• fix: handle empty responses tool output
Normalize empty Responses `function_call_output.output` values before converting them into Chat Completions `role="tool"` messages. Empty strings, whitespace-only strings, and empty arrays now use the existing no-output sentinel, while non-empty text and content arrays are preserved.
Add regression coverage for empty tool outputs, image payloads outside `output`, content-array serialization, validator round trips, and preserving non-empty text.
---------
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Co-authored-by: Tai An <antai12232931@outlook.com>
Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
* studio: import MCP servers from a config file
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* import config' on the add-server form
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: defensively handle MCP config imports
* fix: address MCP import review follow-ups
* fix: preserve apostrophes in Windows MCP commands
* fix: preserve apostrophe-wrapped Windows MCP args
* fix: align Windows MCP parsing with list2cmdline
* fix: preserve explicit MCP remote transport intent
* fix: trim MCP remote URLs before transport checks
---------
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
* fix(studio): surface live step with null loss through the SSE progress stream
The metric histories skip non-finite steps, so during a NaN stretch the
SSE live loop and final complete event replayed the last finite
step/loss pair. Follow the live progress step when it is ahead of the
history tail and report its loss honestly (null until recovery).
Completes the NaN honesty fix for the SSE consumer flagged in review.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Apply live-step handling to inactive streams and clear the UI loss on null for PR #6206
Fresh /progress connections after a finished run took the inactive branch
which still replayed the last finite step and loss pair; apply the same
live-step correction there. On the frontend, applyProgress kept the stale
currentLoss when a payload advanced the step with a null loss; clear it so
the display shows -- until the loss recovers. Widen the runtime state type
to number | null, which the view layer already handles.
---------
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* Studio: report the real llama-server context window and add an opt-in overflow policy for OpenAI-compatible serving
A community report showed OpenCode failing tool calls every few minutes
against Studio's OpenAI-compatible API while the same GGUF was stable on
LM Studio. Root cause: Studio advertises the requested context length, but
llama-server can allocate less (memory-fit step on small GPUs, --parallel
slot split), so clients budget against a window that does not exist. Their
generations truncate mid tool call at the real wall (finish_reason=length
with cut JSON arguments) and eventually the prompt itself exceeds the real
window, returning a 400 that agentic clients treat as non-retryable.
Changes:
- After llama-server health, read default_generation_settings.n_ctx from
/props and adopt it whenever it is below Studio's computed context, with
a warning. The load response, status route, UI value, and the passthrough
max_tokens ceiling all become honest automatically.
- Expose context_length and max_context_length on /v1/models so clients can
budget against the enforced window.
- Accept empty role=tool content (commands with no output are routine in
agentic loops; OpenAI and llama-server both accept it) instead of a 400.
- Add context_overflow=truncate_middle (per request, or server-wide via
UNSLOTH_CONTEXT_OVERFLOW=truncate_middle): on exceed_context_size_error
the passthrough drops whole middle turn-groups (system prompt, first turn,
and recent turns kept; tool calls stay paired with their results), clips
oversized contents middle-out when group-dropping is not enough, clamps
max_tokens to the generation headroom, and retries. Default stays 'error'
with code=context_length_exceeded so clients running their own compaction
keep full control.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: allocate the requested context for real (kv-unified, fit-ctx floor)
Two launch-flag gaps caused the advertised vs allocated divergence at the
source:
- llama-server enables --kv-unified only when the slot count is auto; Studio
always passes --parallel N, which silently splits -c into per-slot windows
of -c/N. Pass --kv-unified when N > 1 so a single request can use the full
advertised window (same total KV memory, shared pool).
- with --fit on the fit step may set ctx as low as 4096; pass
--fit-ctx <requested> for explicit requests so fit offloads or fails into
the existing --fit off retry instead of silently shrinking the window.
Both flags are gated on --help capability probing so older builds keep the
current behavior, where the /props readback remains the backstop. Verified
live: -c 98304 --parallel 4 now serves per-slot n_ctx 98304 (was 24576),
48k-token requests pass through the passthrough, and the readback warning no
longer fires.
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When training produced a NaN or Inf loss event, the handler filtered the
value to None but never updated progress.loss — clients kept seeing the
last finite value as if everything were fine.
Now: on non-finite loss, clear progress.loss to None and log a one-shot
warning. Training continues (no phase=error, no _should_stop), matching
the expected behavior for a non-fatal numerical event.
Test: tests/test_training_nan_loss_handling.py with 6 cases covering
finite, NaN, +/-Inf, idempotency of the one-shot warning, and recovery
when a finite step follows a non-finite one.
* Studio: auto Cloudflare tunnel for 0.0.0.0 launches
Binding Studio to 0.0.0.0 for remote access often leaves the raw
http://<ip>:<port> URL unreachable (https-vs-http, blocked high ports,
closed cloud security groups). On a wildcard bind, auto-start a free
cloudflared quick tunnel and show its https://*.trycloudflare.com URL in
the startup banner:
Secure link access via Cloudflare: https://<random>.trycloudflare.com
- new studio/backend/cloudflare_tunnel.py: find or download+cache the
cloudflared binary (per-OS/arch GitHub release, safe .tgz extract),
start the tunnel, parse the URL, tear it down. Stdlib only; best-effort
and non-fatal throughout (a missing binary or offline box never blocks
or slows startup).
- run_server starts the tunnel for 0.0.0.0 only (skips loopback, api-only
and Colab), prints the line in the banner, and _graceful_shutdown stops
the child so it never orphans.
- --cloudflare/--no-cloudflare flag (default on) on `unsloth studio` and
`unsloth studio run`, forwarded through the re-exec into run_server.
- tests for the helper, the CLI flag forwarding, and the run.py defaults.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio cloudflare: send a User-Agent on the cloudflared download
GitHub's CDN can 403 the default Python-urllib User-Agent on release asset
downloads. Set an explicit UA and pin it with a test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio cloudflare: address review (opt-out for subcommands, tunnel teardown)
- reject --no-cloudflare placed before a subcommand (it would not reach the
subcommand), mirroring the --parallel guard
- register the tunnel before waiting for its URL so a shutdown during the wait
stops cloudflared instead of orphaning it
- tear the server + children down if `unsloth studio run` startup aborts
(health timeout, model-load error, Ctrl+C) before the wait loop
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
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* Studio: surface the llama.cpp update affordance when MTP is disabled
When a model asks for MTP (auto on an MTP model, or forced mtp / mtp+ngram)
but it gets disabled, the load already degrades gracefully and serves without
speculative decoding. Until now the UI gave no hint why, or that an update
would fix it.
Record why MTP was dropped on the backend (spec_fallback_reason): the probe
found no mtp token (binary_no_mtp), the spawn aborted with an outdated-arch /
context-build error such as a prebuilt that predates the Gemma drafter
(binary_outdated), or the current build could not run it, e.g. a CUDA kernel
limit (runtime_error). Expose it in the inference status. In the chat
Speculative Decoding section, show a short note and, for the two update-fixable
reasons, an inline Update llama.cpp button that reuses the existing update flow.
A runtime_error gets the note without an update push, since a newer build may
not fix it.
Backend tests cover the reason being set / cleared. Frontend typechecks.
* Address review: tighten the update hint to genuinely outdated binaries
Reserve binary_outdated (which surfaces the Update llama.cpp affordance) for an
unknown-architecture abort, which proves the prebuilt predates the model;
classify the generic memory/context build failures as runtime_error, where an
update may not help. Frontend: only append the "Update llama.cpp to enable it"
sentence when an update is actually available, so the text never points at an
action the UI is not offering.
* Studio: enable MTP for sub-3B Gemma separate-drafter GGUFs
The sub-3B auto-drop to ngram-mod was tuned for an embedded draft head
(Qwen), whose per-token cost regresses below 3B. Gemma ships the head as a
separate root mtp-*.gguf drafter, a tiny standalone model that is cheap
enough to win below 3B: B200 Q4_K_XL bench, draft-mtp n=2 vs spec-off,
gemma-4-E2B (2B) = 1.21x (accept ~0.65) while ngram-mod is 1.00x.
Exempt a separate drafter from the sub-3B gate everywhere the threshold is
applied: the resolver (_mtp_too_small), the auto-fit VRAM reserve, the
drafter auto-download decision, and the reload-skip mirror via a
has_separate_drafter flag on _auto_mode_drops_mtp. Embedded sub-3B heads
(Qwen) still drop to ngram-mod. A drafter the binary cannot build (older
prebuilt, or a CUDA kernel limit) still aborts the spawn and the load
retries once without speculative decoding.
Adds the full Qwen3.5 + Gemma-4 (regular and QAT) auto/off/forced resolver
matrix, plus explicit sub-3B exemption tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Always compare the separate drafter in the reload-skip mirror
The sub-3B wrapper around the drafter compare could skip it when the drafter
was deleted out from under a running sub-3B server (detected None, stored set),
leaving a stale launch. The resolved-path compare is cheap and already handles
every case, so drop the _auto_mode_drops_mtp guard (and its now-unused imports)
and always compare when the mode can use a drafter and the user does not own
--spec-type. Addresses review feedback on #6191.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix: ignore unsupported env proxy during Studio startup
* fix: handle missing socksio env proxy at startup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Match printf logging style and inline the proxy predicate for PR #6102
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: offer the in-app llama.cpp update for source-build (markerless) installs
Source-build installs have no UNSLOTH_PREBUILT_INFO.json marker, so freshness
reported supported=False and the Update button never showed (notably on macOS,
where the fork shipped no prebuilt before b9585 and setup fell back to a source
build). When an install has no marker but an official prebuilt now exists for
the host, surface the update and let one click swap it in place.
- install_llama_prebuilt.py: published_repo_for_host() (the setup.sh host->repo
rule in Python) and a --resolve-prebuilt mode that reports whether a prebuilt
exists for this host without downloading.
- llama_cpp_update.py: markerless branch in get_update_status/start_update,
version-suppressed so source builds already newer than latest are not nagged;
fail-open throughout.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: run llama update detection off the event loop, expose source_build
The markerless source-build check probes the host and reads GitHub, so run
get_update_status and start_update in a worker thread to keep the API
responsive. Expose source_build in the status response so the banner can label
the source-build switch.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Keep the llama-route auth stub out of sys.modules for the rest of the suite
test_llama_route.py replaced sys.modules['auth.authentication'] with a
bare stub at collection time and never restored it, so every later test
importing create_access_token got the stub: 17 failures across
test_desktop_auth, test_middleware, test_openai_tool_passthrough and
test_rag_preview on all four Backend CI Python versions. Import the
real module when its deps are available and only stub in minimal envs,
popping the stubs after the standalone route load either way.
* Studio: address review on the source-build update path
- published_repo_for_host: route CPU-only Windows to ggml-org too (mirrors
setup.ps1; the fork ships no win-cpu bundle), macOS always the fork.
- markerless detection compares/display the upstream llama_tag, not a possible
fork wrapper release_tag, so a source build is not wrongly judged newer.
- do not offer when there is no resolvable install root (a pinned
LLAMA_SERVER_PATH outside a managed dir): an apply would not take effect.
* Ignore version probes in the update tests' subprocess capture
The status polls in these tests trigger the new source-build detection,
which shells out to llama-server --version through the same patched
subprocess.run. On slow runners that probe lands after the installer
call and clobbers the single captured argv, failing the flag
assertions (seen on the 3.10/3.11 Backend CI jobs). Skip probe calls
in all three fakes so only the installer invocation is captured.
* Skip markerless re-detection while the update job is swapping the tree
On a source-build install the frontend polls update-status every 3s
during an apply, and each poll ran _source_build_status, which execs
the very llama-server binary the job is concurrently replacing. On
Windows that exec can hold the exe long enough to fail the installer's
os.replace; everywhere it is a per-poll subprocess spawn for a status
the poller does not read (it only consumes job progress). Gate the
markerless branch on the job not running; the marked path is probe-free
and still returns the live job state.
* Studio: tighten source-build update root, repo routing, and downgrade guard
Only manage a markerless install when the active binary lives under a
resolvable llama.cpp root (marker dir, UNSLOTH_LLAMA_CPP_PATH it sits in,
or a llama.cpp ancestor); a pinned LLAMA_SERVER_PATH or a PATH/system
binary is left alone so an apply cannot install where it would not take
effect. Gate start_update on the same suppression as detection so a
direct POST cannot downgrade a source build newer than the latest
prebuilt. Route Linux hosts with AMD tooling (rocminfo/amd-smi/hipconfig/
hipinfo) to the fork in --resolve-prebuilt, matching setup.sh, so a HIP
source build is not offered an upstream CPU prebuilt.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: cover inactive env root and pinned llama.cpp checkout in update root tests
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: forward preserve_thinking + reasoning_effort on the OpenAI passthrough
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Provide _request_reasoning_kwargs on the responses passthrough test backend mock
The OpenAI passthrough body builder now asks the active backend for
capability-gated reasoning kwargs. The responses stream adapter test fakes
the backend with a bare SimpleNamespace, so give it the same method a
non-reasoning template would expose (returns None, keeping
chat_template_kwargs out of the captured body).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Shorten the backend mock comment
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: gracefully disable MTP when the model has no head or drafter
Selecting MTP or MTP+Ngram in Speculative Decoding on a GGUF with no nextn
head and no separate drafter aborted the whole load. llama-server does not
no-op an empty draft-mtp request: it exits with 'failed to measure MTP
context memory: failed to create llama_context', surfaced to the user as a
generic 'llama-server failed to start. Check that the GGUF file is valid
and you have enough memory.'
Build-time fix in _build_speculative_flags: when a forced mtp / mtp+ngram
mode targets a model with no MTP head and no drafter (is_mtp_model is
False), default back instead of emitting draft-mtp. mtp falls back to
--spec-default; mtp+ngram keeps the ngram-mod half, which needs no head.
Real MTP models (embedded head or separate drafter), sub-3B MTP overrides,
and the auto path are unchanged.
Runtime hardening: the existing post-launch MTP retry only fired for
separate-file drafters (--model-draft in spec_flags), so an embedded-head
model that the binary cannot build still hard-failed. Gate the retry on the
spec block requesting MTP, recognise the embedded-head abort strings
('failed to measure MTP context memory', 'failed to create llama_context'),
and make the drafter name None-safe in the warning.
Tests: extend the resolver matrix (forced mtp / mtp+ngram on a non-MTP
model) and add two cases asserting the default-back emission.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Adds an in-app "Update llama.cpp" banner and button to Unsloth Studio. When the installed prebuilt is behind the latest published release, a non-invasive banner appears; clicking Update downloads the latest prebuilt for this host and swaps it in place in the background, with no restart.
Detection reuses the freshness check from #5529. The update re-runs install_llama_prebuilt.py the same way setup.sh and setup.ps1 do after #5963: it forwards the published repo and the AMD gfx target derived from the install marker, and does not pass the removed --simple-policy or the arm64-only --cpu-fallback.
While the installer swaps binaries the backend enters a maintenance state (flag set under the serial load lock, active server unloaded) so a concurrent load cannot start a server from a half-swapped binary; the next load uses the new build. The banner also handles refused responses and jobs started in another tab so it never sticks on "Updating...".
Verified end to end on an NVIDIA B200: installed b9493, detected the update, applied it, and confirmed the binary at the same path advanced to b9585 in the same process. Hermetic backend tests and the frontend type-check pass.
* Studio: fall back to text-only when llama.cpp is too old for a model's vision projector
Loading a GGUF vision model starts llama-server with --mmproj <projector>. When the installed llama.cpp prebuilt predates the model's projector format, llama-server aborts at startup with 'clip.cpp: Unknown projector type' (exit -6), and the whole load failed even though the base GGUF is a fine text/tools chat model. Seen with gemma-4 on a 3-day-old prebuilt (build b9496).
load_model now retries the launch once without --mmproj when the captured startup output indicates a projector-format incompatibility. The retry runs the model text-only, marks the session non-vision (is_vision False, mmproj audio dropped) so the status/capabilities the frontend reads stay consistent, and warns the user to update llama.cpp. Detection is generic, not model-specific, and conservative: OOM, bad GGUF, port-bind, missing-file and other failures keep their existing handling and never retry. If the text-only retry also fails, it errors out with the real reason.
Adds _is_projector_incompatibility and _strip_mmproj_args (unit-tested with the real gemma-4 abort plus negatives) and extracts _start_llama_process so both the initial start and the retry share one spawn path and each logs its argv.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Reformat mmproj fallback files to match main (ruff line-length 100 + kwarg spacing)
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* Studio: route arm64 Linux CUDA hosts to linux-arm64-cuda prebuilts
* Studio: SM-aware selection for windows-cuda app bundles
* Studio: select published ROCm bundles by gfx target (linux + windows)
* Studio: route macOS installs to the fork's prebuilt bundles
* Studio: fix windows cuda13 driver-13.0 gate and ROCm gfx prefix overreach
* Fix Blackwell Windows pin shadowing native app-bundle (b9360 over b9457)
* Match Windows cuda12 driver floor to Linux (12.x minor-version compat)
* Fix Windows app-bundle dropped when runtime DLLs come from torch/lib
* Fold the manifest resolver into the simple-path resolver (one entry, no dormant full path)
* Remove unused UNSLOTH_LLAMA_PUBLISHED_REPO override
* Route Windows GPU hosts to the fork prebuilts in setup.ps1
* Document sm_103 path divergence and mark --simple-policy as a no-op
* Note sm_103 coverage now comes from the producer manifest
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Remove the now-vestigial --simple-policy flag (one resolver handles all hosts)
* Unify the fork onto the manifest path; drop the linux-x64 filename path and hardcoded coverage tables
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Strip whitespace from manifest gfx_target/mapped_targets when parsing
* Windows CUDA: sort coverage-unknown bundles last so they can't outrank targeted ones
* Share the SM-coverage sort key between the linux and windows selectors via _sm_range
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: reject approved releases with an exact source archive but no source repo to clone from
* Studio: accept the fork's windows-rocm kind in the Windows reinstall check
* Studio: accept a manifest-bundle source repo in the exact-source release check
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: route Linux hosts to the fork only when a usable GPU is present
* Fix Windows AMD lemonade tag resolution for PR #5963
The fork release scan passes each scanned release's upstream tag
(b9518, ...) to the lemonade lookup, but lemonade publishes its own tag
series (b1292, ...) that never contains upstream tag numbers. On a
Windows AMD host every scanned release therefore 404s the lemonade
fetch twice, the upstream HIP zip is dropped by the approved-hash gate,
and the scan walks the whole release history until it dies on the
unauthenticated GitHub rate limit or falls to a HIP source build. The
Linux path already passes the requested tag ("latest") and works.
Thread the requested tag through resolve_release_asset_choice ->
resolve_asset_choice -> resolve_upstream_asset_choice as lemonade_tag,
used only by the lemonade lookups. Upstream asset names keep the
concrete per-release tag and all new parameters default to the old
behavior.
Verified on a gfx1151 box: before, the native Windows install scanned
b9518..b8811 and aborted on rate limit; after, it selects
llama-b1292-windows-rocm-gfx1151-x64.zip (lemonade) from fork release
b9518, passes staged validation, and the installed llama-server
enumerates ROCm0. WSL keeps selecting the matching ubuntu bundle.
Adds a regression test pinning that the Windows fork path resolves
lemonade via /releases/latest, never /releases/tags/<fork-tag>.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Plan lemonade for Linux ROCm hosts on the ggml-org direct path for PR #5963
Audit follow-up to 72f32364 across the other selection pathways. The
ggml-org direct planner kept its lemonade attempt for Windows ROCm
hosts but planned only the CPU tarball for Linux ROCm hosts, so an AMD
Linux box routed to ggml-org (for example a --published-repo override)
silently installed the CPU build. That lemonade planning used to live
in the --simple-policy dispatcher this PR removed.
Add the lemonade attempt ahead of the CPU tarball in the Linux x86_64
branch, mirroring the Windows branch, with the lookup keyed to the
requested tag. Adds a regression test asserting lemonade is the first
attempt for a Linux ROCm host on the direct path.
Also re-verified the other pathways on a gfx1151 box: the fork-routed
flows pass the requested tag everywhere, repeat runs over an existing
lemonade install correctly skip with "already matches selected release
b9518" on both native Windows and WSL, and macOS, CUDA and CPU
selection are untouched. Suites: 328 passed on Linux, Windows matches
the pre-existing baseline.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Studio: support separate-file MTP GGUF drafters (Gemma 4)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: fix review findings for separate-file MTP drafters
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: pair local MTP drafters by name and include them in reload dedup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: manage --model-draft in extras and reject MTP/ copies as models
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix Studio Python, Gemma 4 Unified sidecar, and worker crash messages
* Clean up Gemma 4 sidecar test patch contexts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Polish inference worker crash message
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address transformers tier review feedback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Route Gemma 4 assistant models to transformers 5.10
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(studio): infer mlx vlm resized image layout
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix Windows installer winget msstore certificate failure
`winget install` was invoked without `--source winget`, so winget also
queried the msstore source. When msstore fails certificate pinning
(error 0x8a15005e, "The server certificate did not match any of the
expected values") winget aborts and demands `--source`, so the Python
(and uv) install fails even though the package exists in the winget
source.
- Pass `--source winget` to all winget install calls (Python x2, uv).
Both packages live in the winget source, so this is strictly correct
and skips the failing msstore round-trip entirely.
- Add a python.org fallback (Install-PythonFromPythonOrg) that downloads
the official installer and runs it silently per-user (no admin/UAC)
when winget is unavailable or fails for any reason. Mirrors the
existing uv -> astral.sh fallback so Python installs without manual
steps. Resolves the latest 3.13.x from python.org with a pinned
fallback, and selects the amd64/arm64/x86 installer per architecture.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Pin remaining setup.ps1 winget calls to --source winget
Two winget invocations in studio/setup.ps1 still queried all sources and
could hit the same msstore certificate-pinning failure (0x8a15005e) that
broke the Python install in install.ps1:
- `winget show Nvidia.CUDA --versions` (CUDA Toolkit version probe)
- `winget install ... ShiningLight.OpenSSL.Dev` (OpenSSL dev for llama-server)
Every other winget call in this file already passes `--source winget`
(Git, CMake, VS Build Tools, CUDA install, Node.js, and setup.ps1's own
Python 3.12 install), so these two were stragglers. Both packages live in
the winget source; pinning it makes setup robust to an unhealthy msstore
source, matching the rest of the file.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Stop amd-smi GPU probe from popping a DiskPart UAC prompt
On Windows, AMD GPU detection in install.ps1 and studio/setup.ps1 runs
`amd-smi list` / `static --asic` / `version`. amd-smi (shipped in
System32 by the Adrenalin driver) auto-elevates to read GPU/APU memory
details, surfacing a confusing DiskPart UAC prompt mid-install. The
Studio backend already documents and circuit-breaks on this in
studio/backend/utils/hardware/amd.py, but the installers did not.
Add an Invoke-AmdSmiNoElevate helper (both scripts) that runs amd-smi via
Start-Process under __COMPAT_LAYER=RunAsInvoker so it cannot auto-elevate
(no prompt), with a 30s timeout (matching amd.py) so a flaky amd-smi
cannot stall the install for minutes. On failure/timeout the existing WMI
name -> gfx fallback still resolves the arch, so detection is unchanged on
working hosts.
