the chat interface
*/
import { getDocument, GlobalWorkerOptions, version } from "pdfjs-dist";
import type { DocumentInitParameters } from "pdfjs-dist/types/src/display/api";
// Safari (through at least 18/26) does not implement
// ReadableStream.prototype[Symbol.asyncIterator], which pdfjs-dist uses
// internally in getTextContent(). Without this polyfill, `for await (const n
// of stream)` throws "undefined is not a function" and PDF processing fails.
if (
typeof ReadableStream !== "undefined" &&
!(ReadableStream.prototype as unknown as Record<symbol, unknown>)[
Symbol.asyncIterator
]
) {
(ReadableStream.prototype as unknown as Record<symbol, unknown>)[
Symbol.asyncIterator
] = async function* (this: ReadableStream<unknown>) {
const reader = this.getReader();
try {
while (true) {
const { done, value } = await reader.read();
if (done) return;
yield value;
}
} finally {
reader.releaseLock();
}
};
}
GlobalWorkerOptions.workerSrc = `https://cdn.jsdelivr.net/npm/pdfjs-dist@${version}/build/pdf.worker.mjs`;
const PDF_PAGE_SCALE = 2.0;
const PDF_MAX_PAGES = 20;
const PDF_MAX_TEXT_CHARS = 100_000;
export interface ChatUploadedFile {
id: string;
name: string;
@@ -10,6 +45,7 @@ export interface ChatUploadedFile {
file: File;
preview?: string;
textContent?: string;
pageImages?: string[];
}
export interface ChatAttachment {
@@ -194,6 +230,58 @@ export function readFileAsText(file: File): Promise<string> {
});
}
async function extractPdfContent(
file: File,
): Promise<{ text: string; pageImages: string[] }> {
const arrayBuffer = await file.arrayBuffer();
const pdf = await getDocument({
data: new Uint8Array(arrayBuffer),
useSystemFonts: true,
} as DocumentInitParameters).promise;
const numPages = Math.min(pdf.numPages, PDF_MAX_PAGES);
const pageTexts: string[] = [];
const pageImages: string[] = [];
for (let i = 1; i <= numPages; i++) {
const page = await pdf.getPage(i);
const content = await page.getTextContent();
const strings = content.items
.filter((item: any) => "str" in item)
.map((item: any) => item.str as string);
pageTexts.push(strings.join(" "));
const viewport = page.getViewport({ scale: PDF_PAGE_SCALE });
const canvas = new OffscreenCanvas(viewport.width, viewport.height);
const ctx = canvas.getContext("2d");
if (ctx) {
await page.render({ canvasContext: ctx as any, viewport }).promise;
const blob = await canvas.convertToBlob({
type: "image/jpeg",
quality: 0.8,
});
const reader = new FileReader();
const dataUrl = await new Promise<string>((resolve, reject) => {
reader.onload = () => resolve(reader.result as string);
reader.onerror = () => reject(reader.error);
reader.readAsDataURL(blob);
});
pageImages.push(dataUrl);
}
}
let text = pageTexts.join("\n\n").trim();
if (text.length > PDF_MAX_TEXT_CHARS) {
text = text.slice(0, PDF_MAX_TEXT_CHARS) + "\n\n[truncated]";
}
if (pdf.numPages > PDF_MAX_PAGES) {
text += `\n\n[showing ${PDF_MAX_PAGES} of ${pdf.numPages} pages]`;
}
return { text, pageImages };
}
/**
* Process uploaded files into ChatUploadedFile format
*/
@@ -223,7 +311,12 @@ export async function processUploadedFiles(
const textContent = await readFileAsText(file);
results.push({ ...base, textContent });
} else if (category === "pdf") {
results.push(base);
const { text, pageImages } = await extractPdfContent(file);
results.push({
...base,
textContent: text || undefined,
pageImages: pageImages.length > 0 ? pageImages : undefined,
});
} else if (category === "audio") {
const preview = await readFileAsDataURL(file);
results.push({ ...base, preview });
+55
View File
@@ -0,0 +1,55 @@
export async function copyText(text: string): Promise<boolean> {
if (
typeof window !== "undefined" &&
window.isSecureContext &&
navigator.clipboard?.writeText
) {
try {
await navigator.clipboard.writeText(text);
return true;
} catch {
// fall through to execCommand fallback
}
}
if (typeof document === "undefined") {
return false;
}
const textarea = document.createElement("textarea");
textarea.value = text;
textarea.setAttribute("readonly", "");
textarea.style.position = "fixed";
textarea.style.top = "0";
textarea.style.left = "0";
textarea.style.width = "1px";
textarea.style.height = "1px";
textarea.style.padding = "0";
textarea.style.border = "none";
textarea.style.outline = "none";
textarea.style.boxShadow = "none";
textarea.style.background = "transparent";
textarea.style.opacity = "0";
document.body.appendChild(textarea);
const previousSelection = document.getSelection();
const previousRange =
previousSelection && previousSelection.rangeCount > 0
? previousSelection.getRangeAt(0)
: null;
try {
textarea.focus();
textarea.select();
textarea.setSelectionRange(0, text.length);
return document.execCommand("copy");
} catch {
return false;
} finally {
document.body.removeChild(textarea);
if (previousRange && previousSelection) {
previousSelection.removeAllRanges();
previousSelection.addRange(previousRange);
}
}
}
+44
View File
@@ -0,0 +1,44 @@
// Mirrors src/exo/shared/models/model_cards.py:derive_base_model
const QUANT_SUFFIXES = new RegExp(
"[-_ ](?:MLX|MXFP[0-9]+|NVFP[0-9]+|GPTQ|AWQ|GGUF|fp16|bf16|fp8|int[0-9]+|[0-9]+(?:\\.[0-9]+)?bit|Q[0-9]+(?:_[A-Z0-9]+)?|gs[0-9]+)" +
"(?:[-_ ](?:MLX|Q[0-9]+|Int[0-9]+|[A-Z0-9]+|gs[0-9]+))*$",
"i",
);
function normalize(s: string): string {
return s
.replaceAll("-", " ")
.replaceAll("_", " ")
.replaceAll(" ", " ")
.trim();
}
export function deriveBaseModel(modelId: string): string {
const short = modelId.includes("/")
? (modelId.split("/").pop() ?? modelId)
: modelId;
const stripped = short.replace(QUANT_SUFFIXES, "");
return normalize(stripped);
}
export function baseModelsCompatible(a: string, b: string): boolean {
return deriveBaseModel(a).toLowerCase() === deriveBaseModel(b).toLowerCase();
}
// Mirrors src/exo/shared/models/model_cards.py:derive_family
export function deriveFamily(modelId: string): string {
const short = modelId.includes("/")
? (modelId.split("/").pop() ?? modelId)
: modelId;
const stripped = short
.replace(QUANT_SUFFIXES, "")
.toLowerCase()
.replaceAll("_", "-");
const parts = stripped.split(/[-.]/);
const familyParts: string[] = [];
for (const p of parts) {
if (/^\d+$/.test(p) || /^\d+[bm]?$/i.test(p)) break;
familyParts.push(p);
}
return familyParts.length > 0 ? familyParts.join("-") : stripped;
}
+30
View File
@@ -265,6 +265,7 @@
let mounted = $state(false);
let localNodeId = $state<string | null>(null);
let pendingFirefoxQuery = $state<string | null>(null); // ?q= param deferred until state loads
// ── Onboarding wizard state ──
const ONBOARDING_COMPLETE_KEY = "exo-onboarding-complete";
@@ -1309,6 +1310,20 @@
return;
}
// Firefox AI sidebar integration: handle ?q= query parameter
// Firefox's built-in AI sidebar (about:config: browser.ml.chat.enabled) sends
// the user's prompt as ?q=<URL-encoded prompt> to the configured provider URL.
// See: https://support.mozilla.org/en-US/kb/ai-chatbot
const queryParam = params.get("q");
if (queryParam) {
// Clean up the URL to prevent re-submission on page refresh
window.history.replaceState({}, "", window.location.pathname);
// Defer the auto-send until cluster state is loaded (topologyData,
// instances, availableMemory) so that model auto-selection works
// correctly. The $effect below will pick this up once data is ready.
pendingFirefoxQuery = queryParam;
}
// Check server-side onboarding state (persisted in ~/.exo)
try {
const res = await fetch("/onboarding");
@@ -1329,6 +1344,18 @@
}
});
// Deferred Firefox AI sidebar auto-send: wait for cluster state and model
// list before submitting. Both data (from /state polling) and models (from
// the async /models fetch in onMount) must be loaded for handleAutoSend to
// correctly auto-select a model.
$effect(() => {
if (pendingFirefoxQuery && data && models.length > 0) {
const query = pendingFirefoxQuery;
pendingFirefoxQuery = null;
handleChatSend(query);
}
});
async function fetchModels() {
try {
const response = await fetch("/models");
@@ -3408,6 +3435,7 @@
>
<li>Connect nodes with TB5 cables</li>
<li>Boot to Recovery (hold power 10s → Options)</li>
<li>Open Terminal from the Utilities menu</li>
<li>
Run
<code class="text-yellow-300 bg-yellow-400/10 px-1 rounded"
@@ -4795,6 +4823,7 @@
>
<li>Connect nodes with TB5 cables</li>
<li>Boot to Recovery (hold power 10s → Options)</li>
<li>Open Terminal from the Utilities menu</li>
<li>
Run
<code class="text-yellow-300 bg-yellow-400/10 px-1 rounded"
@@ -4941,6 +4970,7 @@
>
<li>Connect nodes with TB5 cables</li>
<li>Boot to Recovery (hold power 10s → Options)</li>
<li>Open Terminal from the Utilities menu</li>
<li>
Run
<code
@@ -0,0 +1,81 @@
<script lang="ts">
import { browser } from "$app/environment";
import HeaderNav from "$lib/components/HeaderNav.svelte";
import PrefillDecodeDisaggregation from "$lib/components/PrefillDecodeDisaggregation.svelte";
import { featureFlags, refreshState } from "$lib/stores/app.svelte";
import { onMount } from "svelte";
type TabId = "prefill-decode";
const tabs: { id: TabId; label: string }[] = [
{ id: "prefill-decode", label: "Prefill / Decode" },
];
let activeTab = $state<TabId>(tabs[0].id);
let flagsLoaded = $state(false);
onMount(() => {
refreshState().finally(() => {
flagsLoaded = true;
});
});
const flags = $derived(featureFlags());
const enabled = $derived(flags["disaggregation"] === true);
$effect(() => {
if (browser && flagsLoaded && !enabled) {
// No advanced features enabled — bounce home.
window.location.hash = "/";
}
});
</script>
<div class="min-h-screen bg-exo-dark-gray flex flex-col">
<HeaderNav />
<main class="flex-1 max-w-[1100px] mx-auto w-full px-4 md:px-6 py-8">
{#if !flagsLoaded}
<div class="text-exo-light-gray/60 text-sm">Loading…</div>
{:else if !enabled}
<div class="text-exo-light-gray/60 text-sm">
No advanced features enabled. Set <code
class="text-exo-yellow font-mono">ENABLE_DISAGGREGATION=true</code
> on the cluster to access prefill/decode disaggregation.