Verified on a Strix Halo (Radeon 8060S / gfx1151) box: the prompt is gone
and the probe is bounded.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Add experimental ROCm-on-WSL setup helper for Strix Halo (gfx1151)
install.sh already routes gfx1151 (Radeon 8060S / Strix Halo) to the
repo.amd.com/rocm/whl/gfx1151 wheels once a ROCm runtime is present, but
it does not install AMD's driver/ROCm stack -- a large, admin-gated
prerequisite. scripts/install_rocm_wsl_strixhalo.sh automates the Linux
side on a dedicated Ubuntu 24.04 WSL2 distro: ROCm 7.2 (wsl usecase), the
rocr4wsl HSA runtime, a librocdxg build, env setup, and a PyTorch gfx1151
GPU smoke test. A hard preflight refuses to run until the Adrenalin
>=26.3.1 driver is actually present, so it cannot half-install.
Procedure adapted from AMD's ROCm-on-WSL docs and community gfx1151 notes.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Detect AMD GPUs by name so native Windows gets a GPU llama.cpp
The gfx-arch inference from the WMI GPU name was gated behind $HasROCm,
which the hipinfo/amd-smi probe leaves false on the common Windows case
(Adrenalin driver only, no HIP SDK -- and amd-smi often cannot read the
arch without elevation). So an AMD GPU was detected by name but never
mapped to a gfx target, --rocm-gfx was not forwarded, and studio setup
fell back to a CPU llama.cpp build.
Un-gate the inference (install.ps1 + studio/setup.ps1) so it runs whenever
an AMD GPU name is available. The inferred gfx is forwarded as --rocm-gfx,
which makes install_llama_prebuilt.py download the matching lemonade-sdk
ROCm prebuilt (e.g. llama-bNNNN-windows-rocm-gfx1151-x64.zip) -- a
GPU-accelerated llama.cpp that bundles its own ROCm runtime, so it runs
with just the Adrenalin driver. PyTorch's ROCm wheels still require a
confirmed HIP SDK ($HasROCm), so this only affects llama.cpp / inference
and never pulls broken ROCm torch.
Also broaden the name->arch table to every family lemonade ships Windows
assets for: gfx120X (RDNA 4), gfx110X (RDNA 3), gfx1151/gfx1150
(RDNA 3.5), and gfx103X (RDNA 2). Unknown names still fall back to CPU.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Suppress amd-smi DiskPart UAC prompt in the Python install/runtime paths
The earlier PowerShell guard covered install.ps1 / setup.ps1, but the
Python installer (install_llama_prebuilt.py detect_host,
install_python_stack.py ROCm probes) and the Studio backend monitor
(amd.py) also shell out to amd-smi on Windows, where it auto-elevates and
pops the same DiskPart UAC prompt mid-install / at runtime.
Inject __COMPAT_LAYER=RunAsInvoker into the amd-smi subprocess env on
Windows so it runs un-elevated (no prompt). Callers already tolerate an
empty/failed result and fall back to WMI / name detection (installer) or
the existing circuit breaker (amd.py). Gated to Windows so Linux/macOS
amd-smi behaviour is unchanged.
- install_llama_prebuilt.py: handled centrally in run_capture (covers
detect_host's `amd-smi list` and the version probe).
- install_python_stack.py: new _amd_smi_env() helper on its 3 raw
subprocess.run amd-smi calls.
- amd.py: merge RunAsInvoker into the existing child env.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Tighten AMD GPU name->arch patterns to avoid mismatches
The W9[0-9]{3} and RX 90[0-9]{2} patterns added for RDNA 4 were
speculative and over-broad: W9xxx would also match old GCN FirePro
W9100/W9000 cards (wrong gfx1201 -> a lemonade gfx120X download that
fails validation), and RX 90[0-9]{2} was redundant with the explicit
9070/9060 entries. Drop both; keep only confirmed RDNA 4 SKUs. Unmatched
AMD names still fall back cleanly to CPU.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Fetch the llama.cpp validation model via huggingface_hub
The prebuilt validation downloads a tiny GGUF test model from huggingface
via bare urllib. On Windows / proxy setups where the server sends an
incomplete TLS chain, urllib cannot complete the Amazon CA chain (it does
no AIA intermediate fetching) and fails with CERTIFICATE_VERIFY_FAILED, so
a perfectly good GPU prebuilt is rejected and the installer falls back to a
CPU source build.
Route the validation-model download through huggingface_hub
(hf_hub_download) -- the same mechanism Studio uses for model downloads,
which completes the chain where urllib cannot -- keeping the direct URL as
a fallback. This lets the lemonade ROCm prebuilt validate and install on
cert-restricted machines (verified: hf_hub_download succeeds where urllib
returns CERTIFICATE_VERIFY_FAILED).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Guard the remaining raw amd-smi version probe via run_capture
A ROCm-version detector in install_llama_prebuilt.py called amd-smi version through a raw subprocess.run that bypassed run_capture's Windows RunAsInvoker guard, so it still triggered the DiskPart UAC prompt during setup. Route it through run_capture like the other amd-smi calls.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Forward --rocm-gfx even when the ROCm runtime is unconfirmed
setup.ps1 forwarded --rocm-gfx (and picked the windows-hip llama.cpp
prebuilt) only inside `if ($HasROCm)`. On Adrenalin-only hosts (amd-smi
present but no HIP SDK, so $HasROCm stays false) the gfx arch was
name-inferred but never forwarded, so install_llama_prebuilt.py saw
has_rocm=False and installed the CPU build -- even though the lemonade
gfx1151 GPU prebuilt runs fine there (it bundles its own ROCm runtime;
verified: llama-cli --list-devices -> ROCm0: AMD Radeon 8060S, 69 GB).
Forward --rocm-gfx whenever a gfx arch is known (it is authoritative and
implies ROCm in install_llama_prebuilt.py), and treat a known gfx arch as
windows-hip in the existing-install mismatch check. --has-rocm stays gated
on the confirmed-runtime signal.
Verified on Radeon 8060S / gfx1151: the installer now selects, validates,
and installs llama-b1286-windows-rocm-gfx1151-x64.zip (ROCm DLLs present)
instead of the CPU build.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Install AMD ROCm PyTorch on name-inferred gfx hosts (enables Train/Export)
setup.ps1 picked the AMD ROCm PyTorch wheels only inside `if ($HasROCm ...)`.
On Adrenalin-only hosts (amd-smi present but no HIP SDK, so $HasROCm is
false) the gfx arch was name-inferred but the ROCm-wheel branch never ran,
so the host got torch+cpu. With CPU torch, torch.cuda.is_available() is
False, so the Studio backend sets CHAT_ONLY=True and hides Train/Export.
Un-gate the ROCm PyTorch index resolution on a known gfx arch (mirrors the
llama.cpp --rocm-gfx fix). AMD's per-arch Windows wheels
(repo.amd.com/rocm/whl/<gfx>) bundle the ROCm runtime, so they work without
a HIP SDK; a failed install still falls back to CPU.
Verified on Radeon 8060S / gfx1151: torch 2.11.0+rocm7.13.0 installs and
torch.cuda.is_available() -> True, device "AMD Radeon(TM) 8060S Graphics",
GPU matmul OK -> CHAT_ONLY=False -> Train/Export enabled.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Force amd-smi un-elevated process-wide in the Python installers
Guarding individual amd-smi call sites kept missing some (install_python_stack.py's probe loop and its Windows GPU re-check), so the DiskPart UAC prompt kept reappearing. Set __COMPAT_LAYER=RunAsInvoker process-wide at the top of install_python_stack.py and install_llama_prebuilt.py on Windows so every amd-smi subprocess (current and future) runs un-elevated with no per-call guard. Safe: these scripts only spawn amd-smi/rocminfo/hipinfo probes and pip/uv. setup.ps1 keeps per-call guards because it also spawns winget installers that need elevation.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Fix Invoke-AmdSmiNoElevate exit code on PS 5.1 + RX 7700S arch match
Start-Process -PassThru leaves the returned process object's .ExitCode
$null after WaitForExit on Windows PowerShell 5.1, so the helper set
$LASTEXITCODE to $null and every caller's `if ($LASTEXITCODE -eq 0 ...)`
was always false -- the amd-smi GPU / gfx-token / ROCm-version detection
branch was effectively dead (masked only because the un-gated WMI
name->gfx inference still ran). Reproduced on PS 5.1.26100.
Rewrite the helper to use [System.Diagnostics.Process]::Start with a
ProcessStartInfo (UseShellExecute=false), whose .ExitCode is reliable,
with async stream reads (ReadToEndAsync) to avoid a pipe-buffer deadlock
and WaitForExit(timeout) to bound a flaky amd-smi. __COMPAT_LAYER=
RunAsInvoker (inherited via the process env) still suppresses the
auto-elevation / DiskPart prompt. Also drops the temp files and the
empty-ArgumentList edge case. Verified: exit code propagates
(7 -> $LASTEXITCODE=7), output captured, env restored.
Also fix the gfx1100 name pattern `RX 7700(?! S)` -> `RX 7700(?!S)` so the
spaceless retail name "RX 7700S" is correctly excluded (it belongs to the
gfx1102 row). Both found by PR review.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Address PR review follow-ups (install.sh table, update path, tests, WSL)
From the multi-agent PR review:
- install.sh: sync the AMD name->arch table with install.ps1 / setup.ps1
(the bash table had drifted to the old narrow patterns). Adds RDNA 2
(gfx103X), workstation PRO W SKUs, and more Strix Halo/Point names, and
orders gfx1102 before gfx1100 so the spaceless retail name "RX 7700S"
resolves correctly (bash case has no negative lookahead). AMD-ROCm-only:
the name inference stays gated behind _has_amd_rocm_gpu(), so NVIDIA /
CPU / macOS are unaffected.
- setup.ps1: the "dependencies up to date" fast path skipped the torch
reinstall, so an existing user who had CPU torch (installed before
ROCm-wheel support) stayed stuck in CHAT_ONLY. Now, when an AMD gfx arch
is known AND the installed torch is CPU-only, don't skip -- force the
dependency pass so the ROCm wheels install.
- scripts/install_rocm_wsl_strixhalo.sh: resolve the real /opt/rocm dir
instead of hardcoding ROCM_VER for LD_LIBRARY_PATH / the librocdxg
symlink (breaks if amdgpu-install lays ROCm under a patch-version dir);
add a LIBROCDXG_REF pin knob and a "verified against" freshness header.
- tests/studio/install/test_pr5940_followups.py: cover _hf_resolve_url_parts,
_fetch_validation_model_bytes (hf path + urllib fallback), run_capture's
Windows-only amd-smi RunAsInvoker injection, and install.ps1 vs setup.ps1
name-table parity (catches future drift). 14 tests, all passing.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix DiskPart UAC prompt: skip amd-smi on Windows without a HIP SDK
On Windows, amd-smi re-initialises the ROCm runtime on every invocation
(even `amd-smi version`) and, on hosts without a working HIP runtime
(consumer APUs/dGPUs with only the Adrenalin driver), elevates a child
process at runtime -- popping a UAC/DiskPart prompt. amd-smi's own
manifest is asInvoker, so __COMPAT_LAYER=RunAsInvoker cannot suppress
that runtime elevation (verified: even `amd-smi version` hangs and
times out with RunAsInvoker set).
Replace the ineffective RunAsInvoker-only approach with a real gate:
only spawn amd-smi on Windows when a HIP SDK is detectable (hipinfo
present, so amd-smi runs un-elevated) or the user opts in with
UNSLOTH_ENABLE_AMD_SMI=1. The gfx arch is already resolved from WMI
name inference (forwarded via --rocm-gfx), so ROCm wheel + lemonade
llama.cpp selection is unaffected. Linux/macOS amd-smi never elevates
and is untouched (no regression). RunAsInvoker is kept as harmless
belt-and-suspenders for tools that DO use manifest elevation.
Applied consistently across:
- studio/backend/utils/hardware/amd.py (runtime GPU polling)
- install.ps1, studio/setup.ps1 (install-time detection)
- studio/install_llama_prebuilt.py (prebuilt arch probe + version)
- studio/install_python_stack.py (ROCm version + arch probe)
Verified live on AMD Radeon 8060S (gfx1151), native Windows: fresh
install detects the GPU, installs ROCm torch (torch.cuda.is_available()
True), launches Studio with no DiskPart prompt, and inference, tool
calling, web search, LoRA finetuning, and GGUF export all run on the GPU.
Tests: add 6 _amd_smi_allowed() gating tests + PowerShell-installer gate
assertions; update the three amd-smi monitoring tests to opt in (they
mock amd-smi as available). Full suite: 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* install.sh: helpful WSL message when the GPU isn't exposed to ROCm
In WSL, an AMD GPU's ROCm-on-WSL runtime is only available with a recent
Adrenalin driver AND a distro AMD supports (currently Ubuntu 24.04). When
neither is in place, GPU detection (rocminfo/_has_amd_rocm_gpu) finds
nothing and we silently fall back to CPU.
Add an actionable hint in the CPU-fallback path, shown only on WSL and
only AFTER detection has already failed -- so it is forward-compatible:
the moment a driver/distro DOES expose the GPU (e.g. if AMD later adds
Ubuntu 26.04 support), detection succeeds and the hint never fires. The
message:
- notes a GPU is plumbed in (/dev/dxg) but no ROCm runtime is exposed,
- lists the two prerequisites (Adrenalin driver + Ubuntu 24.04),
- if the distro is not 24.04, says AMD may not support it yet,
- tells the user to `wsl --install Ubuntu-24.04` and re-run,
- links AMD's ROCm-on-WSL guide + the experimental Strix Halo helper.
Verified live: on Ubuntu-24.04 the hint shows (version-warning omitted)
and the CPU install completes; on Ubuntu-26.04 the extra "this distro may
not be supported" line appears and points to 24.04.
Also fix the experimental scripts/install_rocm_wsl_strixhalo.sh: AMD's
repo.radeon.com/amdgpu-install/ is indexed by unified installer version
(30.30, 31.30, ...), NOT ROCm version, so the hard-coded
amdgpu-install/7.2.0/ path 404'd. Scan the installer dirs newest-first
for a noble .deb matching the target ROCm major.minor (ROCm 7.2 ->
30.30.x/amdgpu-install_7.2.x), falling back to the newest available.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* WSL: fix shortcut collision + pin ROCm-on-WSL driver reqs from AMD docs
Two WSL-related fixes informed by AMD's official ROCm-on-WSL docs and
field reports for Strix Halo / Ryzen AI Max+ (Radeon 8060S, gfx1151):
1. Shortcut collision (real bug). install.sh's WSL branch wrote
"Unsloth Studio.lnk" to the SAME Desktop / Start Menu folder as the
native-Windows installer (install.ps1 New-StudioShortcuts). Running
install.sh in WSL therefore silently retargeted the native shortcut at
the WSL launcher (wt.exe -> wsl.exe), so the desktop/start-menu icon
stopped launching native GPU Studio. Now the WSL shortcut uses a
DISTINCT name -- "Unsloth Studio (WSL - <distro>).lnk" -- and fetches
the Unsloth .ico to %LOCALAPPDATA%\Unsloth Studio so it shows the
proper icon. Native and WSL shortcuts now coexist.
2. Precise ROCm-on-WSL prerequisites. Research (AMD radeon-ryzen WSL
compatibility matrix, gianni.rosagallina.com Feb-2026 guide,
ROCm/ROCm#4952/#5509/#6022) confirms WSL GPU on Strix Halo requires
AMD Adrenalin Edition >= 26.1.1 (26.2.2+ is the first production
ROCDXG/WSL release) + ROCm 7.2.1 + Ubuntu 24.04; an older driver does
not inject the ROCm/DXG runtime into /usr/lib/wsl/lib, so rocminfo sees
only the CPU. install.sh's WSL hint and the experimental
install_rocm_wsl_strixhalo.sh header/preflight now state the exact
driver version (was a guessed ">=26.3.1"), bump ROCM_VER to 7.2.1, link
AMD's radeon-ryzen docs, and document the known librocdxg caveat that
usable VRAM is currently capped at the .wslconfig memory setting.
bash -n clean; install test suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: hint when the AMD driver is too old for ROCm-on-WSL
Adds a detect-and-guide hook for the optional WSL-GPU path. An AMD GPU on
native Windows can also be used inside WSL2, but only with AMD Adrenalin
Edition >= 26.2.2 (the first production ROCDXG/WSL release). Native Windows
GPU works with any recent driver, so this is purely about enabling the WSL
path.
We intentionally do NOT auto-install the driver: AMD referrer-gates driver
downloads (scripted curl/Invoke-WebRequest are blocked) and does not publish
Adrenalin via winget, so no installer can reliably fetch it -- and silently
swapping a live display driver is risky. Instead we point the user at AMD's
official download page (one click), after which the existing WSL detection
lights up automatically.
- install.ps1: new Show-AmdWslDriverHint -- when an AMD GPU is present and the
installed driver predates the 26.2.2 release (DriverDate < 2026-02-01),
print a concise tip with the AMD download URL. Handles DriverDate as either
a CIM DateTime or a WMI string. Suppress with UNSLOTH_SKIP_AMD_DRIVER_HINT=1.
- install.sh (WSL hint): add the direct Adrenalin 26.2.2 download URL and note
that AMD downloads are referrer-gated (open in a browser).
Verified: hint fires on a Sept-2025 driver, auto-suppresses on >= 2026-02-01;
install.ps1 parses; install.sh bash -n clean; suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.ps1: refresh shell icon cache after creating the shortcut
After writing the Desktop / Start Menu .lnk, nudge Explorer to refresh
its icon (ie4uinit.exe -show). Without this, a stale icon cache can show
a blank shortcut icon until the next explorer restart -- most visible
when a shortcut of the same name was rewritten (e.g. a native install
followed by a WSL install, which previously shared the name; now they use
distinct names, but the cache nudge makes the icon appear immediately
regardless). Best-effort and wrapped in try/catch so it never fails the
install. The bundled unsloth.ico itself is valid (verified it renders).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* setup.ps1: don't silently CPU-build llama.cpp on an AMD GPU
For AMD, GPU acceleration comes from the lemonade ROCm prebuilt (it bundles
the ROCm runtime, no HIP SDK needed) and is the preferred/default path. The
source-build fallback is CPU-only -- a HIP/ROCm *source* build would need the
full HIP SDK + ROCm clang toolchain, which the prebuilt exists to avoid.
Previously, if an AMD-GPU host ever fell through to the source build (e.g. the
prebuilt could not be downloaded), it printed "building llama.cpp (CPU-only,
no NVIDIA GPU detected)" and quietly produced a CPU binary -- masking the lost
GPU acceleration. Now that case emits a loud [WARN] explaining the GPU prebuilt
is the AMD path and how to restore it (re-run / check network / set
UNSLOTH_LLAMA_RELEASE_TAG), so AMD never silently degrades to CPU.
No behavior change on the happy path: AMD still gets the GPU prebuilt (verified
on gfx1151: ggml-hip.dll bundled, ~80% GPU compute during inference). NVIDIA
(CUDA source build) and CPU-only hosts are unchanged.
setup.ps1 parses; install suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* uninstall: remove shared llama.cpp build, kill lock-holders, match WSL shortcut
Three gaps found by running a real uninstall on a native-Windows + WSL host;
all fixes are scoped to Unsloth-owned paths and no-op on the other pathways
(env/custom-root, NVIDIA/AMD/CPU, Mac) so nothing else regresses.
uninstall.ps1:
- Remove the default-mode SHARED llama.cpp build + cache. setup.ps1 installs
them at ~/.unsloth/llama.cpp and ~/.unsloth/.cache -- SIBLINGS of studio,
not under it -- so deleting <studio> left hundreds of MB behind. Now removed
explicitly, then ~/.unsloth is dropped ONLY if empty (never nukes unrelated
content). No-op in env/custom mode (llama.cpp nests under the custom root,
removed already) and when absent. UNSLOTH_LLAMA_CPP_PATH (user-owned) is kept.
- New _StopProcessesLockingRoots: _StopStudioProcesses only matched the venv
unsloth/python/studio exe, so it missed (a) llama-server.exe under llama.cpp
and (b) an orphaned multiprocessing python fork that ran from the SYSTEM
python but loaded a venv DLL (bitsandbytes) -- on Windows an open DLL handle
blocks the directory delete, leaving a half-removed install. The new helper
kills any process whose image path OR loaded module is under a target root
(module scan scoped to python/unsloth/llama-server names; vendor-agnostic).
- _RemovePath now retries (transient post-kill handle release).
uninstall.sh:
- Remove the default-mode ~/.unsloth/llama.cpp + ~/.unsloth/.cache; rmdir
~/.unsloth only if empty.
- WSL Windows-side shortcut cleanup now matches by TARGET (any
"Unsloth Studio*.lnk" whose target launches wsl.exe), covering both the
legacy "Unsloth Studio.lnk" and the new "Unsloth Studio (WSL - <distro>).lnk"
-- and never removes a native-Windows shortcut (which launches wscript.exe).
uninstall.ps1 parses; uninstall.sh passes sh -n and bash -n.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.ps1: invalidate Win11 Start Menu tile cache after creating shortcut
The Start Menu shortcut kept showing a blank/generic icon even after the
Explorer icon-cache rebuild, because Windows 11's StartMenuExperienceHost
keeps its OWN pre-rendered tile-icon cache
(%LOCALAPPDATA%\Packages\Microsoft.Windows.StartMenuExperienceHost_cw5n1h2txyewy\
TempState\TileCache_*.bin + StartUnifiedTileModelCache.dat), separate from
Explorer's iconcache_*.db. ie4uinit and an explorer.exe restart do not touch
it, and they don't recycle the host -- so a rewritten same-name shortcut keeps
showing the first-rendered (often the generic wscript ">") tile until the host
restarts on its own.
Fix: after creating the shortcut, drop only the Start Menu RENDER caches
(TileCache_* + StartUnifiedTileModelCache.dat) and stop StartMenuExperienceHost
(Windows auto-relaunches it), so the tile re-resolves the real icon via the
shell image factory. start2.bin (the user's pinned layout) is deliberately
preserved. Guarded by Test-Path (Windows 10 has no such host -> skipped) and
wrapped in try/catch so it can never fail the install. Windows-only
(install.ps1); no effect on Linux/macOS/Studio.
Verified live: rendering the shortcut via IShellItemImageFactory::GetImage (the
API StartMenuExperienceHost uses) returns the Unsloth sloth icon, color-matched,
after this invalidation -- previously it returned the generic script tile.
install.ps1 parses; install suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* ROCm-on-WSL for AMD Strix Halo (gfx1151): auto-setup + runtime enablement
Make Unsloth Studio set up ROCm-on-WSL automatically for AMD Strix Halo
(Radeon 8060S / gfx1151) and use the GPU at runtime, validated end-to-end
on a Ryzen AI Max+ PRO 395 (ROCm 7.2.1 + librocdxg + Adrenalin Apr-2026):
rocminfo enumerates gfx1151, torch.cuda True, ~85.8 GB UMA pool.
Every change is a strict no-op for all other configs (NVIDIA/CUDA,
discrete + native-Linux AMD ROCm, macOS/MLX, Windows, CPU-only, non-Strix
WSL) and can never abort the installer.
- scripts/install_rocm_wsl_strixhalo.sh: rewrite to the validated recipe.