</div>
{:else}
<div class="mb-4">
<h1
class="text-white text-xl md:text-2xl font-semibold tracking-wide mb-2"
>
Advanced
</h1>
<p class="text-exo-light-gray/60 text-sm">
Cluster-level configuration. Most users don't need anything here.
</p>
</div>
<div
class="flex flex-wrap gap-2 mb-6 border-b border-exo-light-gray/10 pb-3"
>
{#each tabs as tab (tab.id)}
<button
onclick={() => (activeTab = tab.id)}
class="px-3 py-1.5 text-xs rounded-md transition-all cursor-pointer
{activeTab === tab.id
? 'bg-exo-yellow/15 text-exo-yellow border border-exo-yellow/30'
: 'text-exo-light-gray/60 hover:text-white/80 border border-transparent hover:border-exo-light-gray/20'}"
>
{tab.label}
</button>
{/each}
</div>
<div class="space-y-4">
{#if activeTab === "prefill-decode"}
<PrefillDecodeDisaggregation />
{/if}
</div>
{/if}
</main>
</div>
+99 -94
View File
@@ -9,6 +9,7 @@
refreshState,
lastUpdate as lastUpdateStore,
startDownload,
cancelDownload,
deleteDownload,
} from "$lib/stores/app.svelte";
import {
@@ -349,6 +350,59 @@
});
</script>
{#snippet trashIcon()}
<svg
class="w-5 h-5"
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
>
<path
d="M4 6h12M8 6V4h4v2m1 0v10a1 1 0 01-1 1H8a1 1 0 01-1-1V6h6"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
{/snippet}
{#snippet downloadIcon(size?: string)}
<svg
class={size ?? "w-5 h-5"}
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
>
<path
d="M10 3v10m0 0l-3-3m3 3l3-3M3 17h14"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
{/snippet}
{#snippet pauseIcon()}
<svg class="w-5 h-5" viewBox="0 0 20 20" fill="currentColor">
<path
fill-rule="evenodd"
d="M6 4h2v12H6V4zm6 0h2v12h-2V4z"
clip-rule="evenodd"
></path>
</svg>
{/snippet}
{#snippet deleteButton(nodeId: string, modelId: string)}
<button
type="button"
class="text-white/50 hover:text-red-400 transition-colors cursor-pointer"
onclick={() => deleteDownload(nodeId, modelId)}
title="Delete from this node"
>
{@render trashIcon()}
</button>
{/snippet}
<div class="min-h-screen bg-exo-dark-gray text-white">
<HeaderNav showHome={true} />
<div class="max-w-7xl mx-auto px-4 lg:px-8 py-6 space-y-6">
@@ -486,27 +540,7 @@
<span class="text-xs text-white/70"
>{formatBytes(cell.totalBytes)}</span
>
<button
type="button"
class="text-white/50 hover:text-red-400 transition-colors mt-0.5 cursor-pointer"
onclick={() =>
deleteDownload(col.nodeId, row.modelId)}
title="Delete from this node"
>
<svg
class="w-5 h-5"
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
>
<path
d="M4 6h12M8 6V4h4v2m1 0v10a1 1 0 01-1 1H8a1 1 0 01-1-1V6h6"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
</button>
{@render deleteButton(col.nodeId, row.modelId)}
</div>
{:else if cell.kind === "downloading"}
<div
@@ -533,6 +567,18 @@
<span class="text-[10px] text-white/70"
>{formatSpeed(cell.speed)}</span
>
<div class="flex gap-1 mt-0.5">
<button
type="button"
class="text-white/50 hover:text-exo-yellow transition-colors cursor-pointer"
onclick={() =>
cancelDownload(col.nodeId, row.modelId)}
title="Pause download"
>
{@render pauseIcon()}
</button>
{@render deleteButton(col.nodeId, row.modelId)}
</div>
</div>
{:else if cell.kind === "pending"}
<div
@@ -558,32 +604,24 @@
).toFixed(1)}%"
></div>
</div>
{#if row.shardMetadata}
<button
type="button"
class="text-white/50 hover:text-exo-yellow transition-colors cursor-pointer"
onclick={() =>
startDownload(col.nodeId, row.shardMetadata!)}
title="Resume download on this node"
>
<svg
class="w-5 h-5"
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
<div class="flex gap-1">
{#if row.shardMetadata}
<button
type="button"
class="text-white/50 hover:text-exo-yellow transition-colors cursor-pointer"
onclick={() =>
startDownload(col.nodeId, row.shardMetadata!)}
title="Resume download on this node"
>
<path
d="M10 3v10m0 0l-3-3m3 3l3-3M3 17h14"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
</button>
{:else}
<span class="text-white/50 text-[10px]">paused</span
>
{/if}
{@render downloadIcon()}
</button>
{:else}
<span class="text-white/50 text-[10px]"
>paused</span
>
{/if}
{@render deleteButton(col.nodeId, row.modelId)}
</div>
{:else if row.shardMetadata}
<button
type="button"
@@ -592,19 +630,7 @@
startDownload(col.nodeId, row.shardMetadata!)}
title="Start download on this node"
>
<svg
class="w-6 h-6"
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
>
<path
d="M10 3v10m0 0l-3-3m3 3l3-3M3 17h14"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
{@render downloadIcon("w-6 h-6")}
</button>
{:else}
<span class="text-white/40 text-sm">...</span>
@@ -626,29 +652,20 @@
clip-rule="evenodd"
></path>
</svg>
{#if row.shardMetadata}
<button
type="button"
class="text-white/50 hover:text-exo-yellow transition-colors cursor-pointer"
onclick={() =>
startDownload(col.nodeId, row.shardMetadata!)}
title="Retry download on this node"
>
<svg
class="w-5 h-5"
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
<div class="flex gap-1">
{#if row.shardMetadata}
<button
type="button"
class="text-white/50 hover:text-exo-yellow transition-colors cursor-pointer"
onclick={() =>
startDownload(col.nodeId, row.shardMetadata!)}
title="Retry download on this node"
>
<path
d="M10 3v10m0 0l-3-3m3 3l3-3M3 17h14"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
</button>
{/if}
{@render downloadIcon()}
</button>
{/if}
{@render deleteButton(col.nodeId, row.modelId)}
</div>
</div>
{:else}
<div
@@ -666,19 +683,7 @@
startDownload(col.nodeId, row.shardMetadata!)}
title="Download to this node"
>
<svg
class="w-5 h-5"
viewBox="0 0 20 20"
fill="none"
stroke="currentColor"
stroke-width="2"
>
<path
d="M10 3v10m0 0l-3-3m3 3l3-3M3 17h14"
stroke-linecap="round"
stroke-linejoin="round"
></path>
</svg>
{@render downloadIcon()}
</button>
{/if}
</div>
@@ -0,0 +1,688 @@
<script lang="ts">
import { browser } from "$app/environment";
import { fade } from "svelte/transition";
import HeaderNav from "$lib/components/HeaderNav.svelte";
import IntegrationCard from "$lib/components/IntegrationCard.svelte";
import { instances, refreshState } from "$lib/stores/app.svelte";
import { onMount } from "svelte";
const apiUrl = browser
? window.location.origin.replace("localhost", "127.0.0.1")
: "http://127.0.0.1:52415";
const instancesData = $derived(instances());
let modelCapabilities = $state<Record<string, string[]>>({});
let modelContextLengths = $state<Record<string, number>>({});
let modelReasoningDialects = $state<Record<string, string>>({});
const runningModels = $derived.by(() => {
const models: string[] = [];
for (const [, wrapper] of Object.entries(instancesData)) {
if (wrapper && typeof wrapper === "object") {
const values = Object.values(wrapper as Record<string, unknown>);
if (values.length > 0) {
const instance = values[0];
if (instance && typeof instance === "object") {
const inst = instance as {
shardAssignments?: { modelId?: string };
};
const modelId = inst.shardAssignments?.modelId;
if (modelId && !models.includes(modelId)) {
models.push(modelId);
}
}
}
}
}
return models;
});
function estimateParamSize(modelId: string): number {
const match = modelId.match(/(\d+(?:\.\d+)?)[Bb]/);
return match ? parseFloat(match[1]) : 0;
}
const modelsBySize = $derived(
[...runningModels].sort(
(a, b) => estimateParamSize(b) - estimateParamSize(a),
),
);
const defaultTiers = $derived.by(() => {
const n = modelsBySize.length;
if (n === 0)
return {
opus: "your-model-id",
sonnet: "your-model-id",
haiku: "your-model-id",
};
if (n === 1)
return {
opus: modelsBySize[0],
sonnet: modelsBySize[0],
haiku: modelsBySize[0],
};
if (n === 2)
return {
opus: modelsBySize[0],
sonnet: modelsBySize[1],
haiku: modelsBySize[1],
};
return {
opus: modelsBySize[0],
sonnet: modelsBySize[Math.floor(n / 2)],
haiku: modelsBySize[n - 1],
};
});
let opusModel = $state("");
let sonnetModel = $state("");
let haikuModel = $state("");
$effect(() => {
opusModel = defaultTiers.opus;
sonnetModel = defaultTiers.sonnet;
haikuModel = defaultTiers.haiku;
});
let codexModel = $state("");
let codexMcpPath = $state("/Users/username");
let openClawModel = $state("");
let piModel = $state("");
$effect(() => {
const def = modelsBySize.length > 0 ? modelsBySize[0] : "your-model-id";
codexModel = def;
openClawModel = def;
piModel = def;
});
const claudeShellCommand = $derived(
[
`ANTHROPIC_BASE_URL=${apiUrl} \\`,
`ANTHROPIC_API_KEY=x \\`,
`ANTHROPIC_DEFAULT_OPUS_MODEL=${opusModel} \\`,
`ANTHROPIC_DEFAULT_SONNET_MODEL=${sonnetModel} \\`,
`ANTHROPIC_DEFAULT_HAIKU_MODEL=${haikuModel} \\`,
`API_TIMEOUT_MS=3000000 \\`,
`CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 \\`,
`claude`,
].join("\n"),
);
const claudeSettingsJson = $derived(
JSON.stringify(
{
env: {
ANTHROPIC_BASE_URL: apiUrl,
ANTHROPIC_API_KEY: "x",
ANTHROPIC_DEFAULT_OPUS_MODEL: opusModel,
ANTHROPIC_DEFAULT_SONNET_MODEL: sonnetModel,
ANTHROPIC_DEFAULT_HAIKU_MODEL: haikuModel,
API_TIMEOUT_MS: "3000000",
CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC: "1",
},
},
null,
2,
),
);
const openCodeConfig = $derived.by(() => {
const models: Record<string, Record<string, unknown>> = {};
for (const modelId of runningModels) {
const caps = modelCapabilities[modelId] || [];
const ctxLen = modelContextLengths[modelId] || 0;
const dialect = modelReasoningDialects[modelId];
const entry: Record<string, unknown> = { name: modelId };
if (ctxLen > 0) {
entry.limit = { context: ctxLen, output: Math.min(ctxLen, 16384) };
}
if (caps.includes("vision")) {
entry.modalities = { input: ["text", "image"], output: ["text"] };
}
// Reasoning round-trip: opencode's `interleaved` field tells the
// openai-compatible adapter to send the assistant's prior
// reasoning_content back in subsequent turns. Emit it for dialects
// whose chat templates use prior reasoning:
// - `tool_conditional` (DeepSeek V3.2 / V4): wrapper preserves all
// reasoning when tools are present.
// - `post_last_user` (Qwen3-Thinking, GLM 4.5+, MiniMax M2.x):
// Jinja template reads reasoning_content for assistant turns since
// the last user message — exactly the tool-chain window.
// - `channel` (gpt-oss / Harmony): the model's Jinja template reads
// `message.thinking` rather than `message.reasoning_content`, but
// the server bridges `reasoning_content` → `thinking` before
// rendering, so the round-trip works through the standard field.
// `suffix` (Kimi): reasoning lives in content; no separate field path.
if (
dialect === "tool_conditional" ||
dialect === "post_last_user" ||
dialect === "channel"
) {
entry.interleaved = { field: "reasoning_content" };
}
models[modelId] = entry;
}
if (Object.keys(models).length === 0) {
models["your-model-id"] = { name: "your-model-name" };
}
const firstModel =
runningModels.length > 0 ? runningModels[0] : "your-model-id";
return JSON.stringify(
{
$schema: "https://opencode.ai/config.json",
provider: {
exo: {
npm: "@ai-sdk/openai-compatible",
name: "exo",
options: {
baseURL: `${apiUrl}/v1`,
apiKey: "x",
},
models,
},
},
model: `exo/${firstModel}`,
},
null,
2,
);
});
const codexShellCommand = $derived(`EXO_API_KEY=x npx @openai/codex`);
const codexConfig = $derived(
[
`model = "${codexModel}"`,
`model_provider = "exo"`,
``,
`[model_providers.exo]`,
`name = "exo"`,
`base_url = "${apiUrl}/v1"`,
`env_key = "EXO_API_KEY"`,
``,
`[mcp_servers.filesystem]`,
`command = "npx"`,
`args = ["-y", "@modelcontextprotocol/server-filesystem", "${codexMcpPath}"]`,
].join("\n"),
);
const openClawConfig = $derived(
JSON.stringify(
{
gateway: { mode: "local" },
models: {
providers: {
exo: {
baseUrl: `${apiUrl}/v1`,
apiKey: "x",
api: "openai-completions",
models: [
{
id: openClawModel,
name: "exo local",
input: (modelCapabilities[openClawModel] || []).includes(
"vision",
)
? ["text", "image"]
: ["text"],
},
],
},
},
},
agents: {
defaults: {
model: `exo/${openClawModel}`,
},
},
},
null,
2,
),
);
const piModelsJson = $derived.by(() => {
const models: Record<string, unknown>[] = [];
for (const modelId of runningModels) {
const caps = modelCapabilities[modelId] || [];
const ctxLen = modelContextLengths[modelId] || 0;
const entry: Record<string, unknown> = { id: modelId };
if (caps.includes("vision")) {
entry.input = ["text", "image"];
}
// Mark thinking-capable models so pi surfaces its thinking-level selector
// for them. exo capability strings: "thinking" (model emits reasoning
// content) and "thinking_toggle" (user can turn it on/off).
if (caps.includes("thinking") || caps.includes("thinking_toggle")) {
entry.reasoning = true;
}
if (ctxLen > 0) {
entry.contextWindow = ctxLen;
}
models.push(entry);
}
if (models.length === 0) {
models.push({ id: "your-model-id" });
}
return JSON.stringify(
{
providers: {
exo: {
baseUrl: `${apiUrl}/v1`,
api: "openai-completions",
apiKey: "exo",
compat: {
supportsDeveloperRole: false,
// exo's OpenAI surface takes a boolean `enable_thinking` toggle,
// not graded effort levels, so disable pi's `reasoning_effort`
// parameter and use the matching top-level-boolean format.
supportsReasoningEffort: false,
thinkingFormat: "qwen",
},
models,
},
},
},
null,
2,
);
});
const piShellCommand = $derived(`pi --provider exo --model ${piModel}`);
const ollamaCommand = $derived(
`OLLAMA_HOST=${apiUrl}/ollama ollama run ${modelsBySize.length > 0 ? modelsBySize[0] : "your-model-id"}`,
);
const openWebUiCommand = $derived(
[
`docker run -d -p 3000:8080 \\`,
` -e OLLAMA_BASE_URL=${apiUrl.replace("localhost", "host.docker.internal")}/ollama \\`,
` -v open-webui:/app/backend/data \\`,
` --name open-webui \\`,
` ghcr.io/open-webui/open-webui:main`,
].join("\n"),
);
const n8nDockerCommand = $derived(
[
`docker run -d -p 5678:5678 \\`,
` -v n8n_data:/home/node/.n8n \\`,
` --name n8n \\`,
` docker.n8n.io/n8nio/n8n`,
].join("\n"),
);
const n8nCredentialSteps = $derived(
[
`1. Go to Credentials → Add Credential → search "OpenAI API"`,
`2. Set API Key to: x`,
`3. Set Base URL to: ${apiUrl.replace("127.0.0.1", "host.docker.internal").replace("localhost", "host.docker.internal")}/v1`,
`4. Save the credential`,
].join("\n"),
);
const n8nWorkflowSteps = $derived(
[
`1. Create a new workflow → "Start from Scratch"`,
`2. Add an "AI Agent" or "Basic LLM Chain" node`,
`3. Inside it, add an "OpenAI Chat Model" sub-node`,
`4. Select the OpenAI credential you just created`,
`5. Set Model to "From list" and pick your model (e.g. ${modelsBySize.length > 0 ? modelsBySize[0] : "your-model-id"})`,
`6. Optionally toggle "Use Responses API", add Built-in Tools, or click "Add Option" for sampling settings`,
`7. Connect a "Chat Trigger" node for interactive chat`,
`8. On the Chat Trigger, enable "Allow File Uploads" for vision`,
].join("\n"),
);
const firefoxConfig = $derived(
[
`1. Open about:config in Firefox`,
`2. Set browser.ml.chat.enabled to true`,
`3. Set browser.ml.chat.hideLocalhost to false`,
`4. Set browser.ml.chat.provider to: ${apiUrl}/`,
].join("\n"),
);
const tabs = [
"Claude Code",
"OpenCode",
"Codex",
"OpenClaw",
"Pi",
"Open WebUI",
"n8n",
"Firefox",
] as const;
type Tab = (typeof tabs)[number];
const stored = browser ? localStorage.getItem("exo-integrations-tab") : null;
let activeTab = $state<Tab>(
stored && tabs.includes(stored as Tab) ? (stored as Tab) : "Claude Code",
);
$effect(() => {
if (browser) localStorage.setItem("exo-integrations-tab", activeTab);
});
const selectClass =
"bg-black/30 border border-exo-light-gray/20 rounded px-2 py-1.5 text-white font-mono text-xs focus:border-exo-yellow/50 focus:outline-none appearance-none cursor-pointer";
onMount(async () => {
refreshState();
try {
const resp = await fetch("/v1/models");
const data = (await resp.json()) as {
data: {
id: string;
capabilities: string[];
context_length: number;
reasoning_dialect?: string;
}[];
};
const caps: Record<string, string[]> = {};
const ctxs: Record<string, number> = {};
const dialects: Record<string, string> = {};
for (const model of data.data) {
caps[model.id] = model.capabilities || [];
if (model.context_length > 0) ctxs[model.id] = model.context_length;
if (model.reasoning_dialect)
dialects[model.id] = model.reasoning_dialect;
}
modelCapabilities = caps;
modelContextLengths = ctxs;
modelReasoningDialects = dialects;
} catch {
/* ignore */
}
});
</script>
<div class="min-h-screen bg-exo-dark-gray flex flex-col">
<HeaderNav showHome={true} />
<main
class="flex-1 max-w-3xl mx-auto w-full px-4 md:px-6 py-8"
in:fade={{ duration: 200 }}
>
<div class="mb-8">
<h1
class="text-white text-xl md:text-2xl font-semibold tracking-wide mb-2"
>
Integrations
</h1>
<p class="text-exo-light-gray/60 text-sm">
Connect external tools to your exo cluster.
</p>
</div>
<!-- Status -->
<div class="mb-8">
<span class="text-exo-light-gray/70 text-xs uppercase tracking-wider"
>API Endpoint</span
>
<span class="text-white font-mono text-sm ml-2">{apiUrl}</span>
{#if runningModels.length > 0}
<div class="text-exo-light-gray/50 text-xs mt-2">
Running model{runningModels.length > 1 ? "s" : ""}:
<ul class="mt-1 space-y-0.5 list-none">
{#each runningModels as model}
<li class="text-exo-yellow font-mono">{model}</li>
{/each}
</ul>
</div>
{:else}
<p class="text-exo-light-gray/40 text-xs mt-2 italic">
No models currently running
</p>
{/if}
</div>
<!-- API Endpoints -->
<div class="mb-8">
<div
class="flex flex-col sm:flex-row gap-3 text-xs font-mono text-exo-light-gray/70"
>
<div
class="flex-1 bg-black/20 border border-exo-light-gray/10 rounded px-3 py-2"
>
<span class="text-exo-light-gray/40 text-[10px] uppercase block mb-1"
>OpenAI-compatible</span
>
<span class="text-white/80">{apiUrl}/v1</span>
</div>
<div
class="flex-1 bg-black/20 border border-exo-light-gray/10 rounded px-3 py-2"
>
<span class="text-exo-light-gray/40 text-[10px] uppercase block mb-1"
>Claude-compatible</span
>
<span class="text-white/80">{apiUrl}</span>
</div>
<div
class="flex-1 bg-black/20 border border-exo-light-gray/10 rounded px-3 py-2"
>
<span class="text-exo-light-gray/40 text-[10px] uppercase block mb-1"
>Ollama-compatible</span
>
<span class="text-white/80">{apiUrl}/ollama</span>
</div>
</div>
</div>
<!-- Tabs -->
<div
class="flex flex-wrap gap-2 mb-6 border-b border-exo-light-gray/10 pb-3"
>
{#each tabs as tab}
<button
onclick={() => (activeTab = tab)}
class="px-3 py-1.5 text-xs rounded-md transition-all cursor-pointer
{activeTab === tab
? 'bg-exo-yellow/15 text-exo-yellow border border-exo-yellow/30'
: 'text-exo-light-gray/60 hover:text-white/80 border border-transparent hover:border-exo-light-gray/20'}"
>
{tab}
</button>
{/each}
</div>
<!-- Tab Content -->
<div class="space-y-4">
{#if activeTab === "Claude Code"}
{#if runningModels.length > 1}
<div class="grid grid-cols-3 gap-3 text-xs">
{#each [{ label: "Opus", bind: () => opusModel, set: (v: string) => (opusModel = v) }, { label: "Sonnet", bind: () => sonnetModel, set: (v: string) => (sonnetModel = v) }, { label: "Haiku", bind: () => haikuModel, set: (v: string) => (haikuModel = v) }] as tier}
<div>
<span
class="text-exo-light-gray/50 text-[10px] uppercase tracking-wider block mb-1"
>{tier.label}</span
>
<select
value={tier.bind()}
onchange={(e) =>
tier.set((e.target as HTMLSelectElement).value)}
class="w-full {selectClass}"
>
{#each runningModels as model}
<option value={model}>{model.split("/").pop()}</option>
{/each}
</select>
</div>
{/each}
</div>
{/if}
<IntegrationCard
title="Shell Command"
subtitle="Run in terminal"
description="Launch Claude Code with exo as the backend. Paste this into your terminal."