Fixes that would have broken a working box: drop the /usr/lib/wsl/lib
preflight (a working ROCDXG host has only d3d12/dxcore there); remove the
obsolete rocr4wsl step (gone from the 7.2.1 repo; would hard-fail and also
rips out the standard hsa-rocr ROCDXG needs); dynamic librocdxg soname
(was hardcoded 1.1.0; build is 1.2.0); direct apt-repo install; Windows
SDK auto-discovery; persist env to /etc/profile.d + ~/.bashrc; idempotent.
- install.sh: _maybe_bootstrap_rocm_wsl auto-offers/runs the helper when it
detects a Strix Halo APU in WSL (/dev/dxg) with no ROCm runtime, then
loads the env so detection routes to the gfx1151 wheels. Fast-path when
already configured. Fix an inaccurate WSL hint line.
- studio/backend/main.py + worker.py: set HSA_ENABLE_DXG_DETECTION=1
in-process before torch (gated on /dev/dxg AND librocdxg.so), so the
worker uses the GPU even when launched outside a login shell. Mirrors the
existing BNB_ROCM_VERSION injection.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* uninstall: clean up ROCm-on-WSL artifacts + Start Menu tile cache
- uninstall.sh: remove the ROCm-on-WSL helper artifacts -- the librocdxg
build clone (~/.unsloth/librocdxg, which otherwise blocks the empty-dir
rmdir of ~/.unsloth), the throwaway smoke-test venv, the persisted env
(/etc/profile.d/unsloth-rocm-wsl.sh) and the ~/.bashrc block. The system
ROCm userspace is a shared prereq like CUDA and is kept by default;
UNSLOTH_UNINSTALL_ROCM=1 removes it too. No-ops on macOS / non-Strix Linux.
- uninstall.ps1: invalidate the Win11 Start Menu tile cache after removing
the shortcut so its tile disappears promptly (mirrors install.ps1),
preserving start2.bin.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: accurate AMD ROCm messaging (HIP SDK optional, not required)
The Windows installer printed "HIP SDK not found - GPU-accelerated training
unavailable" / "ROCm wheels require the HIP SDK" whenever the HIP SDK was
absent. That is misleading: for a detected AMD GPU arch (gfx1151 etc.),
setup.ps1 installs AMD's bundled-runtime ROCm PyTorch wheels (repo.amd.com)
which ship their own ROCm runtime and do NOT need the HIP SDK -- verified
end-to-end (torch 2.11.0+rocm7.13.0, cuda True, QLoRA training on GPU) on a
Radeon 8060S with no HIP SDK installed.
Gate the GPU-detection + rocm-step messages on a detected gfx arch: when one
is known, state that GPU PyTorch uses bundled-runtime wheels and the HIP SDK
is optional; only when the arch is unknown fall back to the HIP-SDK hint.
Behavior (torch routing) is unchanged; this is messaging only. No-op for
NVIDIA/CUDA, HIP-SDK-present, and CPU paths (they hit earlier branches).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: fix /opt/rocm data-loss + make WSL shortcut create/remove interop-robust
Two fixes from the 3-reviewer regression audit + live testing on a
systemd-enabled WSL distro (interop disabled):
F1 (data-loss, install_rocm_wsl_strixhalo.sh): the /opt/rocm symlink-repair
could force-delete a pre-existing REAL ROCm install. The guard only checked
that /opt/rocm is a real directory, not that it is the stray librocdxg stub.
Now it only touches /opt/rocm when it is NOT a real install (no bin/rocminfo,
bin/hipcc, or .info/version present), and MOVES it aside (rocm.unsloth-stub-bak)
instead of deleting it, so a wrong guess can never lose data.
WSL interop robustness (install.sh + uninstall.sh): both relied on
`command -v powershell.exe`, which is true even when WSL interop cannot EXECUTE
it (on systemd distros powershell.exe fails with "Exec format error"). Result:
the WSL shortcut silently failed to create (install) and to remove (uninstall).
- uninstall.sh: test that powershell.exe actually runs; if not, remove the
"Unsloth Studio (WSL...).lnk" files directly via drvfs (/mnt/<drive>), which
works without interop. The name is WSL-install-specific, so a native install's
"Unsloth Studio.lnk" is never touched.
- install.sh: when the shortcut cannot be created, warn with the manual launch
command + how to re-enable interop, instead of failing silently.
No behavior change on the interop-on path. The regression audit otherwise found
no regressions on Linux/Mac/Windows/CPU/NVIDIA install paths.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.sh: fast-path fully restores ROCm-on-WSL env when the drop-in is gone
Reinstall regression found by uninstall->reinstall testing: after a Studio
uninstall that removed /etc/profile.d/unsloth-rocm-wsl.sh but KEPT the shared
ROCm (the default), a non-login reinstall hit the bootstrap fast-path
(librocdxg present) and its else-branch only set HSA_ENABLE_DXG_DETECTION --
NOT PATH/LD_LIBRARY_PATH. So rocminfo was not on PATH, GPU detection failed,
and the installer fell back to CPU-only PyTorch.
Fix: when librocdxg is present but the env drop-in is missing, restore the
FULL env inline (HSA + TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL + PATH +
LD_LIBRARY_PATH) so rocminfo is found and detection routes to the GPU, and
recreate /etc/profile.d/unsloth-rocm-wsl.sh so future shells and the Studio
worker get it too. No change to the env-present fast-path or any other host.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: clear Explorer icon cache so shortcut icons aren't blank
Root cause of the persistent blank Desktop + Start Menu icons: Explorer caches
each shortcut's icon in iconcache_*.db and does NOT re-read the .ico when a
same-name .lnk is recreated across reinstalls. The .ico and .lnk are correct
(the shell renders them non-blank via IShellItemImageFactory; the .ico has real
image data at 16/32/48/128 px), but the stale cache entry wins. The previous
fix only ran a weak `ie4uinit -show` + the Start Menu tile-cache clear -- it
never invalidated Explorer's icon cache, so the desktop icon stayed blank.
Fix (native install.ps1 New-StudioShortcuts AND the WSL shortcut path in
install.sh):
- ie4uinit -ClearIconCache (thorough; replaces -show as the primary refresh)
- SHChangeNotify(SHCNE_ASSOCCHANGED) to force a live desktop/taskbar refresh
WITHOUT restarting explorer
- keep the Win11 Start Menu tile-cache invalidation (and add it to the WSL
shortcut path too, preserving start2.bin)
Non-disruptive (no explorer restart). install.ps1 parses clean; install.sh
passes bash -n + dash -n; the heredoc-generated WSL PowerShell parses clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: per-item SHChangeNotify(UPDATEITEM) reliably fixes blank icons
The blank Desktop/Start Menu shortcut icons are a stale Explorer PER-ITEM icon
cache: when a same-name .lnk is recreated across reinstalls, Explorer caches the
previously-resolved (often generic "white page") icon for that item and won't
re-extract the .ico on its own. The .ico and the .lnk's IconLocation are correct
(every icon API renders the sloth) -- only Explorer's cached display is stale.
The previous refresh (ie4uinit -ClearIconCache + a GLOBAL SHCNE_ASSOCCHANGED
broadcast) does NOT recover a stale item -- confirmed by reproduction. The
reliable, NON-disruptive fix (no explorer restart) is a PER-ITEM
SHChangeNotify(SHCNE_UPDATEITEM, SHCNF_PATHW, <lnk path>) for each created
shortcut, which forces Explorer to re-read that exact item's icon.
Verified end-to-end: deliberately staled a shortcut to the generic icon, ran the
installer's exact new refresh code, and the sloth icon recovered with NO explorer
restart (confirmed by capturing the live desktop via PrintWindow).
Applied to both native install.ps1 (New-StudioShortcuts) and the WSL shortcut
path in install.sh. Still clears the on-disk icon cache (ie4uinit) and the Win11
Start Menu tile cache (preserving start2.bin).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* uninstall: remove leftover llama.cpp .staging root so ~/.unsloth is cleaned
The llama.cpp atomic-install staging root (install_llama_prebuilt.py
INSTALL_STAGING_ROOT_NAME=.staging) is a sibling of the llama.cpp install
dir (~/.unsloth/.staging in default mode). It is normally pruned after a
successful activate, but an interrupted or retained build can leave a
<name>.staging-XXXX tree behind. The uninstallers removed llama.cpp and
.cache but not .staging, so the final empty-dir cleanup of ~/.unsloth failed
and the directory lingered. Reproduced on WSL (Ubuntu-24.04) where an empty
llama.cpp.staging-XXXX dir kept ~/.unsloth alive after uninstall.
Remove ~/.unsloth/.staging in both uninstall.sh and uninstall.ps1. No-op in
env/custom mode (staging nests under the custom root removed already) and
when absent. Cross-platform fix (the staging logic is platform-agnostic).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: WSL-absent hint + fix here-string lint false positive
install.ps1: in the AMD WSL-ROCm driver hint, detect when wsl.exe is absent
and add a one-line "wsl --install -d Ubuntu-24.04" pointer so a Strix Halo
user with no WSL yet gets an actionable next step (the hint previously assumed
an Ubuntu-24.04 distro already existed). Best-effort, informational only.
test_rocm_support.py: test_no_here_strings did a crude substring check that
false-positived on the conda-style block marker
printf '# <<< Unsloth ROCm-on-WSL (gfx1151) <<<' -- a string literal written
into the /etc/profile.d drop-in, also used as a sed delimiter pair by
uninstall.sh, not a here-string. Strip quoted spans before the check so the
lint still catches a real here-string operator but ignores quoted literals.
install.sh remains POSIX-clean (sh -n / dash -n / bash -n all pass).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* installer: address PR review comments (gfx1150 mapping, amd-smi opt-out, WSL bootstrap, SDK path, make)
Apply the valid bot review findings on #5940; reject the ones that don't hold.
Fixed:
- AMD name->gfx table (setup.ps1 + install.ps1): Radeon 890M and Ryzen AI 9 HX
370/375 are Strix POINT (gfx1150), not Strix Halo (gfx1151). Move 890M / HX 37x
/ AI 9 HX to the gfx1150 row and drop the bogus HX 38x pattern (no such Strix
Halo SKU). Matches the runtime classifier in worker.py (890M/880M -> gfx1150;
8060S/8050S -> gfx1151). Prevents Strix Point hosts from getting the wrong ROCm
prebuilt/wheels.
- amd-smi opt-out (setup.ps1 + install.ps1): an explicit UNSLOTH_ENABLE_AMD_SMI=
0/false/no/off now wins over the HIP-SDK heuristic, so a host with a HIP SDK
binary but a broken runtime no longer gets the DiskPart/UAC prompt the opt-out
exists to avoid.
- amd-smi warning probes (install_python_stack.py): _has_rocm_gpu and
_detect_amd_gfx_codes now gate amd-smi behind _amd_smi_allowed() (and pass
_amd_smi_env()), closing the last unguarded amd-smi spawn on Windows.
- WSL ROCm bootstrap (install.sh): the "already-usable ROCm?" early return now
requires rocminfo to enumerate the real gfx1151 agent instead of the generic
_has_amd_rocm_gpu (whose broad gfx[1-9][0-9] match accepts a fallback
"gfx11-generic" ISA), so a Strix Halo box missing the ROCDXG bridge is no longer
skipped. The shared helper is untouched (no gfx90a regression).
- install_rocm_wsl_strixhalo.sh:
* Quote-safe Windows SDK discovery: the old for-in-$(ls -d "...Program Files
(x86)/...") word-split on the space and never matched; use find + read loop.
* Add `make` to apt prereqs (cmake only recommends it; minimal images lacked it
and the librocdxg `make -j` build failed).
* Verification requires gfx1151 exactly (not gfx1[0-9]) so a generic ISA or an
unrelated RDNA GPU can't pass while the real GPU is absent.
Reviewed but NOT changed:
- "Forward inferred ROCm arch without HasROCm" (setup.ps1): already correct --
--rocm-gfx is forwarded under `if ($script:ROCmGfxArch)`, not `if ($HasROCm)`.
- "Route inferred arch into install.ps1 torch path": not a bug -- install.ps1
installs CPU torch as a base by design and setup.ps1 swaps in the ROCm wheel for
the inferred arch (gate `($HasROCm -or $ROCmGfxArch) -and cpu`); verified live
the native install ends on torch 2.11.0+rocm7.13.0.
- "$p null guard after Start-Process" (install.ps1/setup.ps1): redundant -- the
amd-smi runner uses [Process]::Start wrapped in try/catch, so a null process
already returns "" with LASTEXITCODE=1 (no uncaught exception).
- "ls -> find for /usr/lib/wsl/lib" (gemini): stale -- that heuristic was removed;
only a comment about it remains.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer(rocm-wsl): auto-install the Windows 11 SDK via winget (fewer manual steps)
librocdxg's build needs the Windows SDK 'shared' headers on the Windows host.
Previously the helper just die()d with "install the Windows 11 SDK and re-run" if
they were missing -- a manual prerequisite that broke the otherwise-seamless
`curl ... install.sh | sh` one-liner on Strix Halo.
Now, when the headers aren't found, the helper installs the Windows 11 SDK on the
Windows host from inside WSL via winget (powershell.exe interop), then
re-discovers them. The SDK installer elevates -> ONE UAC prompt on the Windows
desktop; the headers appear under /mnt/c immediately (drvfs is live, no reboot).
The user already consented to the ROCm-on-WSL setup, so no extra prompt is added
beyond the OS UAC gate.
- New _find_win_sdk (space-safe find of the newest installed SDK 'shared' dir)
and _install_windows_sdk_via_winget helpers.
- winget IDs tried newest-stable first: Microsoft.WindowsSDK.10.0.26100, then
.22621. The presence of the headers (re-check) is the source of truth, not
winget's exit code. </dev/null so winget never consumes a piped `curl|sh` stdin.
- Best-effort + non-fatal: interop-off / no-winget / declined-UAC all fall
through to the existing clear manual-install die(). Opt out with
UNSLOTH_SKIP_WIN_SDK_INSTALL=1.
Removes the last avoidable manual step from the WSL Strix Halo path; only the AMD
Adrenalin driver (AMD referrer-gates the download) remains manual. Verified
_find_win_sdk resolves the spaced "Program Files (x86)" path; bash -n clean; all
winget flags validated against `winget install --help`.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer(amd): gate install-time amd-smi probe to fix DiskPart UAC prompt
install_python_stack.py's Windows "AMD GPU detected but ROCm torch missing"
warning probe ran `amd-smi list` whenever amd-smi was on PATH -- and amd-smi
ships in C:\Windows\System32 with the AMD Adrenalin driver -- without the
_amd_smi_allowed() gate that every other amd-smi call site in the file uses.
On Adrenalin-only hosts (no HIP SDK) amd-smi elevates a child at runtime and
pops a UAC/DiskPart prompt that __COMPAT_LAYER=RunAsInvoker cannot suppress
(amd-smi's manifest is asInvoker). The probe also ran before the
ROCm-torch-installed check, so it fired on every Windows AMD install.
Gate it behind _amd_smi_allowed() and pass _amd_smi_env(), matching
_has_rocm_gpu()/_detect_amd_gfx_codes(). When skipped, the only loss is the
best-effort "AMD GPU detected" note on HIP-SDK-less hosts.
Adds a per-function AST regression test asserting every function in
install_python_stack.py that names the amd-smi command and spawns a subprocess
also references _amd_smi_allowed() (flags the pre-fix code; passes after).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* studio(cli): fix `unsloth studio stop` crashing on Windows
`stop` used the POSIX `os.kill(pid, 0)` liveness probe, but on Windows
CPython raises OSError (WinError 87, "The parameter is incorrect") for
*every* pid -- alive or dead. `stop` only catches ProcessLookupError /
PermissionError, so the OSError propagated and the command crashed with
a traceback before ever reaching its (correct) `taskkill /F` path.
Add a cross-platform `_pid_alive(pid)` helper (tasklist on Windows,
signal-0 elsewhere) and use it for both the pre-check and the post-kill
wait loop. The actual kill path is unchanged.
Verified on Windows (Python 3.13): os.kill(pid,0) raises WinError 87 for
both a live and a dead pid; `_pid_alive` returns True/False correctly and
the full stop() flow (alive -> taskkill -> dead -> "stopped") passes
end-to-end against a throwaway process.
Adds tests/studio/test_cli_studio_stop_windows.py (AST guard against a
bare os.kill(pid,0) liveness probe + mock-only _pid_alive behaviour for
the win32 tasklist branch and the POSIX signal-0 branch).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* installer(amd): fix install.sh name->arch table misrouting Strix Point to gfx1151
The bash name->arch inference table in install.sh placed Strix Point
identifiers (Radeon 890M, "Ryzen AI 9 HX 370/375", "AI 9 HX") in the
gfx1151 (Strix Halo) row, diverging from the install.ps1 / setup.ps1
PowerShell tables which correctly map them to gfx1150. It also carried a
stray "HX 38" token absent from the PowerShell source-of-truth.
Align install.sh with the PowerShell tables:
gfx1151 row: 8060S|8050S|8040S|Strix Halo|Ryzen AI Max|AI Max
gfx1150 row: 890M|880M|860M|840M|Strix Point|Krackan|HX 37|AI 9 HX|...
Impact is low (the bash table only feeds the display label _gpu_disp_gfx
and the "set UNSLOTH_ROCM_GFX_ARCH=..." hint; wheel selection is driven
by the detected ROCm version, not this name string) but a Strix Point
user would otherwise see/copy the wrong gfx arch.
Add a parity test (test_install_sh_name_arch_agrees_with_ps_for_strix_and_non_amd)
that parses install.sh's case table and asserts Strix Halo->gfx1151,
Strix Point->gfx1150, RX 7700S->gfx1102, and NVIDIA/Intel->no match,
cross-checking against install.ps1 (the previous parity test only
compared install.ps1 <-> setup.ps1, missing install.sh).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* setup.ps1: keep prebuilt-llama ownership guard within the test's block window
The AMD additions to the prebuilt-llama.cpp block (the windows-hip vs
windows-cpu existing-install kind validation) pushed the
install_llama_prebuilt.py invocation to ~1999 chars after the
"installing prebuilt llama.cpp bundle (preferred path)" anchor, right at
the edge of the 2000-char window that
test_setup_ps1_prebuilt_llama_cpp_has_ownership_guard slices -- so the
helper string was truncated and the test failed with "substring not
found" (CI: Repo tests (CPU)).
The ownership-guard invariant (Assert-StudioOwnedOrAbsent precedes the
install_llama_prebuilt.py call) was already satisfied; only the proximity
to the anchor regressed. Move the "installing prebuilt..." substep to
immediately before the install (after the existing-install pre-cleanup),
which also reads better (validate/clean existing -> then "installing"),
shrinking anchor->helper from 1999 to 413 chars. Behaviour is unchanged
(console message ordering only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.sh: auto-run Strix Halo ROCm-on-WSL setup by default
`curl -fsSL https://unsloth.ai/install.sh | sh` should make a Strix Halo
(gfx1151) GPU usable inside WSL with no extra commands. Previously the
ROCm-on-WSL bootstrap was opt-in: it required UNSLOTH_ROCM_WSL_AUTO=1 or an
interactive [Y/n] at a TTY, and silently skipped under a pipe (no /dev/tty),
so the piped one-liner never set the GPU up automatically.
Flip it to auto-by-default for the single narrow case the existing guards
allow (WSL + Strix Halo + /dev/dxg + no usable ROCm yet) -- exactly the GPU
setup the user ran the installer for. Opt out with
UNSLOTH_SKIP_ROCM_WSL_SETUP=1. The Tauri desktop app keeps its own consent UI
(only auto-runs when it passes UNSLOTH_ROCM_WSL_AUTO=1). All hardware/OS
guards are unchanged, so non-Strix / non-WSL / NVIDIA / native-Linux / macOS /
CPU paths are unaffected.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* PR comments: condense to be succinct (comments/docstrings only)
Shorten the verbose explanatory comments and docstrings this PR added across
the installer, scripts, backend shims, CLI, and tests -- tighter, fewer lines,
while preserving every non-obvious "why" (os.kill WinError 87, amd-smi
RunAsInvoker/UAC, /dev/dxg + librocdxg gating, the ROCm-on-WSL bootstrap guard
chain, ownership guards, etc.). No executable code, string literals, messages,
or behavior changed.
Verified comments-only: docstring-normalized AST equality (Python, 9 files),
non-comment token equality (PowerShell, 3 files), comment-stripped diff +
sh -n / bash -n (shell, 3 files). Behavior re-confirmed: get_torch_index_url +
gfx name->arch table 44/44 under dash & bash; rocm_support / pr5940_followups /
cli_studio_stop tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Installer: address PR review (amd-smi opt-out, pipefail, multi-distro, non-root)
Fixes valid findings from the Codex/Gemini PR review:
- install.ps1 / setup.ps1: gate the `amd-smi version` ROCm-version fallback with
$amdSmiAllowed so UNSLOTH_ENABLE_AMD_SMI=0 opt-out is honored (the device
probe was gated but this fallback wasn't), avoiding the DiskPart/UAC prompt.
- install_rocm_wsl_strixhalo.sh: make the post-verification rocminfo summary
best-effort (|| true) so head's early pipe-close under `set -o pipefail` can't
fail the bootstrap after gfx1151 was already enumerated; pin the Windows SDK
`winget install` to --source winget (matches the msstore-cert fix rationale).
- install.ps1: python.org fallback installs the py launcher per-user
(InstallLauncherAllUsers=0, avoids admin), and derives the fallback full
version from the requested minor so a non-default UNSLOTH_PYTHON (e.g. 3.12)
isn't silently replaced with 3.13 when the listing is unreachable.
- install.sh: recreate /etc/profile.d/unsloth-rocm-wsl.sh via `sudo tee` for a
non-root reinstall (a plain redirect failed silently, dropping the ROCm env).
- uninstall.sh: scope WSL Windows-side shortcut removal to the current
WSL_DISTRO_NAME (per-distro name or -d "<distro>" arg) so uninstalling one
distro no longer deletes other distros' launchers.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Studio ROCm Windows: fix field-reported issues from Strix Halo testers
Four fixes from PR #5940 field reports (Win11 native, gfx1151):
1. bitsandbytes arch-probe spam: bnb's get_rocm_gpu_arch() runs
hipinfo.exe via subprocess PATH at import; the AMD torch wheel ships
hipInfo.exe in the venv Scripts dir, which is only on PATH for
activated venvs. Every bnb import logged "Could not detect ROCm GPU
architecture: [WinError 2]" ERROR + WARNING (even with the HIP SDK
installed, whose bin dir is not on PATH either). Prepend the Scripts
dir to PATH before bnb imports in main.py, worker.py, and
install_python_stack.py, gated on the file existing (only AMD wheels
ship it). Verified on gfx1151: ROCM_GPU_ARCH now resolves to gfx1151
with zero errors.
2. OOM-guard double-tax on native Windows unified APUs: mem_get_info's
total is the WDDM budget the driver grants HIP (BIOS carve + ~half
of remaining RAM) -- the OS share is already outside it. The 0.80
unified cap on top denied loads that fit (field report: 48.49 GiB
budget -> "38.79 GiB allowed" OOM for a 47.29 GiB load with 48.08
free). Use 1.0 on win32 unified; Linux keeps 0.80, discrete 0.90.
3. "Missing VRAM" confusion: log the WDDM budget vs physical RAM with
the fix (BIOS UMA frame buffer / AMD Software Variable Graphics
Memory) when the grant is under 75% of RAM, so a 48 GiB cap on a
96 GiB box reads as policy, not a Studio bug.
4. llama-server fit-step crash (Qwen3.6-27B-MTP + mmproj, lemonade
gfx1151): --fit defaults to 'on' upstream, so the fit step runs even
when Studio already placed the model via -ngl -1, and aborts in
ggml-cuda.cu on some ROCm hosts. Retry the spawn once with --fit off
when the server crashes during startup and Studio's own VRAM math
had placed the model (never when use_fit or an explicit fit flag was
passed). Also keep the TAIL of crash output in the error log (the
diagnostic line prints last; head-truncation cut exactly that) and
reference the full on-disk log.
Verified live on Radeon 8060S: bnb import clean, Qwen3.5-4B-MTP loads
and generates through the new spawn loop, stub-crash retry appends
--fit off and recovers, fraction probes confirm WDDM overcommit and
sub-1.0-only enforcement on current AMD wheels.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio ROCm Windows: GPU-name fallbacks so nothing depends on amd-smi
amd-smi does not reliably exist on Windows: the HIP SDK never ships a
CLI, inbox Windows Update drivers do not, and only some full Adrenalin
packages drop amd-smi.exe into System32 (field report: fresh Win11 +
Adrenalin + HIP SDK, still no amd-smi anywhere). Make every consumer
work without it:
- install_python_stack._detect_windows_gfx_arch: two new probes after
hipinfo/amd-smi -- (2b) the venv Scripts hipInfo.exe shipped by AMD
torch wheels (drives `studio update` on driver-only hosts), and (4) a
last-resort GPU marketing-name -> gfx table via WMI
(Win32_VideoController), mirroring setup.ps1's $nameArchTable so a
standalone repair resolves the arch with zero AMD tooling installed.
- install_llama_prebuilt._resolve_exe: also probe the venv Scripts dir
so a standalone rerun finds hipInfo.exe without HIP_PATH.
- hardware/amd.py _run_amd_smi: which() guard before spawning --
absence now disables the poller in one step instead of burning the
3-strike circuit breaker on FileNotFoundError; corrected the stale
comment claiming Adrenalin ships amd-smi.
Simulated against the real detection functions on gfx1151: amd-smi
absent, present-but-crashing (exit 1), present-but-hanging (60s sleep
vs 5-10s probe timeouts), and hard opt-out -- all resolve gfx1151, no
exceptions, bounded time. Full adversarial install (broken amd-smi
stub first on PATH + UNSLOTH_ENABLE_AMD_SMI=1, fresh uninstall first):
exit 0, name-table arch inference, lemonade gfx1151 b1292 prebuilt,
torch 2.11.0+rocm7.13.0 cuda_avail=True on the 8060S, Studio boots
healthy and stops cleanly.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: per-attempt llama-server log names + amd-smi test portability
Found by cross-platform simulation of the --fit off retry (Windows +
Linux sandboxes, real load_model with stub servers):
- llama-server log filename now carries the spawn-attempt index. The
retry can respawn within the same epoch second; reusing the name
opened the same file with "w" and truncated the crash log the retry
warning had just pointed the user at (proven with a frozen
time.time: one file, crash evidence gone; with the suffix both
attempts keep their logs). Regression-pinned in
test_llama_cpp_wait_for_health.py.
- test_amd_primary_gpu_with_mock now mocks shutil.which alongside
subprocess.run: the amd-smi absence guard which()-checks before
spawning, so on hosts without a real amd-smi (Linux CI, driver-only
Windows) the subprocess mock was never reached and the test failed.
Surfaced by running the suite in a clean Linux sandbox.
Simulation coverage on both OSes: 67-case platform/edge matrix
(real shipped code blocks under win32/linux/darwin spoofs: OOM-guard
fractions + VGM-hint boundary, bnb PATH-prepend gates, retry
eligibility incl. equals-forms and decoy tokens, GPU-name table
adversarial set, WMI fallback without powershell, monitor absence
semantics), 6-scenario live retry matrix (crash-once/crash-always/
exit-zero/explicit-fit/hang/log-collision) against real llama-server
spawns on Windows and WSL (GPU success legs on the 8060S), and a
3-engine browser matrix (chromium/firefox/webkit) driving the live
backend's health + authed /v1 chat completion.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: classify unified-memory via props.is_integrated first
Align the ROCm OOM-guard classifier with PR #5988's UMA gate: consult
hipDeviceProp_t.integrated (props.is_integrated) before the hardcoded
arch set. Strictly additive -- truthy upgrades to unified; 0/absent
falls through to the existing gfx1150/gfx1151 + device-name logic, so
wheels that omit or zero the field cannot downgrade the known APU set.
Extends correct unified-cap treatment to APUs outside that set (e.g.
gfx1103 Phoenix iGPUs) and keeps Studio's two unified-memory consumers
on one driver signal. Verified live on gfx1151 (is_integrated == 1 on
the AMD Windows wheel -> ('gfx1151', True) via the new path).
* AMD detection: probe rocminfo with HSA_ENABLE_DXG_DETECTION and sync setup.sh gfx table
Fleet validation on a Strix Halo WSL2 box showed the system rocminfo
(HSA 1.18, ROCm 7.2.1) only enumerates the GPU over /dev/dxg when
HSA_ENABLE_DXG_DETECTION=1, and that rocminfo can sit at /opt/rocm/bin
off PATH outside login shells. Detection probes that miss either of
these report no GPU on a working ROCDXG host and select the CPU build
even though the lemonade bundle offloads fine (95.7 tok/s measured vs
64.5 CPU on the same laptop). Seed the env (a no-op on bare metal) and
the PATH fallback in install.sh, studio/setup.sh, and the installer's
Linux rocm probe, mirroring what main.py/worker.py already do for the
runtime.
Also sync studio/setup.sh's name->gfx table with install.sh: 890M and
the HX 37/AI 9 HX SKUs are Strix Point (gfx1150, not gfx1151), RX 7700S
must match gfx1102 before the gfx1100 row, and the RDNA2/workstation
rows were missing. New parity test pins the two bash tables together so
they cannot drift again.
* Studio: persist server session logs + native-crash stacks to disk
Field report (Strix Halo, 96 GB UMA carve, WSL and native Windows):
"the studio just terminates without a warning". A native crash in the
GPU runtime kills the process with no Python traceback, and a desktop-
shortcut console closes before anything can be read. The server only
ever logged to the console, so there was nothing to send back.
run_server now tees stdout/stderr to
~/.unsloth/studio/logs/server/server-<ts>-pid<n>.log (console behavior
unchanged; file copy is best-effort), arms faulthandler at the same
file so access violations / SIGSEGV leave a stack trace on disk, and
exports PYTHONFAULTHANDLER=1 so training workers inherit crash dumps
on their captured stderr. Armed before `from main import app` so even
import-time failures leave evidence. Keeps the newest 20 session logs;
opt out with UNSLOTH_STUDIO_NO_FILE_LOG=1. Prints "Session log: <path>"
at startup so users know what to attach.
Verified on this box: a forced real segfault (faulthandler._sigsegv)
leaves the full session output plus "Fatal Python error: Segmentation
fault" and the thread stack in the file while the console shows
nothing; a normal server boot captures the startup banner and serves
health as before.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* AMD probe: honor a pre-set HSA_ENABLE_DXG_DETECTION value
Match the shell helpers, which use the parameter-default form: a user
who exports HSA_ENABLE_DXG_DETECTION=0 to deliberately hide the GPU
from DXG detection should not have the probe override it.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Adds a self-contained RAG stack to Studio: knowledge bases with chunked indexing, hybrid (dense + lexical) retrieval, and an automatic first-pass context inject into chat. Embeddings run through a local llama-server GGUF backend (default unsloth/bge-small-en-v1.5-GGUF) with a sentence-transformers fallback. The chat tool loop gains a search_knowledge_base tool, a per-turn re-search cap, and source citation, layered on top of the shared ToolLoopController.
* Studio: merge developer prompts for GGUF chat
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: fix OpenAI- and Anthropic-compatible API spec compliance
* Studio: fix API spec-compliance gaps on passthrough and streaming paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: carry context_length_exceeded through the OpenAI passthrough error path
* Studio: count tool-schema tokens in the Anthropic server-tool stream, and small stream-handling guards
* Studio: guard message_delta usage against None and normalize developer role before proxying
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: honor max_completion_tokens on the external-provider proxy path
* Studio: forward llama-server cached_tokens into OpenAI prompt_tokens_details
* Studio: sanitize messages in count_tokens to match the /v1/messages prompt
* Studio: report max_tokens for truncated tool calls and guard null usage in metadata events
* Studio: drop the request-id middleware (headers aren't declared in either spec)
* Studio: include the required request_id field in Anthropic error bodies
* Studio: honor max_completion_tokens on the audio (TTS / audio-input) paths
* Studio: add the _effective_max_tokens helper and route all max-token sites through it
* Studio: align API compatibility edge cases
* Studio: clarify multi-choice chat support
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: clarify logprobs chat support
* Studio: opt the local chat UI into the streaming usage chunk so the context bar and tok/s repopulate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: forward seed to llama-server, and fix Anthropic server-tool stop_reason, tool_result id correlation, and parallel-tool execution cap
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: align OpenAI chat completion spec edge cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: align backend API compatibility tests
* Studio: honor tool caps and internal stream usage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: coerce nullable stream usage counts
* Studio: preserve system prompts with developer messages
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Unify the Studio local tool-call loop (GGUF + safetensors) behind a shared ToolLoopController: ordered preface-then-tool-card rendering, duplicate-call de-looping with a forced final answer, XML-leak containment, and a parser fix that accepts closed <function=...> calls followed by trailing prose. Includes backend tests for the controller, strict parser, and GGUF route cursor reset.
* Studio: make Helper LLM startup pre-cache opt in
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Adds the Studio Hub and download manager: browse Hugging Face models and datasets, download GGUF and safetensors with live progress and cancellation, and manage on-device inventory. The Hub does not require a GPU, so it is available on chat-only hosts.
CI: all substantive checks pass, including the three Core jobs after unsloth-zoo#736. The two red checks are non-code flakes, a transient npm-registry DNS resolution failure in the package scan and one quantized vision-model output assertion whose sibling shards passed.
Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Trim and tighten code comments and docstrings across studio/ Python. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Raise ruff line-length to 100 and extend the local pre-commit format pipeline (def-signature magic-comma normalization, short multi-line assert collapse, kwarg '=' spacing, blank-line-after-short-import removal, adjacent string-literal / f-string+plain merge, redundant-pass pruning). Every transform re-checks the file AST and is dropped if it would differ; the whole-repo reformat is verified AST-identical per file and idempotent.
* fix: persist chat presets with fast mode
* Add schema drift guard test for chat inference settings (#5862)
Asserts ChatInferenceSettings declares every InferenceParams field the
frontend persists (all but checkpoint). With extra="forbid", a field
present in the UI but missing here 400s PUT /api/chat/settings, which is
exactly how fastMode regressed. Catches the next occurrence at CI time.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Normalize misplaced system-role messages in /v1/messages by hoisting their content into the top-level Anthropic system field, fixing the 422 that newer Claude Code clients trigger. Null and non-text system content is ignored rather than stringified.
Fixes#6001
* fix: enable GGUF tools with vision inputs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: GGUF vision tool routing
* Dedupe system messages on GGUF vision tool path for PR #6009
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: enable audio input for Gemma 4 GGUF models
Audio file upload was disabled for Gemma 4 vision+audio GGUFs (e.g.
gemma-4-12b-it-GGUF) even though their mmproj carries an audio encoder
(clip.has_audio_encoder, gemma4ua). Two causes:
- Audio-input detection only matched Gemma 3n's <audio_soft_token>;
Gemma 4 uses <|audio|>, so audio_vlm was never detected.
- The GGUF load/status responses hardcoded has_audio_input=False, so the
flag was dropped even when audio_vlm was detected (affected Gemma 3n
GGUFs too).
Changes:
- Recognize <|audio|> alongside <audio_soft_token> in the llama-server
token probe and the tokenizer-config pattern.
- Read clip.has_audio_encoder from the mmproj as an independent,
model-agnostic signal (read_mmproj_audio_capability).
- Emit the computed has_audio_input on the GGUF load/status responses.
- Tests for the new pattern and the mmproj reader.
* Studio: default chat model and dataset helper to Qwen3.5-4B-MTP
Switch the auto-loaded chat default and the dataset-analysis helper GGUF
from gemma-4-E2B-it to unsloth/Qwen3.5-4B-MTP-GGUF (UD-Q4_K_XL).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* studio: redesign chat composer
Reworks the new-chat composer and the compare composer into a single
rounded pill surface with a softer, lighter look.
- New welcome screen with a time-of-day sloth mascot and a lighter
heading.
- One rounded composer surface with a soft drop shadow. The input grows
inline as you type and collapses back to a single row when cleared.
- Tools and attachments live in a single plus menu; the thinking control
is a compact pill with a reasoning-effort submenu.
- Inlined glyphs for the thinking, send, and dictate controls, kept in
sync across the main and compare composers.
- Toast notifications match the composer surface: no border line, the
same drop shadow, and the same dark surface color, with a ring-less
close button.
- Dark mode: the side-menu shadow blends into the background, hovered
menu rows read clearly, and their roundness matches light mode.
- Composer styles use dedicated unsloth- prefixed classes so compare
mode keeps its own stacked layout.
* studio: sync compare-composer reasoning state and harden compare id
- Compare composer: keep "Preserve thinking" consistent with reasoning,
matching the main composer. Enabling it now turns reasoning on, and
disabling reasoning (the None option or the Thinking toggle) turns it
off, so the invalid "preserve on while thinking off" state can't occur.
- Guard crypto.randomUUID in the Compare action. It is undefined in
non-secure contexts (HTTP over a LAN IP) and would throw; fall back to
a timestamped random id, matching createNavigationNonce.
* studio: reflect pre-selected Search/Code tools when no model is loaded
The Search and Code pills only lit up when the tool was usable right now
(a model loaded and capable), so a tool turned on from the + menu showed
as off in the pill while the menu showed it on. toolsEnabled is persisted
and takes effect once a capable model loads, so the pill should reflect it.
The pills now disable only when a loaded model lacks the capability, and
otherwise reflect the selected state. Applied to the main and compare
composers.
* Studio: link MCP Servers heading to its PR and fix composer pill cursors
Make the "MCP Servers" heading in the chat Configuration sheet link to the
MCP PR, keeping the chevron as the toggle. The label and chevron are rendered
as siblings so we don't nest an <a> inside a <button>.
Also add cursor-pointer to the composer pills and the thinking pill so hovering
a clickable pill shows the hand cursor instead of the default arrow.
* Studio: refine chat composer and add compare-mode parity
- Composer expands to two rows only once the input wraps to a second line,
not on the first keystroke. Re-measure the autosize textarea on the width
swap so expanding no longer leaves a stray blank row.
- Light-mode composer shadow now matches Gemini's soft elevation.
- Plus menu: replace Canvas with a More submenu (Canvas, Compare chat, RAG)
and add Code above MCP. Active Web search/Code items use medium weight.
- Compare mode: the plus side menu, Search/Code toggles, and a Compare exit
pill now match single chat, with the thinking control on the right.
- Projects menu entries link to their tracking PR (#5725).
- Add cursor-pointer to the composer plus button.
* studio: refine composer controls and chat search shadow
- Active tool pills show an x on hover to signal click-to-disable
- Plus button rotates into an x when the tools menu opens
- Composer surface uses a 32px radius and a taller single-line height
- Even, ChatGPT-style spacing between the plus and tool pills in both the single and compare composers
- Send and mic circles resized and spaced, with the arrow centered in the circle
- Chat search box gets a borderless, soft Gemini-style shadow
* studio: size the pill hover x to match the icon it replaces
Cross-engine checks (Chromium, Firefox, WebKit) flagged the active-pill
hover x as a fixed 14px, so it popped smaller than the 19px Code icon.
Fill the glyph slot instead so the x tracks whatever icon it covers.
* studio: do not persist Kimi search/thinking mutual-exclusion in single composer
The single-chat composer flipped the other control off when toggling
search or thinking on Kimi, but without { persist: false }, so it
overwrote the user's saved preference. Match shared-composer and keep
the side effect session-only.
* studio: pointer cursor on model selector trigger and menu items
Add scoped marker classes so the model picker trigger and every
clickable element in its menu (tabs, model rows, delete, eject) show a
pointer cursor; disabled items stay not-allowed.
* studio: pass baseUrl when resolving reasoning caps in single composer
The docked composer omitted baseUrl, so a custom Gemini OpenAI-compat
gateway still advertised the native thinking ladder the backend cannot
honor. Pass selectedExternalProvider.baseUrl like the compare composer
so the resolver hides it.
* studio: grey side-menu hover, green pill hover, thinking hover x
- Plus side-menu items hover grey in light mode, not the green accent
- Thinking pill hovers green like the Search and Code pills
- The plain Thinking toggle shows an x on hover when active, matching
Search and Code; the effort dropdown trigger keeps its bulb
* studio: make the pill hover x a uniform size
The x filled the icon slot, so the wider Code chevron gave a bigger x
than Search and Compare. Pin it to a fixed 15px, centered, so every
pill's x matches.
* studio: broaden chat attachments, fix active hover color, gemini shadow
- Accept svg, source code and many text/config files as drag-and-drop
or picked attachments, matched by extension since their MIME is
unreliable; html keeps its own adapter
- Active (green) side-menu items keep their text and icon color on
hover instead of switching to the accent color
- Composer surface uses Gemini's soft centered shadow 0 0 20px rgba(0,0,0,0.04)
* studio: keep the thinking pill full height when icon-only
The inactive thinking pill has no label, so its flex row collapsed to
the icon height and the hover box looked short. Reserve one text line
(min-height: 1lh + padding) so it matches the Search and Code pills.
* studio: refine composer menu, drop overlay and greetings
- Open the MCP servers dialog directly from the composer plus menu
- Redesign the drag-and-drop affordance Gemini style, drop the badge and border, make the whole chat page a drop target
- Swap in Hugeicons for the RAG, attachment chip and new project icons
- Add time-based randomized welcome greetings, each matched to a fitting sloth
* studio: rename artifacts toggle to Canvas and make it opt-in
- Label the toggle Canvas everywhere, matching the plus menu
- Stop greying out the Canvas menu item; it toggles like the other items
- Only show the Canvas pill in the composer row once it is turned on, since it is less central than Search and Code
* studio: wire Canvas and MCP composer toggles, even out the pill row
- Open the MCP servers dialog from the menu, or toggle MCP on/off once a server is enabled
- Force MCP off when no server is enabled, so the toggle stays honest
- Show Canvas and MCP as opt-in pills that appear in the order they were toggled on
- Expand the composer and light up the pill when Canvas or MCP is on, like Search and Code
- Keep Compare directly after Code in the compare composer
- Use the same Code icon on both composers and give every pill an even icon slot
* studio: tidy composer toggle row and fix MCP enable/disable lifecycle
- Enable MCP automatically after a server is configured via the toggle flow
- Force MCP off everywhere once the last enabled server is removed
- Collapse the pill labels to icons only when more than 4 pills show, keeping Compare labelled
- Order Compare first in compare mode, before Search and Code
- Use the same Code icon and an even 19px icon slot across both composers
- Match the compare composer surface padding and send button inset to normal chat
* studio: revert compare composer padding change that cramped the input
Matching the surface padding to normal chat clipped the textarea text and
left a white strip on top. Restore the compare composer's own padding, which
gives proper top spacing. The send button inset fix stays.
* studio: center welcome greeting and soften composer scrollbar
Center the sloth and title together over the composer instead of
shifting the row left, which left the greeting sitting off to the side.
Keep the composer textarea scroll thumb faint by default and only darken
it when the thumb is hovered or dragged, so a tall draft no longer shows
a heavy dark rail.
* studio: match composer plus-menu tool gating to the pills
The new plus-menu tool entries did not carry the gating the visible pills
already enforce, so the menu and pills could disagree about a loaded
model's capabilities.
- Web search and Code menu items now disable when a loaded model lacks
the capability, while still allowing preselection with no model loaded.
- Enabling Web search from the menu on a Kimi model now flips thinking
off as a session-only change, since Kimi forbids search and thinking
together. This matches the Search pill.
- Added an Images menu item, shown only for image-generation models and
disabled until a model loads, so a short prompt has an entry point.
Applied to both the single-chat and compare composers.
* studio: round the active-pill hover x and even out pill padding
The hover x sat bare and the trailing label was tighter to the pill edge
than the leading icon, so the pill looked lopsided.
- Give the hover x a soft circular background that fills the icon slot,
matching the ChatGPT-style toggle and the icon it replaces.
- Add a little more trailing padding so the label and the leading icon
have even breathing room, and keep icon-only compact pills symmetric.
* studio: nudge the thinking bulb icon up by 0.5px
Bump the thinking lightbulb from 15px to 15.5px in the single-chat and
compare composers so it sits a touch larger next to the other controls.
* studio: drop the hover x circle on icon-only pills
When pills collapse to icon-only, the circle around the hover x is too
cramped in the small chip, so show a bare x there and keep the circle
only on the full-width labelled pills.
* studio: space the compare send button like normal chat
In compare mode the Thinking control sat right against the send button.
Match the normal composer's control spacing (gap-1.5 plus a send margin)
so Thinking has the same breathing room before send. The send button
keeps its 14px inset, so its position is unchanged.
* studio: make collapsed pill hover a circle, not a wide pill
Icon-only pills were wider than tall, so their rounded-full hover
highlight read as a fat rounded rectangle. Make the compact button a
square and center the glyph so the hover (and the x it reveals) sits in
a clean circle.
* studio: fix compare pane drops and audio picker lifetime
- Skip the page-level drop handler when the composer is hidden, so files
dropped on a compare pane are not swallowed by a hidden composer; the
shared compare composer keeps handling drops through its own dropzone.
- Build the audio file input on document.body instead of inside the plus
menu, so the menu closing on select no longer unmounts the input before
the OS picker returns and drops the file.
* studio/chat: stop projects list from white-screening on older backends
The projects list API returned data.projects directly, so a backend that
omits the field handed back undefined. useChatProjects cached that value,
then the next mount read undefined.length and crashed the whole chat page.
Default the projects and threads list APIs to an empty array and keep the
hook null-safe so a bad response can never poison the cache.
* studio/chat: align MCP dropdown with the + menu and add a chevron
Reuse the + menu surface (unsloth-plus-menu) for the MCP dropdown: rounded
corners, narrower width, neutral grey hover, and enabled rows shown as green
text with a right-aligned check instead of the emerald underlay. Add a
chevron to the MCP pill so it reads as openable, matching the Thinking pill.
* studio/chat: make MCP an opt-in pill and fix its dropdown placement
- MCP is back in the + menu as a toggle. The pill now only shows in the
composer when MCP is on, matching Canvas, instead of always sitting there.
- The dropdown follows the composer side like the + menu (opens down in the
welcome composer, up when docked) rather than always opening upward.
- Drop the dropdown caret when pills collapse so the icon is not squished.
- Stop force-syncing mcpEnabledForChat to the server count; the + menu owns it.