config={claudeShellCommand}
language="bash"
/>
<IntegrationCard
title="Settings File"
subtitle="~/.claude/settings.json"
description="Or add this to your Claude Code settings for persistent configuration."
config={claudeSettingsJson}
/>
{:else if activeTab === "OpenCode"}
<IntegrationCard
title="Config File"
subtitle="opencode.json"
description="Add this to your project root or ~/.config/opencode/opencode.json for global config. Vision models automatically get image input modality."
config={openCodeConfig}
/>
{:else if activeTab === "Codex"}
<div class="flex gap-3 text-xs">
{#if runningModels.length > 1}
<div>
<span
class="text-exo-light-gray/50 text-[10px] uppercase tracking-wider block mb-1"
>Model</span
>
<select bind:value={codexModel} class={selectClass}>
{#each runningModels as model}
<option value={model}>{model.split("/").pop()}</option>
{/each}
</select>
</div>
{/if}
<div class="flex-1">
<span
class="text-exo-light-gray/50 text-[10px] uppercase tracking-wider block mb-1"
>MCP Filesystem Path</span
>
<input
type="text"
bind:value={codexMcpPath}
class="w-full bg-black/30 border border-exo-light-gray/20 rounded px-2 py-1.5 text-white font-mono text-xs focus:border-exo-yellow/50 focus:outline-none"
/>
</div>
</div>
<IntegrationCard
title="Config File"
subtitle="~/.codex/config.toml"
description="Add this to your Codex CLI config so the model and provider persist."
config={codexConfig}
/>
<IntegrationCard
title="Shell Command"
subtitle="Run in terminal"
description="Launch Codex with exo as the backend."
config={codexShellCommand}
language="bash"
/>
{:else if activeTab === "OpenClaw"}
{#if runningModels.length > 1}
<div class="text-xs">
<span
class="text-exo-light-gray/50 text-[10px] uppercase tracking-wider block mb-1"
>Model</span
>
<select bind:value={openClawModel} class={selectClass}>
{#each runningModels as model}
<option value={model}>{model.split("/").pop()}</option>
{/each}
</select>
</div>
{/if}
<IntegrationCard
title="Config File"
subtitle="~/.openclaw/openclaw.json"
description="Add this to your OpenClaw config. If you haven't installed OpenClaw yet, run: npm install -g openclaw@latest"
config={openClawConfig}
/>
<IntegrationCard
title="Setup Commands"
subtitle="Run in terminal"
description="After saving the config, run these commands to fix metadata and start the gateway."
config={`openclaw doctor --fix${(modelCapabilities[openClawModel] || []).includes("vision") ? `\nopenclaw models set-image exo/${openClawModel}` : ""}\nopenclaw gateway &\nopenclaw dashboard`}
language="bash"
/>
{:else if activeTab === "Pi"}
{#if runningModels.length > 1}
<div class="text-xs">
<span
class="text-exo-light-gray/50 text-[10px] uppercase tracking-wider block mb-1"
>Model</span
>
<select bind:value={piModel} class={selectClass}>
{#each runningModels as model}
<option value={model}>{model.split("/").pop()}</option>
{/each}
</select>
</div>
{/if}
<IntegrationCard
title="Models Config"
subtitle="~/.pi/agent/models.json"
description="Register exo as a custom provider in pi. Create or edit this file, then run pi and pick an exo model via /model. Install pi with: npm install -g @mariozechner/pi-coding-agent"
config={piModelsJson}
/>
<IntegrationCard
title="Shell Command"
subtitle="Run in terminal"
description="Launch pi directly with the exo provider and model selected."
config={piShellCommand}
language="bash"
/>
{:else if activeTab === "Open WebUI"}
<IntegrationCard
title="1. Start Open WebUI"
subtitle="Run in terminal"
description="Run this to start Open WebUI."
config={openWebUiCommand}
language="bash"
/>
<IntegrationCard
title="2. Open & Select Model"
subtitle="http://localhost:3000"
description={`Open http://localhost:3000 in your browser. Select the running model from the dropdown at the top: ${runningModels.length > 0 ? runningModels.join(", ") : "no models running"}`}
config={"open http://localhost:3000"}
language="bash"
/>
<IntegrationCard
title="Ollama CLI"
subtitle="Run in terminal"
description="Or use the Ollama CLI directly."
config={ollamaCommand}
language="bash"
/>
{:else if activeTab === "n8n"}
<IntegrationCard
title="1. Start n8n"
subtitle="Run in terminal"
description="Start n8n with Docker. If you already have n8n running, skip this step."
config={n8nDockerCommand}
language="bash"
/>
<IntegrationCard
title="2. Open n8n"
subtitle="http://localhost:5678"
description="Open n8n in your browser. If this is your first time, complete the setup and select 'Start from Scratch' when prompted."
config={"open http://localhost:5678"}
language="bash"
/>
<IntegrationCard
title="3. Add OpenAI Credential"
subtitle="n8n UI → Credentials"
description="Create an OpenAI credential pointing at your exo cluster."
config={n8nCredentialSteps}
/>
<IntegrationCard
title="4. Build a Workflow"
subtitle="n8n UI → Workflows"
description="Create a workflow that uses your exo-powered model."
config={n8nWorkflowSteps}
/>
{:else if activeTab === "Firefox"}
<IntegrationCard
title="Firefox AI Chatbot"
subtitle="about:config"
description="Use the exo dashboard as Firefox's built-in AI chatbot. Requires Firefox 130+."
config={firefoxConfig}
/>
{/if}
</div>
</main>
</div>
+1 -1
View File
@@ -81,4 +81,4 @@ Whenever a device produces side effects, it captures those side effects in an `E
## Purity
A significant goal of the current design is to make data flow explicit. Classes should either represent simple data (`CamelCaseModel`s typically, and `TaggedModel`s for unions) or active `System`s (Erlang `Actor`s), with all transformations of that data being "referentially transparent" - destructure and construct new data, don't mutate in place. We have had varying degrees of success with this, and are still exploring where purity makes sense.
A significant goal of the current design is to make data flow explicit. Classes should either represent simple data (`FrozenModel`s typically, and `TaggedModel`s for unions) or active `System`s (Erlang `Actor`s), with all transformations of that data being "referentially transparent" - destructure and construct new data, don't mutate in place. We have had varying degrees of success with this, and are still exploring where purity makes sense.
Generated
+49 -45
View File
@@ -2,11 +2,11 @@
"nodes": {
"crane": {
"locked": {
"lastModified": 1767744144,
"narHash": "sha256-9/9ntI0D+HbN4G0TrK3KmHbTvwgswz7p8IEJsWyef8Q=",
"lastModified": 1775790182,
"narHash": "sha256-pG2RWVQY0Pe+rmmXJx+Jpyi+JcgjWzS18m7fcD1B64Q=",
"owner": "ipetkov",
"repo": "crane",
"rev": "2fb033290bf6b23f226d4c8b32f7f7a16b043d7e",
"rev": "534982f1c41834b101e381b07b1121a4f065a374",
"type": "github"
},
"original": {
@@ -47,11 +47,11 @@
"rust-analyzer-src": "rust-analyzer-src"
},
"locked": {
"lastModified": 1768287139,
"narHash": "sha256-nsXFt0OzUi6K7dUzzJD5/v9e0Ic+fvclfIW936/43ZM=",
"lastModified": 1775807984,
"narHash": "sha256-Redoe3D9zGN5I9QPHWL9vfMVQBehY1fKsMiRXQ83X3w=",
"owner": "nix-community",
"repo": "fenix",
"rev": "a4a3aa956931f90f35453cb519e4545e9ad7f773",
"rev": "fcf90c0c4d368b2ca917a7afa6d08e98a397e5fd",
"type": "github"
},
"original": {
@@ -83,11 +83,11 @@
]
},
"locked": {
"lastModified": 1768135262,
"narHash": "sha256-PVvu7OqHBGWN16zSi6tEmPwwHQ4rLPU9Plvs8/1TUBY=",
"lastModified": 1775087534,
"narHash": "sha256-91qqW8lhL7TLwgQWijoGBbiD4t7/q75KTi8NxjVmSmA=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "80daad04eddbbf5a4d883996a73f3f542fa437ac",
"rev": "3107b77cd68437b9a76194f0f7f9c55f2329ca5b",
"type": "github"
},
"original": {
@@ -96,38 +96,42 @@
"type": "github"
}
},
"nixglhost": {
"inputs": {
"nixpkgs": [
"nixpkgs"
]
},
"locked": {
"lastModified": 1732211616,
"narHash": "sha256-QZCKJoypcwgS3tDNSWMjlxEBZtOYPW3eXV24rMzKsac=",
"owner": "numtide",
"repo": "nix-gl-host",
"rev": "5269b233f83880a0b433eafe026f0bc0d8f1a4a9",
"type": "github"
},
"original": {
"owner": "numtide",
"repo": "nix-gl-host",
"type": "github"
}
},
"nixpkgs": {
"locked": {
"lastModified": 1768127708,
"narHash": "sha256-1Sm77VfZh3mU0F5OqKABNLWxOuDeHIlcFjsXeeiPazs=",
"lastModified": 1775595990,
"narHash": "sha256-OEf7YqhF9IjJFYZJyuhAypgU+VsRB5lD4DuiMws5Ltc=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "ffbc9f8cbaacfb331b6017d5a5abb21a492c9a38",
"rev": "4e92bbcdb030f3b4782be4751dc08e6b6cb6ccf2",
"type": "github"
},
"original": {
"owner": "NixOS",
"ref": "nixos-unstable",
"ref": "nixos-25.11",
"repo": "nixpkgs",
"type": "github"
}
},
"nixpkgs-swift": {
"locked": {
"lastModified": 1761672384,
"narHash": "sha256-o9KF3DJL7g7iYMZq9SWgfS1BFlNbsm6xplRjVlOCkXI=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "08dacfca559e1d7da38f3cf05f1f45ee9bfd213c",
"type": "github"
},
"original": {
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "08dacfca559e1d7da38f3cf05f1f45ee9bfd213c",
"type": "github"
}
},
"purescript-overlay": {