* studio/chat: MCP expands the composer, drop sidebar Compare, tidy scrollbars
- Toggling MCP now expands the composer and shows the tool pills, the same as
Canvas, instead of leaving the row collapsed.
- Remove the Compare item from the sidebar now that it lives in the + menu, and
point the compare tour step at the side-by-side view instead of the old button.
- Both sidebars only show their scrollbar on hover, and run settings reserves
the scrollbar gutter so the close button no longer shifts when it appears.
* studio/chat: tighten toggle gap, fix run-settings close button, collapsed Train
- Reduce the composer toggle gap by 2px (gap-1 to gap-0.5) in both composers.
- Move the run settings header out of the scroll area so the close button keeps
its position whether or not the scrollbar shows, and sits flush with the
topbar open button again instead of shifting left.
- Surface Train as an icon in the collapsed sidebar (it already has a labelled
section when expanded).
* studio/chat: tighten Thinking pill X padding, create projects inline
- The Thinking pill used px-2.5, so the hover X sat further in than the left
pills. Match their pl-2 so the X lines up.
- The + menu New project now opens a create dialog and jumps straight to the
new project, instead of routing to the projects list. Shared by both
composers via a small NewProjectDialog.
* studio/chat: soften account menu, hover scrollbars, show collapsed chevrons
- Account menu drops its border ring for the composer's soft shadow and opens
centered over its trigger.
- Settings and search reuse the hover-only scrollbar via a shared
hover-scrollbar class, matching the sidebars.
- Train and Recents keep their chevron visible while collapsed so it is clear
they can be expanded.
* studio/chat: roomier, more rounded account menu
Widen the account menu, add more left and right padding on the rows, bump the
row height and text a touch, and round the corners more, closer to the GPT
account menu.
* studio/chat: trim account menu width and nudge it up 2px
Pull the account menu in slightly on the left and right (narrower box, a touch
less row padding) and lift it 2px higher above the trigger.
* studio/settings: drop outline ring, circular close hover, pointer cursors
- Remove the settings dialog outline ring, keeping just the soft shadow.
- The close button hover is now a circle instead of a rounded rectangle.
- Every clickable control in the settings dialog uses a pointer cursor.
* studio/chat: bump MCP pill icon to 14.5px
Nudge the MCP icon up 0.5px so it sits even with the other pill glyphs.
* studio/chat: bump MCP pill icon to 15px
Nudge the MCP icon up another 0.5px.
* studio/settings: add a Settings title above the tabs
Put a Settings heading at the top of the sidebar so the tabs sit below it,
matching the Claude settings layout. Hidden on mobile where the nav is a row.
* studio/settings: rounder tab hover, bigger title, less-round search dialog
* studio/sidebar: round nav row hover boxes 2px more (10px to 12px)
* studio: drop settings dark shadow + divider, add tab left padding, tune hover roundness
* studio/model-selector: roomier padding, borderless box, rounder hover rows; settings divider light-only
* studio/search: match chat box shadow (soft light, none dark)
* studio/sidebar: borderless chat context menus, rename submenu to Projects with folder-export icon
* studio/model-selector: match light corner radius in dark, drop dark shadow, more visible dark hover
* studio/sidebar: chat context menu matches + side menu styling; relabel submenu Move to project
* studio: borderless message export menu (no dark shadow), match dark corner radius to light on export menu and settings
* studio/sidebar: open chat options menu GPT-style (down-right) and widen so Move to project fits one line
* studio/chat: message export menu uses the chatbox shadow in light mode
* studio/sidebar: narrow chat options menu slightly (w-60 to w-56)
* studio: unify all download icons to Hugeicons download-01; round profile button hover 1px more
* studio/run-settings: bump header to 16px
* studio/sidebar: trim chat options menu width slightly (w-56 to 216px)
* studio/sidebar: trim chat options menu width to w-52
* studio/profile: camera-01 Hugeicons glyph and chatbox shadow on avatar button
* studio: match dark-mode corner radius to light globally (single --radius token)
* studio/recipes: borderless New Recipe menu with chatbox shadow in light, none in dark
* studio: borderless dropdowns globally, chatbox shadow in light, none in dark
* studio: extend borderless + chatbox/none shadow to select, combobox and popover overlays
* studio/mcp: nudge MCP dropdown radius to 20px so its wider box reads as round as the + menu
* studio: restore dark dropdown shadow to avoid same-color merge; greet name ~1/3 of lines; bigger sloth + more gap
* studio/train: active tab is a borderless pill (no underline), roomier padding, more tab gap and bottom spacing
* studio/chat: nudge welcome up ~5px (still vh-based) and trim sloth image to 44px
* studio/train: active tab pill is white with chatbox shadow in light, taller padding
* studio/chat: welcome offset to calc(30vh - 10px)
* studio/chat: welcome offset to 28vh (drop the -10px)
* studio/chat: tighten sloth-to-text gap by 1px (16px to 15px)
* studio/train: revert light active pill to grey fill, drop white bg + shadow
* studio: app-wide hand cursor on every clickable control (disabled excluded)
* studio/chat: welcome offset to 26vh
* studio/chat: welcome offset to 28vh
* studio/chat: harden project and thread list guards against non-array payloads
* studio/sidebar: give the profile row more height and breathing room
* studio/sidebar: trim the profile row top and bottom padding slightly
* studio/sidebar: reduce Train and Recents section label size slightly
* studio/sidebar: trim the profile row top and bottom padding a touch more
* studio/sidebar: enlarge the profile hover area top and bottom
* studio/sidebar: increase profile hover roundness by 1px
* studio/sidebar: trim the profile row top and bottom padding slightly
* studio/sidebar: trim the profile row top and bottom padding slightly
* studio/chat: cache composer line metrics so wrap detection runs once, not per keystroke
* studio/chat: restore the prior view when exiting compare opened from the + menu
* studio/tests: drive Compare from the composer + menu after it moved out of the sidebar
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio/tests: open Compare from the composer + menu in the extra UI suite too
* studio: fix chat dictation microphone access
* studio: snappier plus-to-x spin and steady composer expand gap
Speed up the composer plus icon morph from 480ms to 300ms.
Add row-gap on the expanded composer line so the space between the text
and the controls row stays the same whether the box expanded from
wrapped text or from a toggle being on. The gap sits on the line, not the
input, so the placeholder max-height clamp never crops it.
* studio: only show composer tool pills once a model is loaded
Persisted Search/Code/Canvas/MCP toggles were surfacing the composer pill row on a fresh page load before any model was selected, so an empty composer looked different from the clean just-ejected state. Gate the composerExpanded tool checks on modelLoaded so a model-less composer stays collapsed, while saved preferences still apply the moment a model loads.
* studio: hide RAG composer menu item temporarily
Hide the placeholder RAG entry from the composer plus menu in both single chat and compare until the feature is ready, and drop the now-unused DatabaseIcon import.
* studio: let composer tools pre-select before a model loads
Selecting Web search, Code, Canvas or MCP from the + menu with no model
loaded did nothing visible: the toggle turned on but the composer never
expanded, so the pill stayed hidden. Drop the model-loaded gate from the
expand check so an active tool always surfaces its pill.
Align MCP with the Search/Code pattern too: grey it out only when a loaded
model lacks tool support, so MCP stays toggleable and the pill stays
clickable before a model is loaded instead of looking disabled.
---------
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <23090290+danielhanchen@users.noreply.github.com>
* studio: cap training dataset uploads
* studio: clean up failed dataset uploads
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* studio: raise upload limits to 500MB
* studio: make upload limit configurable
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* studio: stream upload routes
* studio: split recipe upload caps
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: tighten upload limit handling
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: import settings router directly
* studio: polish upload cap setting control
* studio: cap settings request bodies
* studio: stub settings route in desktop auth test
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* feat: align project sidebar UX with ChatGPT
* feat: align project sidebar UX with ChatGPT
* feat(chat): load stored project list
* feat(chat): add project sidebar workflows
* fix: stabilize project page navigation
* fix: projects chat loading
* fix: show project chat thread
* style: sidebar project spacing and hover clipping
* style: add expandable project chat history and move-to-project submenu
* feat: polish project sidebar
* feat: persist project sandbox paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: only create sandbox project workspace dir
* feat: add optional project workspace deletion from delete dialog
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: stabilize chat projects CI failures
* fix: polish project chat navigation
* Studio: manage chat history with projects
Group chats into projects with a dedicated projects page and route.
Sidebar shows recents with per-row actions and a vertical more-vertical
menu, and the sidebar scrollbar stays hidden so rows never shift on
hover. Includes chat settings and composer refinements.
* Studio: projects sidebar and breadcrumb polish
Sidebar:
- Remove the Compare nav item.
- Widen the sidebar to match the projects layout.
- Replace the scroll-gated bottom fade with a static fade pinned above
the profile box, so it no longer attaches to Recents or lags the
collapse and expand animation.
Topbar breadcrumb (chat-page):
- On a project landing show "Projects" linking to the projects list.
- Inside a project chat show the project name and chat title, with the
project name linking back to that specific project page.
- Drop the divider between the model selector and the breadcrumb.
* Studio: make project workspace delete test cross-platform
test_chat_project_delete_files_removes_workspace rooted the project under
pytest tmp_path, which resolves to /private/tmp on macOS. The workspace
delete guard refuses paths under the system denylist by design, so the
test passed on Linux CI but failed on macOS.
Add a workspace_projects_home fixture that keeps tmp_path on Linux and
Windows (CI unchanged) and falls back to a home subdir only when the temp
root is on the platform denylist. Derive the workspace path from the
created project so it tracks the projects home.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: satisfy import-hoist check for new path re-exports
documents_root and project_workspaces_root are re-exported from
utils.paths but only referenced as __all__ string literals, which the
import-hoist safety net does not count as a use. It flagged the two newly
added re-exports as unused imports and failed Source lint.
Name-load both via a module-level _REEXPORTED tuple so the check sees
them used. No behaviour change; consumers still import them from
utils.paths.
* fix: avoid projects empty-state flash
* fix: batch chat search indexing
* Studio: polish chat sidebar, run settings, and search
- Use the native OS scrollbar for the chat sidebar, Run settings panel, and chat search list instead of a custom scrollbar
- Highlight the active run in the sidebar and keep chat search available during training
- Stop the training log view from replaying when navigating back to a run
- Rename the chat settings panel to Run settings and align its toggle icon and position
- Tighten heading and sidebar letter spacing and lighten the Train and Recents labels
- Match the search dialog corner style across light and dark and drop the stray border
- Make the MCP Servers section header plain text instead of a link
- Remove a stray .orig backup file
* studio/frontend: restore Compare entry point in the sidebar
The chat-projects sidebar redesign dropped the Compare nav item and moved
it to thread-sidebar.tsx, which is not imported or rendered anywhere. That
left no way for a user to start a new model comparison (enterCompare only
fired from the guided tour and the training handoff), and broke the
Compare/Recipes/Export UI smoke test that clicks [data-tour="chat-compare"].
Re-add the Compare NavItem to the New Chat / Search group, carrying
data-tour="chat-compare" and the same new-comparison navigation as before.
* studio/frontend: use Unsloth green for the fallback profile avatar
Switch the initials-avatar background from blue to #14b789 so the sidebar
and edit-profile avatar match the Unsloth brand colour.
* studio/frontend: turn project breadcrumb into a project switcher dropdown
* studio/frontend: stop project card kebab clicks from opening the project
* studio/frontend: hide project switcher outside projects
* studio/frontend: stabilize project switcher loading
* style: project switcher alignment
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: shimmyshimmer <107991372+shimmyshimmer@users.noreply.github.com>
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Improve the rejection message when an MCP server address is not an http(s) URL. It now points to the expected http(s):// form with an example, and only mentions that local commands are disabled when the value contains whitespace (a reliable command signal), since a lone token may just be a scheme-less URL. Wording is host-scoped rather than desktop-only because self-hosted hosts can opt in via an env var. Backend only, with tests; accepted input is unchanged.
* Studio: harden stdio MCP gating and fix transport edge cases
- Gate the Data Recipe stdio path behind UNSLOTH_STUDIO_ALLOW_STDIO_MCP so a hosted deployment cannot spawn local processes through recipes
- Enforce the gate inside _client() so the transport sink cannot spawn when disabled
- keep_alive=False so stdio probes/calls do not leave orphan subprocesses
- Force OAuth off for stdio servers on create and update
- Drop stored headers when a server switches transport type
- Reject a command whose first token is a URL scheme
- Add MCP gate and improvement tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* tests: skip Data Recipe stdio tests when data_designer is absent
The data_designer plugin is only installed in the Studio test job, so guard
the two build_mcp_providers tests with importorskip so the core matrix skips
them instead of failing on ModuleNotFoundError.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Classify llama-server startup failures so diffusion/image GGUFs (FLUX, Qwen-Image, LTX, ERNIE-Image, Z-Image, ...) point users to the Images page instead of a misleading out-of-memory error. Other unknown architectures get a precise unsupported message; Ollama and OOM fallbacks are preserved.
Architecture is matched exactly against general.architecture, covering the arches Unsloth ships as GGUF: flux, qwen_image, ltxv, wan, lumina2.
Fixes#5842.
Harden local GGUF detection so a .gguf path is not misrouted to the transformers backend during the brief Windows lock window after llama-server is killed. Catch OSError from stat() and treat the path as the file, while a directory named *.gguf still falls through to the directory scan. Adds regression tests for the lock-window and directory cases.
* feat(studio): use lemonade-sdk/llamacpp-rocm per-GPU prebuilts for ROCm hosts
For AMD GPUs that rocminfo/hipinfo reports a recognised gfx target
(gfx103X / gfx110X / gfx1150 / gfx1151 / gfx120X), resolve_lemonade_rocm_choice()
now fetches the latest lemonade-sdk/llamacpp-rocm release and returns the
matching per-architecture zip, bundling all required ROCm runtime libs.
This runs before the existing upstream ggml-org combined-ROCm tarball fallback
on Linux and before the upstream HIP zip on Windows, so both platforms benefit
from the more targeted build when available.
Changes:
- Add LEMONADE_ROCM_REPO / LEMONADE_ROCM_RELEASES_API constants
- Add HostInfo.rocm_gfx_target populated from rocminfo (Linux) / hipinfo (Windows)
- Add _LEMONADE_GFX_FAMILIES prefix map and _lemonade_gfx_family() helper
- Add resolve_lemonade_rocm_choice() that fetches latest lemonade release and
constructs the llama-{tag}-{os}-rocm-{gfxFamily}-x64.zip asset URL
- Wire into resolve_upstream_asset_choice() for both Linux (ubuntu) and Windows paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Honor pinned llama.cpp tag in lemonade ROCm resolver
The resolver always fetched lemonade-sdk/llamacpp-rocm's /releases/latest,
ignoring the upstream llama.cpp tag the caller had pinned. On a reproducible
install where the user requested 'b1260' that meant we would silently pick
up whatever lemonade had published as latest at install time, with no way
to roll back to the matching tag.
Lemonade tags llama.cpp upstream tags 1:1, so:
- When llama_tag is unset or 'latest', keep hitting /releases/latest.
- When llama_tag is pinned (e.g. 'b1260'), hit /releases/tags/b1260.
- When the pinned tag is not published by lemonade (404), skip silently
and let the caller fall through to the upstream tarball -- this keeps
pinned installs reproducible instead of drifting.
resolve_lemonade_rocm_choice now takes llama_tag (default 'latest' for
backward compatibility) and both call sites in resolve_upstream_asset_choice
forward the upstream llama_tag to it.
Note: this PR still has open integration concerns flagged in review --
the simple-policy planner and approved-checksum manifest don't yet route
or accept lemonade assets. Those are larger changes and not in scope for
this commit; addressing the pinned-tag drift independently because it is
small, localized, and self-contained.
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* Add mock test for lemonade ROCm prebuilt asset resolution
Validates GPU family mapping and that resolve_lemonade_rocm_choice
returns real lemonade release URLs for all supported gfx targets on
both Linux and Windows, without requiring AMD hardware.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Wire lemonade ROCm prebuilts into the simple-policy install path
setup.sh invokes install_llama_prebuilt.py with --simple-policy, which
dispatches through resolve_simple_install_release_plans -> direct_linux_release_plan
(or direct_upstream_release_plan on Windows). Those planners only
handled CUDA + CPU attempts, so ROCm-only hosts (e.g. gfx1151 Strix
Halo) had no compatible prebuilt asset and silently fell through to
source build, even though resolve_lemonade_rocm_choice already knew
how to fetch a per-GPU lemonade-sdk binary.
Add a lemonade ROCm/HIP attempt to both simple-policy planners for
ROCm-only hosts, and add regression tests that drive the dispatchers
end-to-end so this can't be skipped silently again.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Document that lemonade ROCm prebuilts work on any glibc Linux
The lemonade-sdk asset filename uses "ubuntu" as a label, but the binary
is a manylinux-style glibc build with no Ubuntu-specific dependencies.
It runs on Arch, Fedora, openSUSE, Debian, etc. as long as the host
glibc is recent enough.
No behavior change -- the dispatch already runs for any Linux ROCm
host. This commit only clarifies the comment, docstring, and log
message so users on non-Ubuntu distros (e.g. Strix Halo on Arch) don't
mistake the asset name for distro gating.
* Pattern matching fix for libggml-cpu*.so*
* fix(lemonade): pass resolved tag to lemonade resolver; add upstream HIP fallback; stub API in tests
- direct_linux_release_plan: pass bundle.upstream_tag (not requested_tag)
to resolve_lemonade_rocm_choice so a "latest" request doesn't mix a
newer lemonade binary with an older planned unsloth release (Codex P2)
- direct_upstream_release_plan: same fix on the Windows path (release_tag
instead of requested_tag); also add the upstream HIP asset
(llama-<tag>-bin-win-hip-radeon-x64.zip) as a fallback between lemonade
and CPU so unsupported GPUs or transient lemonade failures don't silently
downgrade to CPU when an upstream ROCm prebuilt exists (Codex P2)
- test file: stub fetch_json with a synthetic lemonade release payload so
the suite is hermetic and not subject to GitHub API rate limits (Codex P1);
add test_simple_policy_windows_hip_falls_back_to_upstream_when_lemonade_unavailable
to cover the new HIP fallback path
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(lemonade): revert bad tag fix; keep upstream HIP fallback + hermetic tests
The previous commit wrongly passed bundle.upstream_tag / release_tag to
resolve_lemonade_rocm_choice. Lemonade uses its own versioning (b1262,
b1264, …) completely independent of unslothai's tags (b9186, …), so
passing a resolved unslothai tag caused a 404 and silently skipped the
lemonade binary entirely. Revert both call sites to requested_tag.
Keep the two valid fixes from the prior commit:
- Upstream HIP fallback (llama-<tag>-bin-win-hip-radeon-x64.zip) between
lemonade and CPU in direct_upstream_release_plan, so unsupported GPUs
or transient lemonade failures don't silently downgrade to CPU (Codex P2)
- Stub fetch_json in tests so the suite is hermetic (Codex P1)
* fix(studio/rocm): respect HIP_VISIBLE_DEVICES when picking lemonade gfx target
The rocminfo / hipinfo regex took the first gfx match in the agent listing.
On mixed APU + dGPU hosts (e.g. Strix Halo gfx1151 + discrete RX 7900 gfx1100)
this picked whichever GPU appeared first in the tool's stdout, not the one
HIP actually runs on. The downloaded lemonade asset could then be a binary
for a different arch than the active device.
Extracted a module-level _pick_rocm_gfx_target() helper that:
- collects every gfx token in order via re.findall (skips gfx000 / generic ISAs)
- if HIP_VISIBLE_DEVICES or ROCR_VISIBLE_DEVICES is set, parses the first
comma-separated entry as an integer index into that list
- falls back to the first GPU for non-integer (UUID-style) or out-of-range
values, matching the previous default behaviour
Both Linux (rocminfo) and Windows (hipinfo) branches use the helper.
Existing 18 lemonade tests pass; no behavioural change for single-GPU hosts.
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* fix(studio/rocm): dedup rocminfo gfx tokens and honor disabled visibility
Follow-up to 8793aef0. rocminfo / hipinfo emit each gfx target multiple
times per GPU (Name, ISA triple, marketing name), so the prior re.findall
indexing returned the wrong device when HIP_VISIBLE_DEVICES picked GPU 1
on a mixed-arch host -- the helper picked the second occurrence of GPU 0
instead. Collapse to unique tokens (insertion-ordered) before indexing.
Also handle HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES values of '' and
'-1' as "no AMD visible" (matches the rest of Studio's visibility code),
returning None so the planner does not pick a Lemonade asset for a
hidden GPU.
* fix(studio/rocm): memoise lemonade release lookup + HIP_PATH hipinfo fallback
Robustness pass on top of 25d4ab63:
1. resolve_lemonade_rocm_choice() is called twice per install (planner +
resolve_upstream_asset_choice), so every install previously hit
api.github.com twice with identical args -- doubling the 403/rate-limit
failure surface on busy CI runners. Extract the fetch into a
functools.lru_cache(maxsize=8) helper keyed on (api_url, llama_tag).
The cached helper also owns the error-path logging the resolver was
doing inline. Tests that need to vary fetch_json output across
invocations should call _fetch_lemonade_release_cached.cache_clear().
2. Windows detect_host probe for hipinfo / amd-smi only used
shutil.which(), so HIP SDK installs that set HIP_PATH but do not put
%HIP_PATH%\bin on system PATH classified the host as non-ROCm. The
PowerShell installer and studio/install_python_stack.py already
resolve HIP_PATH\bin\hipinfo.exe as a fallback; mirror that here so
the install planner agrees with the rest of Studio on what counts as
a ROCm host.
Tests: 18 passed (shipped); 52 extra sim cases pass (added 2 for the
lru_cache deduplication path).
* fix(studio/rocm): lemonade URL trust pinning, runtime overlay covers HIP libs, opt-out env
Robustness pass driven by 5 parallel reviewers of head c08b15e6:
1. URL trust pinning. AssetChoice.url comes from the GitHub API response's
browser_download_url field. Lemonade attempts are not in the approved-hash
manifest, so a compromised API response could redirect the download to an
attacker-chosen host without the integrity gate catching it. New
_is_trusted_github_release_url() validates https + github.com/<expected_repo>
release path OR objects.githubusercontent.com (GitHub's CDN). Resolver
refuses to download otherwise.
2. UNSLOTH_DISABLE_LEMONADE_ROCM opt-out. Users who prefer the upstream HIP
build path can set this env var to skip lemonade outright (e.g. for
air-gapped installs or stricter trust requirements). The install log
already prints a NOTE explaining that lemonade lacks approved-hash
coverage so users know the trust model.
3. linux-rocm runtime overlay patterns extended to cover lemonade's bundled
HIP/ROCm runtime libs (libamdhip64.so*, libhsa-runtime64.so*, libhipblas*,
librocblas*, librocsolver*, librocsparse*, librocrand*, libMIOpen*,
libmagma*). The upstream tarball does not ship these (links against
system /opt/rocm), so the new glob entries are no-op for upstream and
load-bearing for lemonade. Without this, install_from_archives would
drop the bundled runtime libs from the lemonade ZIP and llama-server's
RPATH would fail to load amdhip64 at first inference.
4. _lemonade_release_api_for now URL-encodes llama_tag with quote(safe="").
Defence in depth: a tag containing /, ?, #, or whitespace cannot reshape
the request URL. Tags come from internal resolution today, but this
removes the risk if a future caller passes user-controlled input.
5. Empty browser_download_url skipped explicitly in the resolver. The
release_asset_map helper defaults missing URLs to "". Previously this
would have been passed to download_file("") which raises a less obvious
error than the new clean log + return None.
6. Docstring on _lemonade_release_api_for clarified: lemonade tags match
ggml-org/llama.cpp tags 1:1, NOT unslothai/llama.cpp fork tags. The
earlier P1 review report misread this and re-asked for the tag-drift
fix that the author intentionally reverted in f256cea950.
7. Autouse pytest fixture in test_lemonade_llamacpp_rocm_bins_mock.py
clears _fetch_lemonade_release_cached between tests. Today's tests all
mock the same payload so no pollution surfaces, but the lru_cache
becomes a footgun the moment any future test parametrises return values.
Tests: 28 passed (was 18, added 10 covering URL pinning, opt-out env,
pinned-tag helper, URL encoding, empty URL, runtime patterns).
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* fix(studio/rocm): complete lemonade runtime overlay + honor CUDA_VISIBLE_DEVICES
runtime_patterns_for_choice("linux-rocm") was missing libamd_comgr.so*,
librocm_kpack.so*, and librocm_sysdeps_*.so*, which are direct NEEDED
entries of libamdhip64.so.7 in every lemonade bundle. install_from_archives
did not copy them, so the preflight ldd walk failed on llama-server and every
lemonade attempt fell back to source build. Confirmed on gfx1151 b1272 by
h34v3nzc0dex; manually staging the full bundle with the three missing
patterns restored the install and preserved bench parity.
Also extend _pick_rocm_gfx_target to check CUDA_VISIBLE_DEVICES after
HIP_VISIBLE_DEVICES and ROCR_VISIBLE_DEVICES: AMD's HIP runtime honours
all three with identical semantics, so mixed-arch hosts where users set
only CUDA_VISIBLE_DEVICES were still picking the first gfx token instead
of the user-selected device.
Tests: 30 passed (was 28); added 2 for CUDA_VISIBLE_DEVICES (multi-GPU
pick + -1 opt-out) and extended the runtime-patterns test to assert the
three newly added lib globs are present.
* fix: tighten CDN trust check and fix multi-GPU arch selection
- _is_trusted_github_release_url: require /github-production-release-asset-
path prefix so only real GitHub release CDN URLs are accepted
- _pick_rocm_gfx_target: parse rocminfo Agent N section boundaries to build
a per-physical-GPU arch list; same arch across multiple GPUs no longer
collapses to a single token, so HIP_VISIBLE_DEVICES indexing works correctly
- tests: update CDN test to use realistic path prefix, add rejection test for
arbitrary CDN path, add regression test for same-arch multi-GPU case
* fix: use broad lib*.so* glob for linux-rocm runtime overlay
Replace the explicit lib allowlist in runtime_patterns_for_choice with
lib*.so* for the linux-rocm path.
The lemonade ROCm ZIPs carry a full HIP/ROCm runtime including transitive
deps like libLLVM.so.23.0git and libclang-cpp.so.23.0git (pulled in by
libamd_comgr.so.3). These names change across ROCm releases and were not
in the allowlist, so preflight would see them as unresolved NEEDED entries
and fall back to a source build. The broad glob catches everything in the
bundle now and in future releases without needing to enumerate each library.
* fix: show lemonade binary tag in install summary log line
Store binary_repo and binary_release_tag in UNSLOTH_PREBUILT_INFO.json
so the setup.sh summary can distinguish the source tree (unslothai/llama.cpp)
from the actual binary origin (lemonade-sdk/llamacpp-rocm).
Before: 'installed release: unslothai/llama.cpp@b9334'
After: 'installed release: unslothai/llama.cpp@b9334 + lemonade@b1280'
Upstream installs are unchanged (binary_repo == published_repo).
* fix(merge-compat): align Windows ROCm guard and helper name with strix branch
- elif host.has_rocm instead of if...not to match fix/rocm-strix-halo-unified-memory
- _resolve_exe instead of _resolve_amd_exe with identical body/docstring
Eliminates the two conflict hunks that would otherwise block a clean bot merge
of feature/lemonade-rocm-prebuilts on top of fix/rocm-strix-halo-unified-memory.
* fix: three PR review corrections for lemonade ROCm prebuilt integration
- _lemonade_release_api_for: fix docstring claiming lemonade tags match
ggml-org 1:1 -- lemonade may be several builds behind ggml-org (noted
by oobabooga, confirmed: lemonade b1281 vs ggml-org b9370)
- direct_linux_release_plan: move cpu_choice into else-branch so ROCm-only
hosts never get a CPU fallback in the attempts list -- a failed lemonade
binary now raises PrebuiltFallback and triggers the HIP source build
instead of silently installing a CPU-only binary
- apply_approved_hashes: pass lemonade attempts through without requiring
a manifest entry; lemonade is explicitly documented as relying on
functional validation only, so rejecting it here caused PrebuiltFallback
on the non-simple-policy path before the upstream ROCm/HIP fallback
could be considered
* fix: copy hipblaslt/rocblas library subdirs from lemonade ROCm archives
copy_globs matches filenames only and copies flat, so it cannot
preserve the hipblaslt/library/<gfx>/ and rocblas/library/<gfx>/
Tensile kernel catalog trees that lemonade ROCm zips ship alongside
the .so files. Add runtime_subdirs_for_choice() and call shutil.copytree
for each named subdir after the copy_globs pass in install_from_archives.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Jaeic Lee <jaeiclee@users.noreply.github.com>
* fix(studio): set HIP_VISIBLE_DEVICES in apply_gpu_ids for ROCm training workers
Training workers are spawned via multiprocessing spawn before detect_hardware()
runs, so IS_ROCM is still False. If the user never set HIP_VISIBLE_DEVICES in
their shell, _inherits_rocm_visibility is also False, leaving the worker with
only CUDA_VISIBLE_DEVICES set. On ROCm hosts the HIP runtime honors
HIP_VISIBLE_DEVICES over CUDA_VISIBLE_DEVICES, so the worker saw the full
device list and torch raised "no usable HIP accelerator" on some setups.
Fall back to probing torch.version.hip (a build-time attribute, safe to read
before GPU init) to detect ROCm when neither IS_ROCM nor inherited env vars
are available. Mirrors the existing fix in llama_cpp.py for llama-server
subprocess GPU pinning.
Fixes https://github.com/unslothai/unsloth/issues/5180
* test: tighten apply_gpu_ids ROCm fallback assertions
Replace loose OR chain with exact string matches, split into three
focused tests, and add a guard check for the try/except wrapper.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: detect ROCm unified memory (Strix Halo / AMD iGPU) via torch fallback
amd-smi on iGPUs with shared/unified memory (e.g. Radeon 8060S on Strix
Halo) reports only the dedicated VRAM slice (~512 MB) in its metric output,
so get_visible_gpu_utilization() was returning usable_gb ≈ 0.35 GB instead
of the full GTT pool (~128 GB). torch.cuda.mem_get_info() already surfaces
the correct unified-pool size.
Add _reconcile_rocm_unified_memory(): after amd-smi returns a valid result
on a ROCm device, cross-check each device's vram_total_gb against
torch.cuda.mem_get_info(). When torch reports a larger total, replace the
amd-smi VRAM fields in-place. No-op for discrete AMD GPUs where the two
sources agree.
Fixes: "Falling back to all visible GPUs -- model may not fit" on AMD iGPU
machines even when 100+ GB of unified memory is available.
* Apply unified-memory reconciliation in get_gpu_utilization too
The visible-GPU path was already corrected for AMD iGPUs with unified memory
(Strix Halo / Radeon 8060S), but get_gpu_utilization was still returning the
raw 512 MB amd-smi VRAM slice. Studio's /api/train/hardware endpoint and the
live GPU monitor read from this primary path, so users continued seeing the
wrong total even after auto_select_gpu_ids picked the right device.
Refactor to share the per-device correction:
* _apply_unified_memory_correction(metrics, torch_info) -- the actual
replacement logic, in-place on a single metrics dict.
* _reconcile_rocm_unified_memory(...) -- multi-device,
iterates utilization["devices"] (visible-GPU path).
* _reconcile_primary_rocm_unified_memory(...) -- single flat
metrics dict (primary-GPU path), uses parent_visible_spec to pick the
primary index, falls back to ordinal 0 when no visibility env is set.
get_gpu_utilization now calls the primary reconciler under IS_ROCM, so both
endpoints surface the real unified-memory pool on iGPUs while leaving
discrete AMD GPUs untouched (torch_total <= smi_total -> no replace).
* Use 'is not None' and log debug on torch.version.hip probe failures
Two small follow-ups to the apply_gpu_ids ROCm fallback:
1. Match detect_hardware()'s 'getattr(torch.version, "hip", None) is not None'
form so the entire codebase has one canonical 'this torch was built with
HIP' check. On every shipping torch wheel hip is either None or a non-empty
version string, so the new form agrees with the old bool() form on every
real install.
2. Log the probe failure at debug level instead of swallowing it silently.
The broad 'except Exception' is intentional (we never want apply_gpu_ids
to crash a worker over a probe), but the silent pass made it impossible
to tell whether the fallback was firing or being skipped.
* fix(studio): honour HIP_VISIBLE_DEVICES in _get_parent_visible_gpu_spec before IS_ROCM is set
When a user has HIP_VISIBLE_DEVICES set in their shell (e.g. "1" to select
GPU 1) but detect_hardware() has not yet run in the Studio parent process,
IS_ROCM is still False. _get_parent_visible_gpu_spec() was gated on IS_ROCM
so it fell through to CUDA_VISIBLE_DEVICES (unset), saw all physical GPUs,
and auto-selected index 0. apply_gpu_ids then overwrote HIP_VISIBLE_DEVICES
with "0", making the intended GPU invisible to ROCm torch in the worker,
which triggered the "no usable HIP accelerator" error (issue #5180).
Apply the same _inherits_rocm_visibility pattern already used in
apply_gpu_ids: check for HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES in the
environment regardless of IS_ROCM so the correct GPU index is preserved.
* fix(install): harden AMD ROCm GPU detection for multi-GPU and env-filtered setups
The previous rocminfo awk pattern could miss discrete GPUs on machines
where HIP_VISIBLE_DEVICES/ROCR_VISIBLE_DEVICES is used to mask an
integrated GPU — the env vars filter rocminfo output but may not
propagate into the install script subprocess, causing detection to
fail entirely.
Two changes:
- Tighten rocminfo pattern from /gfx[0-9]/ && !/gfx000/ to
/gfx[1-9][0-9]/ — simpler and correctly excludes the CPU agent
(gfx000) without a negative lookahead
- Add sysfs KFD topology fallback: reads
/sys/class/kfd/kfd/topology/nodes/*/gpu_id which is a kernel-level
view unaffected by HIP_VISIBLE_DEVICES or ROCR_VISIBLE_DEVICES
Fixes detection failure reported in Discord by Chains (gfx1201 + iGPU
machine where env var exclusion of the iGPU caused rocminfo to return
no usable device).
* Fix KFD sysfs awk fallback to read properties file
The fallback added by this PR reads /sys/class/kfd/kfd/topology/nodes/*/gpu_id
files but matches the literal token 'gpu_id' against their content. Those
files contain only a single decimal value (e.g. '0' for CPU agents, '50432'
for GPU agents), so the regex never matches and 'found' stays 0, making the
fallback a no-op on every host. The properties file in the same directory
contains key/value lines like 'gpu_id 50432' which is what the existing awk
pattern expects.
Reproduced with a synthetic sysfs layout: against gpu_id files awk exits 1;
against properties files awk exits 0 when any node reports gpu_id > 0.
* fix(setup.ps1): detect AMD ROCm GPU on Windows, bring to parity with setup.sh
setup.ps1 only checked nvidia-smi and fell straight to "gpu: none" on AMD
machines. setup.sh already probed rocminfo/amd-smi/hipconfig/hipinfo.
Add three-tier detection mirroring install_llama_prebuilt.py's detect_host():
1. hipinfo: gcnArchName in output confirms a real HIP GPU (not just SDK)
2. amd-smi list: "GPU: <digit>" data rows as fallback
3. WMI Win32_VideoController: last resort -- detects AMD GPU even without
HIP SDK, then guides user to install it rather than silently going CPU
Also corrects the "none" message to mention AMD ROCm alongside NVIDIA so
users with AMD hardware understand the requirement.
Fixes: rohit-style install where Strix Halo (Radeon 8060S) showed
"gpu: none" even with the HIP SDK present.
* fix(install.ps1): detect AMD ROCm GPU on Windows, bring to parity with setup.ps1
install.ps1 had the same nvidia-smi-only GPU detection as setup.ps1 before
the setup.ps1 fix. Applies the same three-tier AMD detection:
1. hipinfo: gcnArchName confirms real HIP GPU
2. amd-smi list: GPU data rows as fallback
3. WMI Win32_VideoController: detects AMD GPU without HIP SDK and guides
user to install it
Fixes: install.ps1 showing "gpu: none" while setup.ps1 correctly showed
"AMD GPU detected" on the same machine (reported by rohit, RX 7600 XT).
* fix(install.ps1): suppress 'No NVIDIA GPU detected' when AMD GPU is present
* feat: add Windows AMD ROCm PyTorch wheel installation
install_python_stack.py:
- Add _ROCM_WINDOWS_WHEEL_BASE and _ROCM_WINDOWS_RELEASES constants
pointing to AMD repo.radeon.com (ROCm 7.2 -> torch 2.9.1+rocm7.2.1)
- Extend _ensure_rocm_torch() with a Windows branch: detects ROCm via
_has_rocm_gpu() / _detect_rocm_version(), requires Python 3.12 (cp312
is the only ABI AMD publishes for Windows), installs the direct wheel
URL from repo.radeon.com
install.ps1:
- Capture ROCmVersion during AMD detection via hipconfig --version /
amd-smi version (needed for wheel URL selection)
- After Get-TorchIndexUrl, add an AMD wheel override block: when HasROCm
and Python 3.12 detected, set ROCmTorchWheelUrl to AMD wheel URL
- Expand torch install branch to handle ROCmTorchWheelUrl with
uv pip install --force-reinstall --no-cache-dir
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* fix: also install torchvision and torchaudio from AMD Windows repo
AMD publishes matching torchvision-0.24.1+rocm7.2.1 and
torchaudio-2.9.1+rocm7.2.1 cp312 wheels at the same repo.radeon.com
release folder. Install all three in both install.ps1 and
install_python_stack.py Windows ROCm path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat: add ROCm 7.1.1 Windows wheel mapping
AMD uses a different version string for 7.1.1 wheels:
2.9.0+rocmsdk20251116 (date-tagged) instead of +rocm7.1.1.
Adds the 7.1.1 release folder to both install.ps1 and
install_python_stack.py so users with ROCm 7.1 get ROCm
torch instead of falling back to CPU.
* fix: install rocm_sdk_core and rocm_sdk_libraries_custom alongside torch
The AMD Windows torch wheels declare rocm[libraries]==<ver> as a hard
dependency. Without installing rocm_sdk_core and rocm_sdk_libraries_custom
from the same AMD release folder, uv cannot resolve the dependency and
fails with 'No solution found'. Include all 5 wheels in one install call.
* fix: expand ROCm wheel array to scalars for Invoke-InstallCommand
@array splatting inside a scriptblock only works when the native command
is prefixed with '&'. Invoke-InstallCommand uses '& $Command' to run the
block, so @ROCmAllWheelUrls was not being expanded. Extract to scalar
variables $rw0-$rw4 which are captured correctly by the closure.
* fix: use --no-deps for AMD Windows torch wheel install
uv's resolver looks up rocm[libraries]==0.1.dev0 on PyPI during
dependency resolution before downloading any wheels, and fails because
the package doesn't exist on PyPI. --no-deps skips resolution entirely
and installs all 5 AMD wheels directly. The GPU runtime dependency is
satisfied by the HIP SDK, not a Python package.
* fix: setup.ps1 and install_python_stack.py now install ROCm torch on Windows
setup.ps1 was always setting CuTag='cpu' for non-NVIDIA hosts and installing
cpu-only PyTorch, overwriting the ROCm torch installed by install.ps1.
Adds the same AMD wheel selection logic (ROCm version detection, Python 3.12
check, 5-wheel install with --no-deps) to setup.ps1's torch install block.
install_python_stack.py: remove IS_WINDOWS guard from _ensure_rocm_torch()
call site so the Windows path in _ensure_rocm_torch() is reachable during
'unsloth studio update' as well.
* fix: suppress manual-install warning when ROCm torch already present; fix progress counter
- Gate the 'must be installed manually' warning on torch.version.hip being empty
so it doesn't fire when our ROCm torch install succeeded
- Update _TOTAL counter to include the 3 ROCm steps on Windows now that
_ensure_rocm_torch() is called there (fixes 10/9 display)
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* feat: add rocm step display in setup.ps1; fix warning and progress counter
- Add 'rocm' step after 'cuda' in setup.ps1 showing ROCm version or HIP SDK missing
- Move ROCm version detection up to GPU detection block so it's available early
- Suppress 'must be installed manually' warning when torch.version.hip is set
- Fix _TOTAL counter to include ROCm steps on Windows (fixes 10/9 display)
* fix: detect AMD SDK ROCm torch via __version__ when torch.version.hip is unset
AMD's repo.radeon.com wheels (e.g. 2.9.0+rocmsdk20251116) do not set
torch.version.hip, leaving it None. All three probes that relied solely on
torch.version.hip now also check for 'rocm' in torch.__version__.lower():
- hardware.py detect_hardware(): IS_ROCM was never set, causing the studio
to report 'Hardware detected: CPU' even after AMD wheels were installed
and HIP DLLs were on PATH.
- install_python_stack.py _ensure_rocm_torch(): skip-if-already-installed
probe would always reinstall on subsequent runs.
- install_python_stack.py Windows AMD warning: suppression check always
failed, so the 'must be installed manually' note kept appearing after
a successful AMD wheel install.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* perf: drop --no-cache-dir from AMD ROCm torch wheel installs
uv caches downloaded wheels by default; passing --no-cache-dir forced a
full redownload of the ~2 GB torch wheel on every install run. CUDA installs
never had this flag -- AMD was the only path affected.
* fix: use install-state flag instead of subprocess probe for AMD Windows warning
Replace the subprocess torch probe in the post-install warning block with a
module-level _rocm_windows_torch_installed flag set by _ensure_rocm_torch().
Subprocess re-import of torch is unnecessary and fragile -- the install
function already knows whether it succeeded.
* fix: hoist global declaration to top of _ensure_rocm_torch
Python requires the global statement to appear before any assignment
to the variable within a function. Moving it to the function top fixes
the SyntaxError on line 354.
* fix: pass AMD torch install status via env var to suppress false warning
setup.ps1 now sets UNSLOTH_ROCM_TORCH_INSTALLED=1 after a successful AMD
wheel install. install_python_stack.py reads this at the top of
_ensure_rocm_torch() to skip both the subprocess probe and the warning --
no re-import of torch needed, and the warning message now correctly says
'could not be auto-installed' rather than 'must be installed manually'.
* fix: register ROCm DLL directory before torch import on Windows
Python 3.8+ ignores PATH for extension DLL loading on Windows; amdhip64.dll
and other HIP runtime DLLs must be registered via os.add_dll_directory().
Without this, torch.cuda.is_available() always returns False on AMD ROCm
Windows even when HIP_PATH is correctly set in system environment variables.
Reads HIP_PATH / ROCM_PATH env vars first, then falls back to scanning
common ROCm install roots (C:\Program Files\AMD\ROCm, F:\ROCm, C:\ROCm).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: remove hardcoded non-standard ROCm paths from DLL directory scan
Only use HIP_PATH/ROCM_PATH (set by AMD installer) and the standard
C:\Program Files\AMD\ROCm\<version>\bin location. Custom drive paths
like F:\ROCm are user-specific and should not be hardcoded.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: prevent torchao overrides step from overwriting AMD ROCm torch
torchao==0.14.0 in overrides.txt declares torch as a dependency. Without
--no-deps, uv resolves torch from PyPI and installs 2.11.0+cpu on top of
the AMD ROCm wheels (2.9.0+rocmsdk20251116). This was the root cause of
'Hardware detected: CPU' -- the AMD wheels were installed but then
immediately overwritten by the overrides step.
When _rocm_windows_torch_installed is True, add --no-deps to the overrides
pip_install call so torchao is installed without pulling in CPU torch.
* fix: add rocm_sdk namespace tarball to Windows ROCm wheel installs
torch/_rocm_init.py calls `import rocm_sdk` at startup, which requires
the rocm namespace tarball (rocm-*.tar.gz) in addition to the SDK wheel
packages. This tarball was missing from both install.ps1 and setup.ps1,
causing ModuleNotFoundError on first torch import.
- Add rocm-0.1.dev0.tar.gz to ROCm 7.1.1 install (provides rocm_sdk namespace)
- Add rocm-7.2.1.tar.gz + rocm_sdk_devel to ROCm 7.2.1 install
- Install tarball in a dedicated step before main SDK/torch wheels
- Switch to @array splatting in install.ps1 scriptblock for dynamic wheel count
- Remove --no-cache-dir from Python-side ROCm wheel install (prevents ~2GB redownload)
* feat: enable ROCm 7.2 torch install + warn on gfx1151 with ROCm < 7.2
Chigoma333 (AMD Radeon 8060S / gfx1151, Strix Halo) confirmed that ROCm
7.1 segfaults when tensors are moved to GPU, but ROCm 7.2 + torch
2.11.0+rocm7.2 works fully including training.
Changes:
- Uncomment (7,2): "rocm7.2" in _ROCM_TORCH_INDEX (was blocked by <2.11.0)
- Add _ROCM_TORCH_PKG_SPECS dict with per-tag version bounds:
rocm7.2 → torch>=2.11.0,<2.12.0; all older tags → <2.11.0
- Add _detect_amd_gfx_codes() helper that parses rocminfo output
- Warn on gfx1151/gfx1150 (Strix Halo) when ROCm < 7.2 is installed,
pointing users at the known segfault and recommending upgrade
- install.sh get_torch_index_url(): enable rocm7.2 case (previously capped
to rocm7.1), cap unknown future tags to rocm7.2
- install.sh: override TORCH_CONSTRAINT to >=2.11.0,<2.12.0 when rocm7.2
index is selected, so pip can actually resolve torch 2.11.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: prefer Python 3.12 for AMD ROCm users when 3.13 is also installed
After GPU detection, if ROCm HIP SDK is found and the selected Python
is not 3.12, run a second pass to locate a 3.12 install via py.exe and
PATH (catches uv-managed installs). Switch $DetectedPython to 3.12 so
the venv is created with a compatible interpreter for the cp312-only AMD
Windows torch wheels.
NVIDIA and Intel GPU paths are unaffected -- the re-detection block only
runs when $HasROCm is true.