"inputs": {
"flake-compat": "flake-compat",
@@ -164,11 +168,11 @@
]
},
"locked": {
"lastModified": 1763662255,
"narHash": "sha256-4bocaOyLa3AfiS8KrWjZQYu+IAta05u3gYZzZ6zXbT0=",
"lastModified": 1773870109,
"narHash": "sha256-ZoTdqZP03DcdoyxvpFHCAek4bkPUTUPUF3oCCgc3dP4=",
"owner": "pyproject-nix",
"repo": "build-system-pkgs",
"rev": "042904167604c681a090c07eb6967b4dd4dae88c",
"rev": "b6e74f433b02fa4b8a7965ee24680f4867e2926f",
"type": "github"
},
"original": {
@@ -184,11 +188,11 @@
]
},
"locked": {
"lastModified": 1764134915,
"narHash": "sha256-xaKvtPx6YAnA3HQVp5LwyYG1MaN4LLehpQI8xEdBvBY=",
"lastModified": 1775439158,
"narHash": "sha256-NHY9SJNU019n+8NCabBDtmuzRFeE2gZlYKHowp9bV24=",
"owner": "pyproject-nix",
"repo": "pyproject.nix",
"rev": "2c8df1383b32e5443c921f61224b198a2282a657",
"rev": "fb6b728260f3f32761367e9fd1e1a25b4245bcd0",
"type": "github"
},
"original": {
@@ -203,8 +207,8 @@
"dream2nix": "dream2nix",
"fenix": "fenix",
"flake-parts": "flake-parts",
"nixglhost": "nixglhost",
"nixpkgs": "nixpkgs",
"nixpkgs-swift": "nixpkgs-swift",
"pyproject-build-systems": "pyproject-build-systems",
"pyproject-nix": "pyproject-nix",
"treefmt-nix": "treefmt-nix",
@@ -214,11 +218,11 @@
"rust-analyzer-src": {
"flake": false,
"locked": {
"lastModified": 1768224240,
"narHash": "sha256-Pp1dDrXKPBUJReZnnDElFyHYn67XTd48zRhToheLjtk=",
"lastModified": 1775745684,
"narHash": "sha256-8MbfLwd60FNa8dRFkjE+G3TT/x21G3Rsplm1bMBQUtU=",
"owner": "rust-lang",
"repo": "rust-analyzer",
"rev": "725349602e525df37f377701e001fe8aab807878",
"rev": "64ddb549bc9a70d011328746fa46a8883f937b6b",
"type": "github"
},
"original": {
@@ -257,11 +261,11 @@
]
},
"locked": {
"lastModified": 1768158989,
"narHash": "sha256-67vyT1+xClLldnumAzCTBvU0jLZ1YBcf4vANRWP3+Ak=",
"lastModified": 1775636079,
"narHash": "sha256-pc20NRoMdiar8oPQceQT47UUZMBTiMdUuWrYu2obUP0=",
"owner": "numtide",
"repo": "treefmt-nix",
"rev": "e96d59dff5c0d7fddb9d113ba108f03c3ef99eca",
"rev": "790751ff7fd3801feeaf96d7dc416a8d581265ba",
"type": "github"
},
"original": {
@@ -280,11 +284,11 @@
]
},
"locked": {
"lastModified": 1767701098,
"narHash": "sha256-CJhKZnWb3gumR9oTRjFvCg/6lYTGbZRU7xtvcyWIRwU=",
"lastModified": 1775706324,
"narHash": "sha256-BTb4sydzX2B5/oNbvCdQFeSbk97xEnbb8bk84CiKCOs=",
"owner": "pyproject-nix",
"repo": "uv2nix",
"rev": "9d357f0d2ce6f5f35ec7959d7e704452352eb4da",
"rev": "5707df99097375896a3dda811d492a2fabe63500",
"type": "github"
},
"original": {
+37 -44
View File
@@ -2,7 +2,7 @@
description = "The development environment for Exo";
inputs = {
nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
nixpkgs.url = "github:NixOS/nixpkgs/nixos-25.11";
flake-parts = {
url = "github:hercules-ci/flake-parts";
@@ -46,17 +46,18 @@
inputs.nixpkgs.follows = "nixpkgs";
};
# Pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs)
nixpkgs-swift.url = "github:NixOS/nixpkgs/08dacfca559e1d7da38f3cf05f1f45ee9bfd213c";
nixglhost = {
url = "github:numtide/nix-gl-host";
inputs.nixpkgs.follows = "nixpkgs";
};
};
nixConfig = {
extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI=";
extra-substituters = "https://exo.cachix.org";
extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI= cache.nixos-cuda.org:74DUi4Ye579gUqzH4ziL9IyiJBlDpMRn9MBN8oNan9M=";
extra-substituters = "https://exo.cachix.org https://cache.nixos-cuda.org";
};
outputs =
inputs:
outputs = inputs:
inputs.flake-parts.lib.mkFlake { inherit inputs; } {
systems = [
"x86_64-linux"
@@ -71,32 +72,38 @@
./python/parts.nix
];
perSystem =
{ config, self', pkgs, lib, system, ... }:
debug = true; # Enable options autocompletion
perSystem = { config, self', pkgs, lib, system, ... }:
let
# Use pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs)
pkgsSwift = import inputs.nixpkgs-swift { inherit system; };
in
{
# Allow unfree for metal-toolchain (needed for Darwin Metal packages)
_module.args.pkgs = import inputs.nixpkgs {
pkgsArgs = {
inherit system;
config.allowUnfreePredicate = pkg: (pkg.pname or "") == "metal-toolchain";
overlays = [
inputs.nixglhost.overlays.default
(import ./nix/apple-sdk-overlay.nix)
(final: prev: {
macmon = prev.macmon.overrideAttrs (_: {
(final: _: {
macmon = final.rustPlatform.buildRustPackage {
pname = "macmon";
version = "git";
src = final.fetchFromGitHub {
owner = "swiftraccoon";
owner = "vladkens";
repo = "macmon";
rev = "9154d234f763fbeffdcb4135d0bbbaf80609699b";
hash = "sha256-CwhilKNbs5XL9/tF5DMwyPBlE/hpmjGNTuxQ36sM50M=";
rev = "a1cd06b6cc0d5e61db24fd8832e74cd992097a7d";
hash = "sha256-wcq4PUXK44XfUKOZKl32u8LpOxXpSbUUfItQGwS2Zso=";
};
});
cargoHash = "sha256-Epj3L+db1flGNK5y6yfSig8piEiXTz15lPo/FNkqlkA=";
};
})
];
};
in
{
# Allow unfree for metal-toolchain (needed for Darwin Metal packages)
_module.args = {
pkgs = import inputs.nixpkgs pkgsArgs;
unfreePkgs = import inputs.nixpkgs (pkgsArgs // { config.allowUnfree = true; });
};
treefmt = {
projectRootFile = "flake.nix";
programs = {
@@ -116,29 +123,19 @@
};
swift-format = {
enable = true;
package = pkgsSwift.swiftPackages.swift-format;
package = pkgs.swiftPackages.swift-format;
};
shfmt.enable = true;
taplo.enable = true;
};
};
packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin (
let
uvLock = builtins.fromTOML (builtins.readFile ./uv.lock);
mlxPackage = builtins.head (builtins.filter (p: p.name == "mlx" && p.source ? git) uvLock.package);
uvLockMlxVersion = mlxPackage.version;
uvLockMlxRev = builtins.elemAt (builtins.split "#" mlxPackage.source.git) 2;
in
{
metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { };
mlx = pkgs.callPackage ./nix/mlx.nix {
inherit (self'.packages) metal-toolchain;
inherit uvLockMlxVersion uvLockMlxRev;
};
default = self'.packages.exo;
}
);
packages = {
default = self'.packages.exo;
} //
lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin {
metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { };
};
devShells.default = with pkgs; pkgs.mkShell {
inputsFrom = [ self'.checks.cargo-build ];
@@ -149,16 +146,15 @@
config.treefmt.build.wrapper
# PYTHON
python313
self'.packages.editableVenv
uv
ruff
basedpyright
# RUST
config.rust.toolchain
maturin
# NIX
nixd
nixpkgs-fmt
# SVELTE
@@ -168,9 +164,6 @@
just
jq
]
++ lib.optionals stdenv.isLinux [
unixtools.ifconfig
]
++ lib.optionals stdenv.isDarwin [
macmon
];
+6 -4
View File
@@ -22,19 +22,21 @@ sync-clean:
uv sync --all-packages --force-reinstall --no-cache
rust-rebuild:
cargo run --bin stub_gen
PYO3_PYTHON="$(uv run python -c 'import sys; print(sys.executable)')" cargo run --bin stub_gen
uv sync --reinstall-package exo_pyo3_bindings
build-dashboard:
#!/usr/bin/env bash
cd dashboard
pushd dashboard
npm install
npm run build
popd
package:
package: build-dashboard
uv run pyinstaller packaging/pyinstaller/exo.spec
rm -rf build
build-app: package
build-app: rust-rebuild sync-clean package
xcodebuild build -project app/EXO/EXO.xcodeproj -scheme EXO -configuration Debug -derivedDataPath app/EXO/build
@echo "\nBuild complete. Run with:\n open {{justfile_directory()}}/app/EXO/build/Build/Products/Debug/EXO.app"
+1 -1
View File
@@ -59,7 +59,7 @@ index 262b0495..5c7446ad 100644
add_custom_command(
OUTPUT ${MLX_METAL_PATH}/mlx.metallib
- COMMAND xcrun -sdk macosx metallib ${KERNEL_AIR} -o
- COMMAND xcrun -sdk macosx metal ${KERNEL_AIR} -o
+ COMMAND metallib ${KERNEL_AIR} -o
${MLX_METAL_PATH}/mlx.metallib
DEPENDS ${KERNEL_AIR}
-158
View File
@@ -1,158 +0,0 @@
{ stdenv
, lib
, fetchFromGitHub
, replaceVars
, fetchzip
, cmake
, nlohmann_json
, apple-sdk_26
, metal-toolchain
, runCommand
, fmt
, python313Packages
, uvLockMlxVersion
, uvLockMlxRev
}:
assert stdenv.isDarwin;
let
python = python313Packages.python;
# Static dependencies included directly during compilation
gguf-tools = fetchFromGitHub {
owner = "antirez";
repo = "gguf-tools";
rev = "8fa6eb65236618e28fd7710a0fba565f7faa1848";
hash = "sha256-15FvyPOFqTOr5vdWQoPnZz+mYH919++EtghjozDlnSA=";
};
metal_cpp = fetchzip {
url = "https://developer.apple.com/metal/cpp/files/metal-cpp_26.zip";
hash = "sha256-7n2eI2lw/S+Us6l7YPAATKwcIbRRpaQ8VmES7S8ZjY8=";
};
nanobind = fetchFromGitHub {
owner = "wjakob";
repo = "nanobind";
rev = "v2.10.2";
hash = "sha256-io44YhN+VpfHFWyvvLWSanRgbzA0whK8WlDNRi3hahU=";
fetchSubmodules = true;
};
mlx = stdenv.mkDerivation rec {
pname = "mlx";
version = uvLockMlxVersion;
pyproject = true;
src = fetchFromGitHub {
owner = "rltakashige";
repo = "mlx-jaccl-fix-small-recv";
rev = uvLockMlxRev;
hash = "sha256-GosFIWxIB48Egb1MqJrR3xhsUsQeWdRk5rV93USY6wQ=";
};
patches = [
(replaceVars ./darwin-build-fixes.patch {
sdkVersion = apple-sdk_26.version;
metalVersion = metal-toolchain.metalVersion;
})
];
postPatch = ''
substituteInPlace mlx/backend/cpu/jit_compiler.cpp \
--replace-fail "g++" "$CXX"
'';
dontUseCmakeConfigure = true;
enableParallelBuilding = true;