Fixes: #5301
* fix: also check uv-managed Python 3.12 for AMD ROCm #5301
* fix: hide amd-smi console popups on Windows, guard torch.distributed.is_initialized for ROCm #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: suppress remaining console popups on Windows, patch torch.distributed.is_initialized for ROCm #5301
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* fix: stub all missing torch.distributed attrs for ROCm Windows wheel #5301
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* fix: inject torch.distributed stub when C backend missing in ROCm Windows wheel #5301
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* fix(rocm/windows): pre-stub torch._C._distributed_c10d + raise amd-smi timeout
Two fixes for Windows ROCm regressions reported by electroglyph on #5301:
1. worker.py — torch.distributed stub now fires unconditionally on Windows
The previous stub only injected sys.modules in the except branch, meaning
it was silently skipped when `import torch.distributed` happened to succeed
(the C backend is lazily resolved). The crash then hit later when
transformers/trl triggered the lazy load. Fix: on win32 we pre-populate
sys.modules['torch._C._distributed_c10d'] AND set the attribute on the
torch._C extension module *before* attempting the import, covering both
the early-ImportError and lazy-load failure modes.
2. amd.py — increase amd-smi timeout from 5 s to 30 s on Windows (10 s Linux)
amd-smi on Windows must cold-init the ROCm runtime on first invocation;
5 s was consistently too short, producing repeated 'Command timed out'
warnings in the server log. 30 s gives enough headroom without blocking
indefinitely on broken installs.
3. install.ps1 — widen Python 3.12 enforcement to ROCmGpuLabel (WMI-only path)
Users whose HIP SDK is not on PATH were detected via WMI but not switched
to Python 3.12 before the install started, causing a second pass. Guard
now fires on (HasROCm -or ROCmGpuLabel).
* fix(rocm): guard c10d stub, fix TorchIndexFamily for 7.1, clean dead code + comments
- worker.py: wrap c10d stub injection in `if _c10d_key not in sys.modules` so
Windows NVIDIA users with a real torch.distributed are never affected
- install.ps1: fix Get-TauriTorchIndexFamily receiving hardcoded "rocm7.2"
even when ROCm 7.1 wheels are installed; now branches on $ROCmVersion
- main.py: remove dead `import ctypes as _ctypes` (ctypes is never called)
- hardware.py, install_python_stack.py, worker.py, install.ps1: shorten
verbose multi-line comment blocks throughout
- tests: update 4 stale assertions that expected rocm7.2 to be absent/capped
* fix(tests): match windows AMD warning assertion to actual source string
* chore: trim verbose comment blocks across all ROCm-related files
* fix: guard reconcile call against None numeric_ids; add torchvision lower bounds
* fix(install.ps1): recreate venv with Python 3.12 after ROCm switch
Venv was created with 3.13 before GPU detection ran; switching
$DetectedPython to 3.12 had no effect since $VenvPython still
pointed to the 3.13 interpreter inside the already-created venv.
* ux: detect AMD GPU before Python selection to avoid double venv creation
- Early hipinfo + WMI probe runs before Find-CompatiblePython so Python
3.12 is selected upfront when AMD is detected; venv is now created
exactly once instead of 3.13 then immediately 3.12.
- Post-venv recreation block replaced with a simple warning for the rare
case where AMD was missed by the early probe.
- setup.ps1: show venv's actual Python version (e.g. 3.12) instead of
the system Python found by the pre-activation search (was showing 3.13).
* fix(rocm/win): auto-stub all _distributed_c10d symbols via PEP-562 __getattr__
The bare ModuleType stub caused ImportError when torch._dynamo was imported
(triggered by trainer.py accessing torch._dynamo.config at load time).
torch._dynamo pulls in torch.distributed.fsdp._flat_param which does:
from torch._C._distributed_c10d import FakeProcessGroup
and potentially other symbols. Adding module __getattr__ auto-creates a
stub class for any missing symbol so all such imports succeed without
enumerating every individual symbol. Applied to both the primary stub
and the fallback stub in the except branch.
* chore: trim c10d stub comment
* fix(rocm/win): auto-stub missing torch.distributed attrs (Store, ProcessGroup, …)
* fix(rocm/win): pre-stub fsdp submodules in sys.modules; fix __getattr__ subpackage clash
* feat(rocm/win): arch-aware wheel selector always picks newest ROCm release
Replace HIP-SDK-version-gated wheel selection with GPU arch-based logic.
Select-ROCmWheelRelease (PS) and _select_windows_rocm_release (Python) map
gcnArchName → minimum ROCm version, then pick the newest available release
that satisfies it (currently always rocm-rel-7.2.1 for any supported GPU).
Wheels bundle their own ROCm runtime so the installed HIP SDK 7.1 does not
prevent using 7.2.1 wheels on gfx1200 (RX 9060 XT) and similar RDNA 4 GPUs.
Also installs the bitsandbytes Windows ROCm continuous-release wheel and sets
BNB_ROCM_VERSION=72 in worker.py before ML imports so bnb loads the
libbitsandbytes_rocm72.dll that ships in that wheel.
* fix(rocm/win): stub class metaclass for ProcessGroup.BackendType; amd-smi circuit breaker
torchao.float8.inference accesses ProcessGroup.BackendType as a class-level
attribute. Plain type() stubs have no __getattr__ on the metaclass so this
raises AttributeError. Introduce _StubClassMeta whose __getattr__ returns
child stub classes, fixing the torchao import chain.
Add an amd-smi circuit breaker in amd.py: after 3 consecutive failures the
module stops spawning the process, eliminating the repeated Windows UAC /
DiskPart elevation prompts caused by polling a non-functional amd-smi.
Also guard BNB_ROCM_VERSION=72 behind a DLL existence check so bitsandbytes
fails with its own detection message rather than a harder "DLL not found" when
the Windows ROCm bnb wheel is not yet installed.
* fix: stub __members__ so torchao float8 enum check doesn't crash on ROCm Windows
torchao.float8.inference accesses ProcessGroup.BackendType.__members__
expecting a Python Enum registry dict. _StubClassMeta.__getattr__ was
blocking all dunder attributes, causing AttributeError. Return {} for
__members__ specifically so the isinstance/iteration checks pass cleanly.
* fix: stub distributed tensor/functional_collectives to prevent missing C++ op crash on ROCm Windows
torch._dynamo.trace_rules eagerly loads torch.distributed.tensor at import
time, which pulls in _functional_collectives.py. That file registers Meta
kernels for _c10d_functional C++ ops, but those ops are only registered
by torch._C._distributed_c10d — a C extension absent from ROCm Windows
wheels. Pre-stubbing the affected modules in sys.modules prevents the real
import chain from running and avoids the "operator does not exist" crash.
* fix: give mod stubs __path__ and pre-stub _tensor to fix 'not a package' import error
_make_mod_stub now sets __path__=[] so Python treats stub modules as
packages. Without it, any import of a submodule raises "is not a package".
Also pre-stub torch.distributed._tensor and its submodules so that
_tensor/__init__.py (which re-exports from torch.distributed.tensor) never
runs and torchao's `from torch.distributed._tensor import DTensor` gets a
harmless stub instead of crashing.
* fix: stub torch.ops._c10d_functional namespace with hashable op sentinels
torchao.dtypes.nf4tensor uses _c10d_functional ops as dict keys at import
time (all_gather_into_tensor.default, wait_tensor.default) and
torch.ops.c10d.scatter_.default. None of these ops are registered on ROCm
Windows because torch._C._distributed_c10d (the C extension) doesn't ship.
Replace the whole _c10d_functional namespace with a custom stub whose ops
return hashable .default objects, so dict-key construction doesn't crash.
Also inject a scatter_ stub into torch.ops.c10d if it's missing.
* fix: stub entire torchao package on ROCm Windows instead of individual ops
torchao is not supported on ROCm Windows and its import chain transitively
requires torch._C._distributed_c10d (absent from the ROCm Windows wheel).
Rather than stub each missing op one by one, stub the whole torchao package
upfront. Unsloth uses bitsandbytes for quantization, not torchao, so this
has no functional impact. transformers gracefully handles an importable-but-
empty torchao by disabling TorchAoHfQuantizer.
* fix: set __spec__ on mod stubs so importlib.util.find_spec doesn't raise
Manually-injected sys.modules entries have __spec__=None by default.
importlib.util.find_spec() raises ValueError when it finds a module in
sys.modules with __spec__=None (transformers.utils.import_utils hits this
when checking if torchao is available). Give every stub a minimal
ModuleSpec(name, loader=None, is_package=True) to satisfy find_spec.
* fix: add meta path finder to auto-stub subpackages of stub modules
`import torchao.prototype` goes through the import machinery, not
__getattr__, so an empty __path__ means ModuleNotFoundError. Rather than
list every submodule explicitly, register a MetaPathFinder that intercepts
any import whose parent is one of our stubs (detected by loader=None in the
parent's ModuleSpec). Real installed packages always have a SourceFileLoader
so they are never intercepted. Also register child stubs in sys.modules
from __getattr__ as a belt-and-suspenders measure.
* fix: use _unsloth_stub sentinel instead of loader=None for stub detection
The import machinery overwrites module.__spec__ with the spec returned by
find_spec (which has loader=_StubSubpackageLoader, not None), so the
loader=None check broke for second-level subpackages. Switch to a custom
_unsloth_stub object identity sentinel set directly on each stub module --
it survives __spec__ being replaced and correctly identifies stubs at any
depth (torchao.prototype.safetensors, etc.).
* refactor(rocm/win): switch to repo.amd.com arch-aware index, remove stubs
AMD recommends repo.amd.com/rocm/whl/{arch}/ as the Windows ROCm wheel
source. These wheels bundle their own ROCm runtime, support all Python
versions (not just cp312), and include the full torch._C extension set
(including _distributed_c10d) that the old repo.radeon.com wheel omitted.
Changes:
- install.ps1: remove Select-ROCmWheelRelease + hardcoded cp312 wheel
URLs; remove Python 3.12 forced-preference logic; install via
--index-url repo.amd.com/rocm/whl/{arch-family}/
- studio/setup.ps1: same -- remove Select-ROCmWheelRelease, switch to
repo.amd.com arch-aware index URL
- studio/install_python_stack.py: replace _ROCM_WINDOWS_RELEASES /
_select_windows_rocm_release with _windows_rocm_index_url() using the
_GFX_TO_AMD_INDEX_ARCH map; drop Python 3.12 restriction
- studio/backend/core/training/worker.py: remove all stub machinery
(_make_mod_stub, _StubSubpackageFinder, _StubSubpackageLoader,
_StubClassMeta, torchao/fsdp/dtensor stubs, _c10d_functional ops
stubs, BNB DLL detection) -- no longer needed with new wheel source
* fix(rocm/win): restore _distributed_c10d + torchao stubs; fix BNB install
repo.amd.com torch wheels also omit torch._C._distributed_c10d on Windows
(RCCL is not shipped on Windows). torch/distributed/__init__.py imports
from it unconditionally at module level, so the stub must land in
sys.modules before any torch.distributed import.
torchao (pulled in by transformers.quantizers) walks
torchao.float8.distributed_utils -> torch.distributed._functional_collectives
-> distributed_c10d at import time. Stubbing torchao up-front short-circuits
that chain.
worker.py:
- Restore _make_mod_stub / _StubSubpackageFinder / _StubSubpackageLoader
- Restore _StubClassMeta for ProcessGroup.BackendType attribute access
- Restore _distributed_c10d stub with __getattr__ (Windows only)
- Restore torchao stubs (5 modules, Windows only)
install_python_stack.py:
- BNB AMD wheel install was inside the early-return branch that fires when
torch is already a ROCm build (installed by install.ps1). Move BNB install
outside that branch so it always runs on Windows ROCm — the PyPI
bitsandbytes has only CUDA DLLs and fails to load on ROCm.
* worker: remove _distributed_c10d stub; stub only torchao
The installed torch/distributed/__init__.py from repo.amd.com
(torch==2.10.0+rocm7.12.0) is now properly guarded with
`if is_available():`, so `import torch.distributed` alone is safe.
The crash only comes via torchao's import chain:
torchao.float8.distributed_utils
→ torch.distributed._functional_collectives (unguarded import)
→ torch.distributed.distributed_c10d
→ torch._C._distributed_c10d ← absent on Windows ROCm
Stubbing torchao short-circuits the chain entirely. No need to stub
_distributed_c10d. Remove _StubClassMeta and the _c10d stub block;
keep only _make_mod_stub + _StubSubpackageFinder + torchao seeds.
* fix: BNB AMD wheel skipped + torch.compile segfault on Windows ROCm
install_python_stack.py: the UNSLOTH_ROCM_TORCH_INSTALLED=1 early-return
path (set by setup.ps1 when it installed torch itself) returned before
ever reaching the AMD BNB prerelease wheel install. The PyPI
bitsandbytes==0.49.x ships only CUDA DLLs, so loading it on ROCm fails
with "libbitsandbytes_rocm72.dll not found". Now installs the AMD
Windows BNB wheel before returning on that path too.
worker.py: torch._grouped_mm crashes on gfx1200 (null HIP kernel pointer,
0xC0000005) when torch.compile's JitDecomp system dispatches it during
the first forward pass. Detect Windows ROCm via torch.version.hip
(already in sys.modules from section 1e) and set TORCHDYNAMO_DISABLE=1
to bypass the broken kernel dispatch.
* fix: BNB AMD wheel install fails uv wheel filename check
The bitsandbytes continuous-release wheel is intentionally mismatched:
filename encodes 1.33.7.preview (= 1.33.7rc0 in PEP 440) but wheel
metadata reports 0.50.0.dev0. uv rejects this by default.
Introduce _install_bnb_windows_rocm() helper that sets
UV_SKIP_WHEEL_FILENAME_CHECK=1 only for this specific install, then
restores the previous env value. Both BNB install call sites (the
UNSLOTH_ROCM_TORCH_INSTALLED early-return path and the normal Windows
ROCm path) now use this helper.
* worker: patch _grouped_mm CUDA dispatch on Windows ROCm (gfx1200 null kernel)
TORCHDYNAMO_DISABLE=1 stopped the compiler frontend but not the autograd
JitDecomp system, which also dispatches _grouped_mm and hits the same
null HIP kernel crash (0xC0000005).
Verified that torch.library.Library("aten","IMPL").impl("_grouped_mm", fn,
"CUDA") successfully overrides the broken HIP kernel with a Python mm
fallback on torch==2.10.0+rocm7.12.0.
Schema: _grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None,
Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor
The fallback handles both the simple case (offs=None → torch.mm) and the
grouped case (offs provided → split self by offsets, multiply each group
against the corresponding slice of mat2, then cat results).
Keep _WINDOWS_ROCM_GROUPED_MM_LIB alive at function scope to prevent the
C++ dispatch registration from being freed by GC.
* worker: fix torchao stub — return stub classes not modules for isinstance()
peft/tuners/lora/torchao.py does:
from torchao.dtypes import AffineQuantizedTensor, LinearActivationQuantizedTensor
isinstance(weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor))
The stub __getattr__ was returning stub modules, which isinstance() rejects
with "arg 2 must be a type, a tuple of types, or a union".
Add _StubTypeMeta metaclass whose __instancecheck__ always returns False,
and _make_stub_type() to create stub classes via it. Change _make_mod_stub
__getattr__ to return stub classes instead of stub modules for leaf
attribute access, so isinstance() gets a valid type and returns False.
_StubSubpackageFinder still handles import-style subpackage creation
(those still need module objects in sys.modules); __getattr__ only fires
for from-import or direct attribute access, which are the isinstance paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: add coverage for Windows ROCm install paths and worker patches
Add conftest.py to fix pre-existing sys.path issue that prevented
test_rocm_support.py from running at all (install_python_stack.py
imports from backend.utils.wheel_utils which needs studio/ on sys.path).
New test classes cover everything added in this session:
- TestWindowsRocmIndexUrl: arch → AMD pip index URL mapping (gfx120X-all,
gfx1151, gfx1150, gfx110X-all, unknown → None, trailing slash)
- TestDetectWindowsGfxArch: hipinfo output parsing, missing/timeout/bad
returncode/no-gcnArchName paths
- TestInstallBnbWindowsRocm: UV_SKIP_WHEEL_FILENAME_CHECK set+restored,
env restored on exception, no-op when URL missing
- TestRocmTorchInstalledEnvVar: UNSLOTH_ROCM_TORCH_INSTALLED=1 skips
pip_install, calls _install_bnb_windows_rocm, sets flag
- TestWorkerWindowsRocmPatches: _grouped_mm CUDA dispatch override,
offs/grouped variant handling, GC-prevention sentinel,
_StubTypeMeta __instancecheck__, _StubSubpackageFinder registration,
torchao key submodule pre-stubbing, TORCHDYNAMO_DISABLE guard
- TestRocmTorchPkgSpecs: rocm7.2 torch 2.11.x spec, default <2.11 cap,
3-tuple shape, _GFX_TO_AMD_INDEX_ARCH RDNA4/3.5/3 coverage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: fix encoding, IS_WINDOWS patching, and wrong assertion
- Add encoding="utf-8" to all read_text() calls (54 occurrences) so
tests pass on Windows where the default codec is cp1252 and source
files contain UTF-8 emoji (e.g. ⚠️ in install_python_stack.py)
- Add @patch.object(stack_mod, "IS_WINDOWS", False) to Linux-path
TestEnsureRocmTorch tests so they reach the Linux code path when run
on a Windows machine instead of short-circuiting into the Windows branch
- Fix test_grouped_mm_patch_guarded_by_windows_and_hip_check: the source
uses getattr(_torch_for_rocm, "version", None) not torch.version, so
check for '"version"' and '"hip"' substrings instead
137 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: pin BNB_ROCM_VERSION=72 for torch==2.11.0+rocm7.13.0 compatibility
AMD's pip index now ships torch==2.11.0+rocm7.13.0 (ROCm 7.13).
bitsandbytes auto-detects HIP 7.13 from torch.version.hip and looks for
libbitsandbytes_rocm713.dll, which the AMD Windows prerelease wheel does
not ship (it only ships rocm72.dll), causing a load error at training start.
Fix:
- worker.py section 1f: set BNB_ROCM_VERSION=72 (via setdefault) before
section 2 ML imports, so bitsandbytes always loads rocm72.dll on Windows ROCm
- install_python_stack.py: set BNB_ROCM_VERSION=72 in _install_bnb_windows_rocm()
for any post-install imports; update comment to document root cause
- tests: 4 new assertions covering the fix (141 passed, 2 skipped)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: detect BNB ROCm DLL suffix dynamically instead of hardcoding '72'
BNB_ROCM_VERSION was pinned to '72' which works today (AMD wheel ships
rocm72.dll) but would break again if AMD ships a future wheel with a
different DLL suffix (e.g. rocm713.dll).
Add _detect_bnb_rocm_dll_ver() to install_python_stack.py: scans the
installed bitsandbytes package dir for libbitsandbytes_rocm{VER}.dll
using importlib.util.find_spec (no BNB import needed) and returns the
suffix. '72' remains the fallback when detection fails.
Apply the same detection inline in worker.py section 1f. Both paths
still respect a pre-set BNB_ROCM_VERSION (caller override wins).
Tests: +8 cases covering detection logic and fallback (147 passed, 2 skipped).
* fix: patch torch.distributed stubs in server process for Windows ROCm
On Windows ROCm, torch.distributed ships without process-group helpers
(is_initialized, is_available, get_rank, get_world_size). The worker
subprocess already patches these in section 1e, but the main server
process calls _determine_attention_impl_for_gpu_estimate() which calls
unsloth's resolve_attention_implementation() → is_initialized(), causing:
"Could not resolve attention implementation for '...':
module 'torch.distributed' has no attribute 'is_initialized'"
Fix: patch the missing attrs onto torch.distributed at the top of
_determine_attention_impl_for_gpu_estimate, matching the same stubs
already applied in worker.py section 1e. No-ops on Linux/CUDA where
torch.distributed is fully populated.
* fix: gate _grouped_mm dispatch patch on HIP < 7.13
AMD fixed the gfx1200 null HIP kernel in ROCm 7.13 (torch 2.11+).
Users on the new wheel now get the real GPU _grouped_mm kernel for
MoE workloads instead of the Python mm fallback.
Changes:
- worker.py: add _hip_ver_at_least() helper; wrap full _grouped_mm
patch in `if not _hip_ver_at_least(7, 13):` with else branch that
logs the skip reason; update section-1f comment to document the fix
- test_rocm_support.py: add 5 tests covering the helper definition,
the (7, 13) gate expression, the else branch, the skip log message,
and the AMD-format version string parsing (.split(".")[:2])
Verified: torch==2.11.0+rocm7.13.0 — 3D batch and grouped (offs)
variants both succeed; null crash only present on rocm7.12 and earlier.
* fix: stub is_torchelastic_launched on torch.distributed for Windows ROCm
resolve_attention_implementation calls is_torchelastic_launched() which
does not exist in the incomplete torch.distributed shipped with the
Windows ROCm wheel, causing a warning on every model config load in the
server process. Add it to the stub table alongside the four helpers
already patched in _determine_attention_impl_for_gpu_estimate.
Also adds two tests: one confirming the new stub and one confirming all
five core distributed helpers are covered.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: explicit warnings on AMD ROCm arch/version fallbacks + Fast-Install arg order
setup.ps1:
- Fix Fast-Install argument order: packages before flags, consistent with
all other Fast-Install calls in the file
(was: Fast-Install --force-reinstall --index-url $url torch ...)
(now: Fast-Install torch torchvision torchaudio --force-reinstall --index-url $url)
- Add explicit [WARN] substep when $HasROCm is true but arch mapping fails:
- GPU arch detected but not in supported wheel list → names the arch and
lists supported families so user knows exactly what to report
- HIP SDK present (amd-smi path) but gcnArchName unreadable → instructs
user to re-install the HIP SDK; previously fell back silently to CPU
install.sh:
- Add [WARN] to stderr before silent CPU fallback when AMD GPU is confirmed
(rocminfo/amd-smi) but ROCm version cannot be read from any source
(amd-smi, /opt/rocm/.info/version, hipconfig, dpkg, rpm)
- Add [WARN] to stderr when ROCm version is too old (< 6.0) with upgrade link
install.ps1 and setup.sh: no changes needed (already handle these paths correctly)
* fix: robust gfx arch detection for Strix Halo / HIP-runtime-only installs
Covers users who have the HIP runtime (amd-smi available) but not the
full HIP SDK (no hipinfo), which is common on Strix Halo iGPU systems.
Without this, $ROCmGfxArch stays null and the installer silently falls
back to CPU-only PyTorch despite a working GPU.
Detection waterfall (setup.ps1 + install.ps1):
1. hipinfo gcnArchName -- full HIP SDK (existing, unchanged)
2. amd-smi list gfx pattern -- newer amd-smi versions embed arch
3. amd-smi static --asic -- ROCm 6+ ASIC details with GFX target
4. UNSLOTH_ROCM_GFX_ARCH env -- manual override escape hatch
5. GPU name → arch table -- best-effort from marketing name:
890M / Strix Halo → gfx1151 (RDNA 3.5 iGPU, Strix Halo)
880M / Strix Point → gfx1150 (RDNA 3.5 iGPU, Strix Point)
780M / Phoenix → gfx1103 (RDNA 3 iGPU)
RX 7900/7800/7700 → gfx1100 (RDNA 3 desktop)
RX 9070 XT / 9080 → gfx1201 (RDNA 4)
RX 9070 / 9060 XT → gfx1200 (RDNA 4)
When arch is inferred from name, a Cyan substep tells the user to set
UNSLOTH_ROCM_GFX_ARCH to skip inference on future installs.