# Allows multiple cores to be used in Python builds.
postUnpack = ''
export MAKEFLAGS+="''${enableParallelBuilding:+-j$NIX_BUILD_CORES}"
'';
# Updates the wrong fetcher rev attribute
passthru.skipBulkUpdate = true;
env = {
DEV_RELEASE = 1;
CMAKE_ARGS = toString [
(lib.cmakeBool "USE_SYSTEM_FMT" true)
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_GGUFLIB" "${gguf-tools}")
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_JSON" "${nlohmann_json.src}")
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_NANOBIND" "${nanobind}")
(lib.cmakeBool "FETCHCONTENT_FULLY_DISCONNECTED" true)
(lib.cmakeBool "MLX_BUILD_CPU" true)
(lib.cmakeBool "MLX_BUILD_METAL" true)
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_METAL_CPP" "${metal_cpp}")
(lib.cmakeOptionType "string" "CMAKE_OSX_DEPLOYMENT_TARGET" "${apple-sdk_26.version}")
(lib.cmakeOptionType "filepath" "CMAKE_OSX_SYSROOT" "${apple-sdk_26.passthru.sdkroot}")
];
SDKROOT = apple-sdk_26.passthru.sdkroot;
MACOSX_DEPLOYMENT_TARGET = apple-sdk_26.version;
};
build-system = [
python313Packages.setuptools
];
nativeBuildInputs = [
cmake
metal-toolchain
python313Packages.pypaBuildHook
python313Packages.pypaInstallHook
python313Packages.setuptools
python313Packages.typing-extensions
python313Packages.wheel
python313Packages.cmake
python313Packages.ninja
];
buildInputs = [
fmt
gguf-tools
python313Packages.nanobind
python313Packages.pybind11
apple-sdk_26
];
# Tests require Metal GPU access which isn't available in the Nix sandbox.
# To run tests, build with: nix build --option sandbox false .#mlx.passthru.tests.mlxTest
doCheck = false;
pythonImportsCheck = [ "mlx" ];
passthru.tests = {
# Runs example scripts to verify MLX works. Requires --option sandbox false
# since Metal GPU access is needed.
mlxTest =
runCommand "run-mlx-examples"
{
buildInputs = [ mlx ];
nativeBuildInputs = [ python ];
}
''
cp ${src}/examples/python/logistic_regression.py .
${python.interpreter} logistic_regression.py
rm logistic_regression.py
cp ${src}/examples/python/linear_regression.py .
${python.interpreter} linear_regression.py
rm linear_regression.py
touch $out
'';
};
meta = {
homepage = "https://github.com/ml-explore/mlx";
description = "Array framework for Apple silicon";
changelog = "https://github.com/ml-explore/mlx/releases/tag/${src.tag}";
license = lib.licenses.mit;
platforms = [ "aarch64-darwin" ];
};
};
in
mlx
+6
View File
@@ -0,0 +1,6 @@
{
"name": "exo",
"lockfileVersion": 3,
"requires": true,
"packages": {}
}
+12 -9
View File
@@ -1,5 +1,6 @@
# -*- mode: python ; coding: utf-8 -*-
import sys
import importlib.util
import shutil
from pathlib import Path
@@ -56,6 +57,7 @@ HIDDEN_IMPORTS = sorted(
set(
collect_submodules("mlx")
+ _safe_collect("mlx_lm")
+ _safe_collect("mlx_vlm")
+ _safe_collect("transformers")
)
)
@@ -67,18 +69,19 @@ DATAS: list[tuple[str, str]] = [
(str(EXO_SHARED_MODELS_DIR), "exo/shared/models"),
]
MACMON_PATH = shutil.which("macmon")
if MACMON_PATH is None:
raise SystemExit(
"macmon binary not found in PATH. "
"Install the pinned fork used by exo via: "
"cargo install --git https://github.com/swiftraccoon/macmon "
"--rev 9154d234f763fbeffdcb4135d0bbbaf80609699b macmon --force"
)
if sys.platform == "darwin":
MACMON_PATH = shutil.which("macmon")
if MACMON_PATH is None:
raise SystemExit(
"macmon binary not found in PATH. "
"Install the pinned fork used by exo via: "
"cargo install --git https://github.com/vladkens/macmon "
"--rev a1cd06b6cc0d5e61db24fd8832e74cd992097a7d macmon --force"
)
BINARIES: list[tuple[str, str]] = [
(MACMON_PATH, "."),
]
] if sys.platform == "darwin" else []
a = Analysis(
[str(ENTRYPOINT)],
+104 -19
View File
@@ -1,9 +1,9 @@
[project]
name = "exo"
version = "0.3.68"
version = "0.3.70"
description = "Exo"
readme = "README.md"
requires-python = ">=3.13"
requires-python = "==3.13.*"
dependencies = [
"aiofiles>=24.1.0",
"aiohttp>=3.12.14",
@@ -12,23 +12,24 @@ dependencies = [
"fastapi>=0.116.1",
"filelock>=3.18.0",
"rustworkx>=0.17.1",
"huggingface-hub>=0.33.4",
"huggingface-hub>=1.8.0",
"psutil>=7.0.0",
"loguru>=0.7.3",
"exo_pyo3_bindings", # rust bindings
"exo-pyo3-bindings", # rust bindings
"anyio==4.11.0",
"mlx; sys_platform == 'darwin'",
"mlx[cpu]==0.30.6; sys_platform == 'linux'",
"mlx-lm",
"mlx==0.31.2; sys_platform == 'darwin'",
"mlx-lm; sys_platform=='darwin'",
"tiktoken>=0.12.0", # required for kimi k2 tokenizer
"hypercorn>=0.18.0",
"openai-harmony>=0.0.8",
"httpx>=0.28.1",
"tomlkit>=0.14.0",
"mflux==0.17.2",
"mflux==0.17.2; sys_platform == 'darwin'",
"python-multipart>=0.0.21",
"msgspec>=0.19.0",
"zstandard>=0.23.0",
"mlx-vlm>=0.3.11; sys_platform == 'darwin'",
"transformers>=5.6.2",
]
[project.scripts]
@@ -45,11 +46,29 @@ dev = [
"ruff>=0.11.13",
]
# mlx[cuda] requires a newer version of mlx. the ideal on linux is: default to mlx[cpu] unless[cuda] specified.
[project.optional-dependencies]
# cuda = [
# "mlx[cuda]==0.26.3",
# ]
build = ["nanobind"]
cpu = [
"mlx==0.31.1; sys_platform == 'linux'",
"mlx-cpu==0.31.1; sys_platform == 'linux'",
"mlx-lm; sys_platform == 'linux'",
"mlx-vlm>=0.3.11; sys_platform== 'linux'",
"torch>=2.10.0; sys_platform == 'linux'",
]
cuda12 = [
"mlx==0.31.1; sys_platform == 'linux'",
"mlx-cuda-12==0.31.1; sys_platform == 'linux'",
"mlx-lm; sys_platform == 'linux'",
"mlx-vlm>=0.3.11; sys_platform== 'linux'",
"torch>=2.10.0; sys_platform == 'linux'",
]
cuda13 = [
"mlx==0.31.1; sys_platform == 'linux'",
"mlx-cuda-13==0.31.1; sys_platform == 'linux'",
"mlx-lm; sys_platform == 'linux'",
"mlx-vlm>=0.3.11; sys_platform== 'linux'",
"torch>=2.10.0; sys_platform == 'linux'",
]
###
# workspace configuration
@@ -59,12 +78,30 @@ dev = [
members = ["rust/exo_pyo3_bindings", "bench"]
[tool.uv.sources]
exo_pyo3_bindings = { workspace = true }
exo-pyo3-bindings = { workspace = true }
mlx = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git", branch = "address-rdma-gpu-locks", marker = "sys_platform == 'darwin'" }
mlx-lm = { git = "https://github.com/rltakashige/mlx-lm", branch = "leo/fix-deepseek-v32-indexer" }
# Uncomment to use local mlx/mlx-lm development versions:
# mlx = { path = "/Users/Shared/mlx", editable=true }
# mlx-lm = { path = "/Users/Shared/mlx-lm", editable=true }
mlx-lm = { git = "https://github.com/rltakashige/mlx-lm", branch = "leo/deepseek-v4" }
torch = [
{ index = "pytorch-cu130", marker = "sys_platform == 'linux' and extra == 'cuda13' and extra != 'cpu' and extra != 'cuda12'" },
{ index = "pytorch-cu120", marker = "sys_platform == 'linux' and extra == 'cuda12' and extra != 'cpu' and extra != 'cuda13'" },
{ index = "pytorch-cpu", marker = "(extra != 'cuda12' and extra != 'cuda13' and sys_platform == 'linux') or sys_platform == 'darwin'" },
]
vllm = { git = "https://github.com/hmellor/vllm.git", branch = "transformers-v5" }
[[tool.uv.index]]
name = "pytorch-cu130"
url = "https://download.pytorch.org/whl/cu130"
explicit = true
[[tool.uv.index]]
name = "pytorch-cu120"
url = "https://download.pytorch.org/whl/cu120"
explicit = true
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true
[build-system]
requires = ["uv_build>=0.8.9,<0.9.0"]
@@ -75,7 +112,7 @@ build-backend = "uv_build"
###
[tool.basedpyright]
include = [".venv/lib/mlx", ".venv/lib/mlx_lm", "src", "bench"]
include = ["src", "bench"]
typeCheckingMode = "strict"
failOnWarnings = true
@@ -102,9 +139,14 @@ exclude = [
]
stubPath = ".mlx_typings"
[[tool.basedpyright.executionEnvironments]]
root = "src/exo/worker/engines/image"
reportMissingModuleSource = false
[[tool.basedpyright.executionEnvironments]]
root = "src"
###
# uv configuration
###
@@ -114,6 +156,50 @@ root = "src"
required-version = ">=0.8.6"
prerelease = "allow"
environments = ["sys_platform == 'darwin'", "sys_platform == 'linux'"]
conflicts = [[{ extra = "cuda12" }, { extra = "cuda13" }, { extra = "cpu" }]]
constraint-dependencies = ["transformers>=5.6.2"]
override-dependencies = [
"mlx==0.31.1; sys_platform=='linux'",
"mlx; sys_platform=='darwin'",
]
[tool.uv.extra-build-dependencies]
miniaudio = ["setuptools", "cffi", "pycparser"]
mlx = [
"setuptools",
"typing-extensions",
"nanobind",
"pybind11",
"wheel",
"cmake",
"ninja",
]
mlx-lm = ["setuptools"]
xgrammar = [
"nanobind",
"setuptools",
"scikit-build-core",
"packaging",
"pathspec",
]
rouge-score = ["setuptools"]
sacrebleu = ["setuptools"]
sqlitedict = ["setuptools"]
word2number = ["setuptools"]
vllm = [
"setuptools",
"setuptools-scm",
"scikit-build-core",
"jinja2",
"wheel",
"markupsafe",
"typing-extensions",
"torch",
]
fastsafetensors = ["setuptools", "pybind11"]
torch = ["typing-extensions"]
torchvision = ["torch"]
torchaudio = ["torch"]
###
# ruff configuration
@@ -121,7 +207,6 @@ environments = ["sys_platform == 'darwin'", "sys_platform == 'linux'"]
[tool.ruff]
extend-exclude = [
"shared/protobufs/**",
"*mlx_typings/**",
"rust/exo_pyo3_bindings/**",
"bench/vendor/**",
+179 -146
View File
@@ -1,18 +1,36 @@
{ inputs, ... }:
{
perSystem =
{ config, self', pkgs, lib, system, ... }:
let
# Load workspace from uv.lock
workspace = inputs.uv2nix.lib.workspace.loadWorkspace {
workspaceRoot = ../.;
};
mkPythonSet = { pkgs, lib, self', members }:
let
# Load workspace from uv.lock
workspace = inputs.uv2nix.lib.workspace.loadWorkspace {
workspaceRoot = inputs.self;
};
# Create overlay from workspace
# Use wheels from PyPI for most packages; we override mlx with our pure Nix Metal build
overlay = workspace.mkPyprojectOverlay { sourcePreference = "wheel"; };
# Override overlay to inject Nix-built components
inherit (pkgs.stdenv.hostPlatform) isLinux isDarwin isx86_64;
inherit (pkgs.config) cudaSupport;
inherit (pkgs) cudaPackages;
cuda13Support = cudaSupport && cudaPackages.cudaMajorVersion == "13";
libmlx_source = if cuda13Support then "mlx-cuda-13" else if cudaSupport then "mlx-cuda-12" else "mlx-cpu";
python = pkgs.python313;
cudaLibs = with cudaPackages; [
cuda_cudart
cuda_cccl
cuda_cupti
cuda_nvrtc
cuda_nvtx
cudnn
libcufile
libcublas
libcufft
libcurand
libcusolver
libcusparse
libcusparse_lt
libnvjitlink
libnvshmem
nccl
];
exoOverlay = final: prev: {
# Replace workspace exo_pyo3_bindings with Nix-built wheel.
# Preserve passthru so mkVirtualEnv can resolve dependency groups.
@@ -32,126 +50,162 @@
'';
};
};
buildSystemsOverlay = final: prev:
lib.optionalAttrs isDarwin
{
mlx = prev.mlx.overrideAttrs (old:
let
# Static dependencies included directly during compilation
gguf-tools = pkgs.fetchFromGitHub {
owner = "antirez";
repo = "gguf-tools";
rev = "8fa6eb65236618e28fd7710a0fba565f7faa1848";
hash = "sha256-15FvyPOFqTOr5vdWQoPnZz+mYH919++EtghjozDlnSA=";
};
python = pkgs.python313;
metal_cpp = pkgs.fetchzip {
url = "https://developer.apple.com/metal/cpp/files/metal-cpp_26.zip";
hash = "sha256-7n2eI2lw/S+Us6l7YPAATKwcIbRRpaQ8VmES7S8ZjY8=";
};
# Overlay to provide build systems and custom packages
buildSystemsOverlay = final: prev: {
# mlx-lm is a git dependency that needs setuptools
mlx-lm = prev.mlx-lm.overrideAttrs (old: {
nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
final.setuptools
];
});
# rouge-score and sacrebleu don't declare setuptools as a build dependency
rouge-score = prev.rouge-score.overrideAttrs (old: {
nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
final.setuptools
];
});
sacrebleu = prev.sacrebleu.overrideAttrs (old: {
nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
final.setuptools
];
});
sqlitedict = prev.sqlitedict.overrideAttrs (old: {
nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
final.setuptools
];
});
word2number = prev.word2number.overrideAttrs (old: {
nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [
final.setuptools
];
});
} // lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin {
# Use our pure Nix-built MLX with Metal support (macOS only)
mlx = self'.packages.mlx;
nanobind = pkgs.fetchFromGitHub {
owner = "wjakob";
repo = "nanobind";
rev = "v2.10.2";
hash = "sha256-io44YhN+VpfHFWyvvLWSanRgbzA0whK8WlDNRi3hahU=";
fetchSubmodules = true;
};
in
{
nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [ pkgs.cmake self'.packages.metal-toolchain ];
# TODO: non-sdk_26 support
buildInputs = (old.buildInputs or [ ])
++ [ gguf-tools pkgs.fmt pkgs.nlohmann_json pkgs.apple-sdk_26 ];
patches = [
(pkgs.replaceVars ../nix/darwin-build-fixes.patch {
sdkVersion = pkgs.apple-sdk_26.version;
inherit (self'.packages.metal-toolchain) metalVersion;
})
];
postPatch = ''
substituteInPlace mlx/backend/cpu/jit_compiler.cpp \
--replace-fail "g++" "${lib.getExe' pkgs.stdenv.cc "c++"}"
'';
DEV_RELEASE = 1;
CMAKE_ARGS = toString ([
(lib.cmakeBool "USE_SYSTEM_FMT" true)
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_GGUFLIB" "${gguf-tools}")
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_JSON" "${pkgs.nlohmann_json.src}")
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_NANOBIND" "${nanobind}")
(lib.cmakeBool "FETCHCONTENT_FULLY_DISCONNECTED" true)
(lib.cmakeBool "MLX_BUILD_CPU" true)
(lib.cmakeBool "MLX_BUILD_METAL" true)
(lib.cmakeOptionType "string" "CMAKE_INSTALL_LIBDIR" "lib")
(lib.cmakeOptionType "filepath" "FETCHCONTENT_SOURCE_DIR_METAL_CPP" "${metal_cpp}")
(lib.cmakeOptionType "string" "CMAKE_OSX_DEPLOYMENT_TARGET" "${pkgs.apple-sdk_26.version}")
(lib.cmakeOptionType "filepath" "CMAKE_OSX_SYSROOT" "${pkgs.apple-sdk_26.passthru.sdkroot}")
] ++ lib.optionals (isDarwin && isx86_64) [
(lib.cmakeBool "MLX_ENABLE_X64_MAC" true)
]);
SDKROOT = pkgs.apple-sdk_26.passthru.sdkroot;
MACOSX_DEPLOYMENT_TARGET = pkgs.apple-sdk_26.version;
});
} // lib.optionalAttrs isLinux {
mlx = prev.mlx.overrideAttrs (old: {
buildInputs = old.buildInputs ++ lib.optionals cudaSupport cudaLibs;
autoPatchelfIgnoreMissingDeps = lib.optionals cudaSupport [ "libcuda.so.1" ];
postInstall = ''
cp -r "${final.${libmlx_source}}/${final.python.sitePackages}/mlx" "$out/${final.python.sitePackages}/mlx/"
'';
});
} // lib.optionalAttrs cudaSupport {
"${libmlx_source}" = prev."${libmlx_source}".overrideAttrs (old: {
buildInputs = old.buildInputs ++ cudaLibs;
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
nvidia-cufile = prev.nvidia-cufile.overrideAttrs (old: {
buildInputs = old.buildInputs ++ [ pkgs.rdma-core ];
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
nvidia-cusolver = prev.nvidia-cusolver.overrideAttrs (old: {
buildInputs = old.buildInputs ++ cudaLibs;
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
nvidia-nvshmem-cu13 = prev.nvidia-nvshmem-cu13.overrideAttrs (old: {
buildInputs = old.buildInputs ++ [ pkgs.rdma-core pkgs.pmix pkgs.libfabric pkgs.ucx pkgs.openmpi ];
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
nvidia-cusparse = prev.nvidia-cusparse.overrideAttrs (old: {
buildInputs = old.buildInputs ++ [ cudaLibs ];
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
torch = prev.torch.overrideAttrs (old: {
buildInputs = old.buildInputs ++ cudaLibs;
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
};
pyprojectOverlay = workspace.mkPyprojectOverlay {
sourcePreference = "wheel";
dependencies = members;
};
editableOverlay = workspace.mkEditablePyprojectOverlay {
# Use environment variable pointing to editable root directory
root = "$REPO_ROOT";
members = [ "exo" "exo-bench" ];
};
# Additional overlay for Linux-specific fixes (type checking env).
# Native wheels have shared lib dependencies we don't need at type-check time.
linuxOverlay = final: prev:
let
ignoreMissing = drv: drv.overrideAttrs { autoPatchelfIgnoreMissingDeps = [ "*" ]; };
nvidiaPackages = lib.filterAttrs (name: _: lib.hasPrefix "nvidia-" name) prev;
in
lib.optionalAttrs pkgs.stdenv.hostPlatform.isLinux (
(lib.mapAttrs (_: ignoreMissing) nvidiaPackages) // {
mlx = ignoreMissing prev.mlx;
mlx-cuda-13 = prev.mlx-cuda-13.overrideAttrs (old: {
buildInputs = (old.buildInputs or [ ]) ++ [
final.nvidia-cublas
final.nvidia-cuda-nvrtc
final.nvidia-cudnn-cu13
final.nvidia-nccl-cu13
];
preFixup = ''
addAutoPatchelfSearchPath ${final.nvidia-cublas}
addAutoPatchelfSearchPath ${final.nvidia-cuda-nvrtc}
addAutoPatchelfSearchPath ${final.nvidia-cudnn-cu13}
addAutoPatchelfSearchPath ${final.nvidia-nccl-cu13}
'';
autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ];
});
torch = ignoreMissing prev.torch;
triton = ignoreMissing prev.triton;
}
);
pythonSet = (pkgs.callPackage inputs.pyproject-nix.build.packages {
inherit python;
}).overrideScope (
lib.composeManyExtensions [
inputs.pyproject-build-systems.overlays.default
overlay
pyprojectOverlay
exoOverlay
buildSystemsOverlay
linuxOverlay
]
);
# mlx-cpu and mlx-cuda-13 both ship mlx/ site-packages files; keep first.
# mlx-cpu/mlx-cuda-13 and nvidia-cudnn-cu12/cu13 ship overlapping files.
venvCollisionPaths = lib.optionals pkgs.stdenv.hostPlatform.isLinux [
"lib/python3.13/site-packages/mlx*"
"lib/python3.13/site-packages/nvidia*"
];
# Exclude bench deps from main env (bench has its own benchVenv)
exoDeps = removeAttrs workspace.deps.default [ "exo-bench" ];
exoVenv = (pythonSet.mkVirtualEnv "exo-env" exoDeps).overrideAttrs {
venvIgnoreCollisions = venvCollisionPaths;
};
# Virtual environment with dev dependencies for testing
testVenv = (pythonSet.mkVirtualEnv "exo-test-env" (
exoDeps // {
exo = [ "dev" ]; # Include pytest, pytest-asyncio, pytest-env
}
)).overrideAttrs {
venvIgnoreCollisions = venvCollisionPaths;
};
mkPythonScript = name: path: pkgs.writeShellApplication {
venv = name: (pythonSet.mkVirtualEnv "${name}-env" members).overrideAttrs (_: { venvSkip = [ "lib/python${python.pythonVersion}/site-packages/mlx/share/cmake/*" ]; });
mkApp = cmd: name: pkgs.writeShellApplication {
inherit name;
runtimeInputs = [ exoVenv ];
runtimeEnv = {
EXO_DASHBOARD_DIR = self'.packages.dashboard;
EXO_RESOURCES_DIR = inputs.self + /resources;
};
text = ''exec python ${path} "$@"'';
runtimeInputs = [
# mlx and mlx-cuda ship clashing cmake files - we dont need them at runtime anyway
(venv name)
]
++ lib.optionals isDarwin [ pkgs.macmon ];
text = "exec " + lib.optionalString cudaSupport "${lib.getExe pkgs.nix-gl-host} " + cmd;
};
in
{
inherit venv;
editablePythonSet = pythonSet.overrideScope editableOverlay;
mkPythonScript = path: mkApp ''python ${path} "$@"'';
mkExo = mkApp ''exo "$@"'';
};
in
{
perSystem =
{ self', pkgs, unfreePkgs, lib, ... }:
let
inherit (pkgs.stdenv.hostPlatform) isLinux;
inherit (mkPythonSet { inherit self' pkgs lib; members = { exo = [ "cpu" ]; }; }) editablePythonSet mkExo;
benchVenv = pythonSet.mkVirtualEnv "exo-bench-env" {
exo-bench = [ ];
# Virtual environment with dev dependencies for testing
testVenv = (mkPythonSet {
inherit self' pkgs lib; members = {
exo = [ "dev" "cpu" ]; # Include pytest, pytest-asyncio, pytest-env
};
}).venv "exo-test";
mkBenchScript = name: path: pkgs.writeShellApplication {
inherit name;
runtimeInputs = [ benchVenv ];
text = ''exec python ${path} "$@"'';
mkBenchScript = (mkPythonSet {
inherit self' pkgs lib; members = {
exo = [ "cpu" ];
exo-bench = [ ]; # Include pytest, pytest-asyncio, pytest-env
};
}).mkPythonScript;
mkSimplePythonScript = name: path: pkgs.writeShellApplication {
inherit name;
@@ -159,56 +213,35 @@
text = ''exec python ${path} "$@"'';
};
exoPackage = pkgs.runCommand "exo"
{
nativeBuildInputs = [ pkgs.makeWrapper ];
}
''
mkdir -p $out/bin
# Create wrapper script
makeWrapper ${exoVenv}/bin/exo $out/bin/exo \
--set EXO_DASHBOARD_DIR ${self'.packages.dashboard} \
--set EXO_RESOURCES_DIR ${inputs.self + /resources} \
${lib.optionalString pkgs.stdenv.hostPlatform.isDarwin "--prefix PATH : ${pkgs.macmon}/bin"}
'';
in
{
# Python package only available on macOS (requires MLX/Metal)
packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin
{
exo = exoPackage;
# Test environment for running pytest outside of Nix sandbox (needs GPU access)
exo-test-env = testVenv;
} // {
packages = {
exo = mkExo "exo";
editableVenv = editablePythonSet.mkVirtualEnv "exo-dev-env" { exo = [ "dev" ]; };
# for running tests in ci
exo-test-env = testVenv;
exo-bench = mkBenchScript "exo-bench" (inputs.self + /bench/exo_bench.py);
exo-eval = mkBenchScript "exo-eval" (inputs.self + /bench/exo_eval.py);
exo-eval-tool-calls = mkBenchScript "exo-eval-tool-calls" (inputs.self + /bench/eval_tool_calls.py);
# used by ./tests/run_exo_on.sh
exo-get-all-models-on-cluster = mkSimplePythonScript "exo-get-all-models-on-cluster" (inputs.self + /tests/get_all_models_on_cluster.py);
} // lib.optionalAttrs isLinux {
exo-cuda-12 = (mkPythonSet { inherit self' lib; inherit (unfreePkgs.pkgsCuda.cudaPackages_12) pkgs; members = { exo = [ "cuda12" ]; }; }).mkExo "exo-cuda-12";
exo-cuda-13 = (mkPythonSet { inherit self' lib; inherit (unfreePkgs.pkgsCuda.cudaPackages_13) pkgs; members = { exo = [ "cuda13" ]; }; }).mkExo "exo-cuda-13";
};
checks = {
# Ruff linting (works on all platforms)
lint = pkgs.runCommand "ruff-lint" { } ''
export RUFF_CACHE_DIR="$TMPDIR/ruff-cache"
${pkgs.ruff}/bin/ruff check ${inputs.self}
touch $out
'';
# Hermetic basedpyright type checking
typecheck = pkgs.runCommand "typecheck"
{
nativeBuildInputs = [
testVenv
pkgs.basedpyright
];
}
''
cd ${inputs.self}
export HOME=$TMPDIR
basedpyright --pythonpath ${testVenv}/bin/python
touch $out
'';
typecheck = pkgs.runCommand "typecheck" { nativeBuildInputs = [ testVenv ]; } ''
cd ${inputs.self}
basedpyright
touch $out
'';
};
};
}
@@ -8,6 +8,15 @@ family = "deepseek"
quantization = "4bit"
base_model = "DeepSeek V3.1"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 131072
[storage_size]
in_bytes = 405874409472
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.1/blob/main/generation_config.json
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.1/discussions/19
[sampling_defaults]
temperature = 0.6
top_p = 0.95
@@ -8,6 +8,15 @@ family = "deepseek"
quantization = "8bit"
base_model = "DeepSeek V3.1"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 131072
[storage_size]
in_bytes = 765577920512
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.1/blob/main/generation_config.json
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.1/discussions/19
[sampling_defaults]
temperature = 0.6
top_p = 0.95
@@ -8,6 +8,15 @@ family = "deepseek"
quantization = "4bit"
base_model = "DeepSeek V3.2"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "tool_conditional"
context_length = 131072
[storage_size]
in_bytes = 378086226621
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.2/blob/main/generation_config.json
# Source: https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "deepseek"
quantization = "8bit"
base_model = "DeepSeek V3.2"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "tool_conditional"
context_length = 131072
[storage_size]
in_bytes = 755957120916
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.2/blob/main/generation_config.json
# Source: https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -0,0 +1,21 @@
model_id = "mlx-community/DeepSeek-V4-Flash"
n_layers = 43
hidden_size = 4096
num_key_value_heads = 1
supports_tensor = true
tasks = ["TextGeneration"]
family = "deepseek"
quantization = "8bit"
base_model = "DeepSeek V4 Flash"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "tool_conditional"
context_length = 1048576
[storage_size]
in_bytes = 155095760030
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
[sampling_defaults]
temperature = 1.0
top_p = 1.0
@@ -0,0 +1,21 @@
model_id = "mlx-community/DeepSeek-V4-Pro"
n_layers = 61
hidden_size = 7168
num_key_value_heads = 1
supports_tensor = true
tasks = ["TextGeneration"]
family = "deepseek"
quantization = "8bit"
base_model = "DeepSeek V4 Pro"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "tool_conditional"
context_length = 1048576
[storage_size]
in_bytes = 849681803879
# Source: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro
[sampling_defaults]
temperature = 1.0
top_p = 1.0
@@ -8,6 +8,13 @@ family = "glm"
quantization = "8bit"
base_model = "GLM 4.5 Air"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 131072
[storage_size]
in_bytes = 122406567936
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 0.6
top_p = 0.95
@@ -8,6 +8,13 @@ family = "glm"
quantization = "bf16"
base_model = "GLM 4.5 Air"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 131072
[storage_size]
in_bytes = 229780750336
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 0.6
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "4bit"
base_model = "GLM 4.7"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 198556925568
# Source: https://huggingface.co/zai-org/GLM-4.7
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "6bit"
base_model = "GLM 4.7"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 286737579648
# Source: https://huggingface.co/zai-org/GLM-4.7
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "8bit"
base_model = "GLM 4.7"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 396963397248
# Source: https://huggingface.co/zai-org/GLM-4.7
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "4bit"
base_model = "GLM 4.7 Flash"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 19327352832
# Source: https://huggingface.co/zai-org/GLM-4.7-Flash
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "5bit"
base_model = "GLM 4.7 Flash"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 22548578304
# Source: https://huggingface.co/zai-org/GLM-4.7-Flash
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "6bit"
base_model = "GLM 4.7 Flash"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 26843545600
# Source: https://huggingface.co/zai-org/GLM-4.7-Flash
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,15 @@ family = "glm"
quantization = "8bit"
base_model = "GLM 4.7 Flash"
capabilities = ["text", "thinking", "thinking_toggle"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 34359738368
# Source: https://huggingface.co/zai-org/GLM-4.7-Flash
# Source: https://unsloth.ai/docs/models/glm-4.7-flash
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,14 @@ family = "glm"
quantization = "8bit"
base_model = "GLM-5"
capabilities = ["text", "thinking"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 790517400864
# Source: https://huggingface.co/zai-org/GLM-5
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,14 @@ family = "glm"
quantization = "MXFP4-Q8"
base_model = "GLM-5"
capabilities = ["text", "thinking"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 405478939008
# Source: https://huggingface.co/zai-org/GLM-5
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -8,6 +8,14 @@ family = "glm"
quantization = "bf16"
base_model = "GLM-5"
capabilities = ["text", "thinking"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 1487822475264
# Source: https://huggingface.co/zai-org/GLM-5
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
@@ -0,0 +1,21 @@
model_id = "mlx-community/GLM-5.1-DQ4plus-q8"
n_layers = 78
hidden_size = 6144
num_key_value_heads = 64
supports_tensor = true
tasks = ["TextGeneration"]
family = "glm"
quantization = "8bit"
base_model = "GLM-5.1"
capabilities = ["text", "thinking"]
reasoning_dialect = "post_last_user"
context_length = 202752
[storage_size]
in_bytes = 465173655552
# Source: https://huggingface.co/zai-org/GLM-5.1
# Source: https://docs.z.ai/api-reference/llm/chat-completion
[sampling_defaults]
temperature = 1.0
top_p = 0.95
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