WMI block intentionally does not set $HasROCm (no runtime confirmation).
Tests: 11 new tests in TestStrixHaloGfxArchDetection covering all five
detection levels, WMI safety, and gfx regex in both ps1 files.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: resolve hipinfo/hipconfig via HIP_PATH/ROCM_PATH when not on PATH
AMD HIP SDK sets HIP_PATH on Windows but does not always add the bin
directory to PATH. Get-Command hipinfo therefore silently fails and
detection falls through to WMI, which cannot provide a gfx arch, leaving
the user with a CPU-only PyTorch install and no warning.
Changes:
- setup.ps1 / install.ps1: before falling through to amd-smi, attempt to
locate hipinfo.exe and hipconfig.exe under $env:HIP_PATH\bin (then
$env:ROCM_PATH\bin) when Get-Command returns nothing
- Emit a [WARN] with the resolved path and a one-liner to permanently fix
PATH via SetEnvironmentVariable
- Emit a [WARN] when HIP_PATH/ROCM_PATH is set but the exe is still not
found (incomplete SDK install)
- Emit a [WARN] with the first hipinfo output line when hipinfo runs but
returns a non-zero exit code (e.g. "no ROCm-capable device detected")
- 18 new tests in TestHipSdkEnvPathResolution; total 183 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat: print HIP SDK path and full hipconfig version in terminal on AMD detection
Both install.ps1 and setup.ps1 now emit substeps under the gpu step when
AMD ROCm is detected:
gpu AMD ROCm (gfx1200)
HIP SDK: C:\Program Files\AMD\ROCm\7.1
hipconfig: 7.1.51803-d3a86bd04
Previously only the gpu label (e.g. "AMD ROCm (gfx1200)") was shown with
no indication of where the SDK was found or which exact build was active.
The full hipconfig build string (e.g. 7.1.51803-d3a86bd04 instead of just
7.1) is now stored in ROCmVersionFull and also used in setup.ps1's
'rocm' step label.
9 new tests in TestHipSdkDetectedSubstep; total 192 passed, 2 skipped
* fix: Strix rocm7.1 segfault bypass + Ubuntu 24.04 HIP gcc-install-dir
Issue 1 (install.sh): gfx1151/gfx1150 + ROCm 7.1 causes a segfault in
torch._grouped_mm (moe_utils.py:167). The Radeon repo now ships cp313
wheels for rocm-rel-7.1, so _amd_gpu_radeon=true silently lands on the
broken combo. When Strix Halo/Point is detected and TORCH_INDEX_URL is
rocm7.1, override to rocm7.2 PyTorch index, update TORCH_CONSTRAINT, and
set _amd_gpu_radeon=false to bypass the Radeon repo entirely. Emits a
clear [WARN] explaining the segfault and linking to the ROCm upgrade docs.
Issue 2 (setup.sh): ROCm 7.x ships clang-20 which on Ubuntu 24.04+ picks
/usr/lib/gcc/x86_64-linux-gnu/14/ (runtime dir, no C++ headers), causing
'cstdlib file not found' and a failed llama.cpp HIP build. Iterate gcc
versions 14→11 to find the first install dir that has both runtime and
/usr/include/c++/<ver> headers, then pass --gcc-install-dir to clang via
CMAKE_HIP_FLAGS. Fix confirmed by h34v3nzc0dex (llama.cpp 417/417 clean).
11 new tests across TestStrixRocm71Override and TestSetupShGccInstallDir;
total 203 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: BNB_ROCM_VERSION in server process + torch._C._distributed_c10d stubs
Two errors visible in training logs on Windows ROCm:
1. Server process bitsandbytes crash:
"Configured ROCm binary not found at libbitsandbytes_rocm713.dll"
The installed BNB wheel ships rocm72.dll (not rocm713.dll). The
training worker already sets BNB_ROCM_VERSION=72 via DLL detection
but the server process (main.py) imported bitsandbytes before that
ran. Fix: add the same DLL-scan + BNB_ROCM_VERSION assignment to
main.py inside the existing win32 guard, before any downstream
import can pull in bitsandbytes.
2. torch.distributed import failure:
"No module named 'torch._C._distributed_c10d'; torch._C is not a package"
torch._C is a C extension on Windows ROCm — Python cannot do
submodule imports from it, so torch.distributed fails to import
before our attribute stubs could ever run. Fix: inject empty
ModuleType stubs for _distributed_c10d, _distributed_autograd and
_distributed_rpc into sys.modules inside the win32 guard in
hardware.py BEFORE importing torch.distributed, so the import
succeeds and our attribute stubs take effect.
9 new tests in TestServerStartupRocmFixes; total 212 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(win32): populate distributed c10d stub with dummy symbols
torch.distributed tries to `from torch._C._distributed_c10d import
FakeProcessGroup` (and ProcessGroup, Work, Store, etc.). The previous
empty ModuleType stub caused an AttributeError on those names.
Populate every stub with a _Dummy class for each known symbol so the
import chain completes silently on Windows ROCm where torch._C is a
compiled extension and its _distributed_c10d submodule doesn't exist.
Adds four new tests in TestServerStartupRocmFixes covering FakeProcessGroup,
ProcessGroup, setattr population, and all three _distributed_* siblings.
* fix(win32): distinguish HIP SDK installed vs GPU not ROCm-accessible
Previously, when hipinfo was found but exited non-zero (e.g. "no
ROCm-capable device detected"), both install.ps1 and setup.ps1 fell
through to the WMI-label-only branch and printed "AMD GPU detected --
HIP SDK not found" -- factually wrong since the SDK binary is present.
Add $HipSdkInstalled flag (set true when hipinfo binary is found,
regardless of exit code). When HipSdkInstalled && !HasROCm:
- Show "AMD GPU detected -- not ROCm-accessible (HIP <ver>)" instead
- Explain this is a driver issue, not an SDK issue, with a link
- Still run hipconfig version capture so version shows in output
- CPU-only hint now says "GPU not ROCm-accessible" not "require HIP SDK"
Also applies to setup.ps1 (same detection block, same branches).
Adds TestHipSdkInstalledButDeviceInaccessible (11 tests).
* fix(win32): scope ROCm workarounds to AMD hosts only
Three Codex-flagged issues where Windows ROCm workarounds incorrectly
applied to Windows CUDA (NVIDIA) machines:
main.py (P1): BNB_ROCM_VERSION was set unconditionally on all win32
hosts. On NVIDIA, bitsandbytes sees BNB_ROCM_VERSION and looks for a
ROCm DLL that doesn't exist, breaking bitsandbytes initialisation.
Fix: gate the block on HIP_PATH/ROCM_PATH being present (ROCm hosts only).
worker.py (P2): torchao stubs were seeded for all win32 runs, shadowing
real torchao on Windows CUDA and silently disabling torchao quantization
for NVIDIA users. Fix: gate on HIP_PATH/ROCM_PATH (win32 ROCm only).
install_python_stack.py (P1): _detect_windows_gfx_arch() only checked
shutil.which("hipinfo"), skipping the HIP_PATH/ROCM_PATH fallback that
the PowerShell installers use. On installs where the HIP SDK bin dir is
not on PATH, _ensure_rocm_torch() returned early without installing
ROCm wheels or bitsandbytes. Fix: mirror the env-var fallback.
* fix(linux): route Strix + ROCm 7.1 to AMD arch-specific index
Instead of falling back to pytorch.org/rocm7.2, the Strix override now
routes to repo.amd.com/rocm/whl/gfx1151/ (or gfx1150/) which serves
torch 2.11.0+rocm7.13.0 -- AMD's build containing the actual _grouped_mm
kernel fix, verified on real gfx1151 hardware by h34v3nzc0dex.
This exercises the real GPU kernel path rather than the rocm7.2 workaround.
UNSLOTH_AMD_ROCM_MIRROR can override the base URL for air-gapped installs.
Also teaches _tauri_torch_index_family to recognise AMD arch-specific URLs
(repo.amd.com/rocm/whl/gfx*) and return the rocm7.13 family label so
_tauri_gpu_branch correctly classifies these installs as rocm.
Suggested by h34v3nzc0dex based on hardware-verified probe results.
* fix(studio/rocm): gate ROCm-only side-effects on active torch runtime
Address five edge cases flagged during PR review:
1. studio/backend/main.py: BNB_ROCM_VERSION was set whenever HIP_PATH or
ROCM_PATH was present in the environment. A Windows CUDA user who once
installed the HIP SDK and reverted to a CUDA torch wheel still has those
env vars set, so bitsandbytes would try to load libbitsandbytes_rocm72.dll
against a CUDA torch and crash. Now probe torch.version.hip inside the
env-var guard (worker.py already does this).
2. studio/backend/main.py: os.add_dll_directory returned handles were
discarded. Per CPython docs, the directory leaves the DLL search list when
the handle is garbage collected. Retain handles in module-level
_ROCM_DLL_HANDLES list so they survive process lifetime.
3. studio/install_python_stack.py: _install_bnb_windows_rocm() returned None
regardless of pip_install_try outcome, and the caller flipped
_rocm_windows_torch_installed to True unconditionally. On a failed BNB
install the post-install "manual install may be required" warning was
suppressed and the user was misled. Helper now returns bool; caller gates
on it.
4. studio/install_python_stack.py: _detect_windows_gfx_arch returned the raw
capture group, so mixed-case hipinfo output ("Gfx1151") missed the
lowercase keys in _GFX_TO_AMD_INDEX_ARCH and silently fell back to CPU
torch. Lowercase the token.
5. studio/install_python_stack.py: UNSLOTH_ROCM_TORCH_INSTALLED=1 early-
return trusted the env var even when the venv was wiped between runs.
Subprocess-probe torch importability first; fall through to the full
install path if the probe fails.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(adds one new test for case 5 fall-through).
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* fix(studio/rocm): worker.py parity + don't roll back ROCm torch on bnb failure
Addresses findings from a 10x reviewer pass on the prior fix commit:
1. studio/backend/core/training/worker.py (parity with main.py):
- Gate the torchao stub block on torch.version.hip / 'rocm' in
torch.__version__ instead of HIP_PATH / ROCM_PATH env-var presence.
Same root cause as main.py: HIP SDK env vars stick around on CUDA hosts.
- Add module-level Windows ROCm DLL registration block. Worker subprocesses
inherit env vars but not the parent's add_dll_directory handles, so the
first `import torch` in the worker could fail to find amdhip64.dll when
HIP_PATH\bin is not on PATH. Mirrors main.py setup. Handles retained at
module scope via _ROCM_DLL_HANDLES.
- Promote _WINDOWS_ROCM_GROUPED_MM_LIB to module scope with `global` in
run_training_process so the torch.library.Library registration survives
past function return / mid-run garbage collection.
- Harden _torch_has_hip() to also accept 'rocm' in torch.__version__
(AMD SDK / Radeon wheels may not set torch.version.hip).
2. studio/install_python_stack.py:
- Don't roll back ROCm torch when bitsandbytes install fails. The prior
commit gated _rocm_windows_torch_installed on _install_bnb_windows_rocm()
returning True; if torch installed successfully but bnb failed, the flag
stayed False and later install steps could overwrite ROCm torch with the
generic CPU torch wheel. Set the flag after torch install; surface bnb
failure as a separate warning instead.
- _detect_windows_gfx_arch now probes in three tiers: UNSLOTH_ROCM_GFX_ARCH
env-var override (matches the PowerShell installer), then hipinfo (PATH
or HIP_PATH\bin), then amd-smi (`static --asic`, `list`). Without the
amd-smi fallback, runtime-only Radeon installs without hipinfo on PATH
made `studio update` return early and leave the venv on CPU torch.
- Linux torch-already-rocm probe in _ensure_rocm_torch now matches the
Windows probe shape: accepts torch.version.hip OR 'rocm' in
torch.__version__ to cover AMD SDK / Radeon Linux wheels.
3. studio/backend/utils/hardware/hardware.py:
- apply_gpu_ids() final-fallback torch probe accepts 'rocm' in
torch.__version__ in addition to torch.version.hip, matching
detect_hardware(). AMD SDK wheels could otherwise leak through with
CUDA-only visibility masks on a spawned ROCm worker.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(no test changes needed; the probe shape that prints the hip version (or
'rocm' sentinel) preserves the existing non-empty-string contract).
Not addressed in this commit (deferred or out of scope):
- Tag drift / lemonade checksum (PR 5303 surface, not this PR).
- install.sh rocm7.2.1 URL: small fix, separate.
- install.ps1 / setup.ps1 'Radeon 8060S' marketing-name fallback table.
- Strix Halo + ROCm 7.1 routing asymmetry in Python update path.
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* fix(studio/rocm): robustness pass - rocm tag normalisation, Strix routing parity, hardened detection
Robustness pass on top of 76137b2d. Four targeted fixes:
1. install.sh ROCm-tag routing normalisation.
`rocm7.2.1` would route to https://download.pytorch.org/whl/rocm7.2.1
which does not exist (PyTorch publishes major.minor URLs only). Same
for any future patch-level tag. Normalise every rocm{maj.min}* pattern
to the bare {maj.min} index URL.
2. install.ps1 + studio/setup.ps1 marketing-name fallback.
The gfx1151 row matched 890M / Strix Halo / HX 37x / HX 38x / AI 9 HX
but not the actual retail name 'AMD Radeon 8060S Graphics' shipped by
OEMs (Ryzen AI MAX+ 395). Add '8060S' to the regex.
3. install_python_stack.py Strix + ROCm 7.1 routing parity with install.sh.
The shell installer reroutes Strix Halo / Point + ROCm 7.1 to
repo.amd.com/rocm/whl/{gfx}/ (which serves torch 2.11.0+rocm7.13.0
with the upstream _grouped_mm fix). The Python `studio update` path
only warned and still installed the broken generic rocm7.1 wheel.
Mirror the override: detect gfx1151/gfx1150 on ROCm 7.1, route to
the AMD per-gfx index, honour UNSLOTH_AMD_ROCM_MIRROR override.
4. _detect_windows_gfx_arch amd-smi parsing tightened.
The amd-smi fallback added in the prior commit used a bare
`\bgfx[1-9][0-9a-z]{2,3}\b` match against the lowercased stdout,
which could pick up stray gfx references in warnings / device-name
strings. Anchor on labelled lines first (Target_Graphics_Version,
ASIC, Arch, gfx) and fall back to the bare match only when no
labelled line is present.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py;
sim_5301 23 cases pass (6 new sims for the Strix override + amd-smi parsing).
* fix(studio/rocm): multi-GPU selection, Strix sibling handling, defensive cleanups
Round 4 robustness pass based on 5 parallel Opus reviewers of head 21773215.
Seven items from across regression / edge-case / error-paths / architecture
reviews:
1. studio/backend/main.py BNB gate: aligned with the broad ROCm check used
everywhere else in this PR (torch.version.hip OR 'rocm' in __version__).
AMD SDK / Radeon Linux wheels do not always populate torch.version.hip;
without this, main.py would silently skip BNB_ROCM_VERSION while worker.py
set it.
2. studio/install_python_stack.py _install_bnb_windows_rocm: init _ok = False
before the try block. Without this, if pip_install_try itself raises
(e.g. OSError on uv binary missing), the finally block restored env vars
correctly but the subsequent `if not _ok:` raised UnboundLocalError,
masking the original exception.
3. studio/install_python_stack.py _detect_windows_gfx_arch:
- Rewrote to use re.findall (not re.search) on both hipinfo and amd-smi
output, dedup tokens preserving order, and select via new
_pick_visible_index() helper.
- HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES (first comma entry, integer)
now picks the right GPU on multi-AMD-GPU hosts. Out-of-range or non-int
values fall back to the first GPU (matches detect_host behaviour in
install_llama_prebuilt.py).
4. studio/install_python_stack.py Strix override now consults the runtime
target before flipping:
- Previous behaviour intersected gfx_codes with {gfx1151, gfx1150} and
picked the first Strix arch, ignoring whether HIP_VISIBLE_DEVICES
selected a non-Strix sibling (e.g. discrete RX 7900 in a mixed APU+dGPU
box). Could install Strix-specific wheels onto a gfx1100 dGPU.
- Now resolves the runtime gfx via _pick_visible_index() and only
overrides when that runtime target is in the Strix set.
5. studio/backend/main.py + studio/backend/core/training/worker.py: ROCm
version dir scan no longer sorts lexically. Previous sort placed "10.0"
before "7.0" alphabetically, which would mis-prioritise ROCm 10.x bin
dirs once AMD ships them. New _ver_key() splits on "." and sorts
numerically with a string fallback.
6. install.sh Strix override URL: replaced ${var%/} (strips one trailing
slash) with a while-loop that strips all trailing slashes, matching
Python's .rstrip("/"). A user setting UNSLOTH_AMD_ROCM_MIRROR with
"http://corp/whl///" no longer ends up with "http://corp/whl///gfx1151/"
which strict pip proxies (artifactory, sonatype) 404 on.
7. studio/install_python_stack.py: bumped torch import probe timeout from
30s to 90s. PyTorch's lazy .so loading can take 60-90s on cold NFS or
USB-backed venvs. The shorter timeout was producing a false "torch
missing" classification and reinstalling a working ROCm torch.
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass (added 7 new sims for
multi-GPU detection, Strix sibling handling, and _ok-init regression).
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* fix(studio/rocm): worker BNB/grouped_mm broad gate, install.sh Strix visibility, runtime-only ROCm detection
Round-5 robustness pass based on 20 parallel reviewers of head 96b9e465.
1. studio/backend/core/training/worker.py - BNB version pin / dynamo disable
/ _grouped_mm fallback block was still gated on torch.version.hip alone
despite the torchao stub block above already using the broad check. AMD
SDK / Radeon Windows wheels (torch.__version__ contains "rocm" but
torch.version.hip is None) silently skipped the Windows ROCm runtime
patches. Aligned to the same broad check (8/20 reviewers).
2. studio/backend/core/training/worker.py - _hip_ver_at_least() now also
parses the ROCm version out of torch.__version__ (e.g. "2.11.0+rocm7.13.0")
when torch.version.hip is missing, so the kernel-fix gate is correct for
SDK / Radeon wheels too.
3. studio/backend/core/training/worker.py - _grouped_mm_safe_impl with
offs=None now picks torch.bmm/matmul for 3-D inputs instead of always
calling torch.mm. The real _grouped_mm accepts 3-D batched matmul; the
prior fallback raised "self must be a matrix" on MoE workloads (2/20).
4. studio/backend/main.py - dropped the HIP_PATH / ROCM_PATH env-var gate
from the BNB block; probe torch directly. Runtime-only Radeon / AMD SDK
Windows installs do not set those SDK env vars but still ship ROCm torch
(5/20 reviewers).
5. install.sh - Strix override now collects every gfx token from
rocminfo / amd-smi (in enumeration order), then indexes by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES so a mixed Strix iGPU + non-
Strix dGPU host where the user selected the dGPU does NOT get rerouted
to the Strix per-gfx index. Mirrors the Python update path (5/20 reviewers).
6. install.sh - Strix detection chain now also probes `amd-smi static --asic`,
matching the PowerShell installer (1/20). Closes the gap on runtime-only
Strix hosts where `amd-smi list` does not surface a gfx token.
7. studio/install_python_stack.py - _has_rocm_gpu() now has the sysfs KFD
topology fallback (/sys/class/kfd/kfd/topology/nodes/*/gpu_id), matching
install.sh. On minimal package-managed installs without rocminfo /
amd-smi GUI tools, `studio update` can now detect the GPU and repair the
venv instead of returning early (2/20).
8. studio/install_python_stack.py - _detect_amd_gfx_codes() now falls back
to `amd-smi list` and `amd-smi static --asic` when rocminfo is missing
(2/20). Strix routing on runtime-only Radeon hosts now matches what
install.sh has done for a while.
9. studio/install_python_stack.py - Strix override now applies even when
has_hip_torch is True. The whole point of the override is to repair an
existing broken torch.version.hip == "7.1" install; skipping the
reinstall left users on the known _grouped_mm segfaulting stack (3/20).
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass. sim_cross 12 pass.
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* fix(studio/rocm): code review hardening pass
- main.py: numeric DLL sort (string sort picked rocm72 over rocm713);
add basename() to regex; log warning on detection failure; log info
when BNB_ROCM_VERSION is set (mirrors worker.py)
- worker.py: explicit len-guard in _hip_ver_at_least() with warning
logs instead of silent IndexError/ValueError swallow
- hardware.py: isinstance(result, dict) guard before result.get() in
_smi_query() to prevent AttributeError on non-dict backend returns
- amd.py: round() before int() on parsed GPU IDs; log warning when
truncation occurs (defensive against malformed amd-smi output)
- setup.sh: quote --gcc-install-dir value in CMAKE_HIP_FLAGS so paths
with spaces do not break the CMake argument
- install.ps1, setup.ps1: apply colon-split + ToLower() to hipinfo
gcnArchName match (consistent with each other and with setup.sh)
- install.sh: tighten ROCm tag case patterns to explicit
rocmX.Y|rocmX.Y.* to avoid unintended prefix matches
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* fix(studio/training): GPU OOM guard to prevent system freeze on VRAM exhaustion
On RDNA 4 (gfx1200/gfx1201) and other ROCm GPUs, exhausting VRAM can
cause a HIP driver hang that freezes the entire system rather than
raising a recoverable Python exception.
Two-part fix:
- set_per_process_memory_fraction(0.90) caps the HIP/CUDA allocator at
90% of VRAM so PyTorch raises OutOfMemoryError before hitting the
hardware limit, keeping the driver alive and the system responsive
- top-level exception handler detects OOM errors by type and message
and surfaces a clear actionable message to the UI (reduce
max_seq_length, enable gradient_checkpointing, lower batch size)
instead of the raw CUDA/HIP error string
* fix(studio/rocm): OOM guard ROCm-only + unified memory, multi-GPU arch selection
OOM guard (worker.py):
- Scope to _hw.IS_ROCM only -- NVIDIA CUDA has a graceful OOM path and
does not need the allocator cap
- Detect unified memory by comparing torch VRAM against psutil system RAM;
use 0.80 on unified-memory APUs (gfx1151 Strix Halo) where the GPU pool
is carved from host RAM, 0.90 on discrete cards
Multi-GPU arch selection:
- install.ps1 / setup.ps1: replace -match (first hit only) with
[regex]::Matches() to collect all gcnArchName entries, then index by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES
- install_python_stack.py: index into full token list before dedup so
HIP_VISIBLE_DEVICES=2 on [gfx1100, gfx1100, gfx1151] resolves gfx1151
- install.sh: remove awk dedup from gfx token collection for same reason
GCC multiarch (setup.sh):
- Only append -linux-gnu when gcc -print-multiarch does not already return
the full triple, fixing double-suffix on Ubuntu 24.04
* fix(tests): update ROCm version cap expectations from rocm7.1 to rocm7.2
Daniel's normalisation commit updated the cap from rocm7.1 to rocm7.2
since PyTorch now publishes that index and rocm7.2 ships torch 2.11.0.
Test expectations were stale.
* fix(tests): correct MLX smoke test losses_per_step assertion
logging_steps=1 with max_steps=30 produces 30 loss entries, not 7.
The assertion was stale from a previous config.
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* fix(studio/worker): detect unified-memory APU by GPU name not VRAM/RAM ratio
The previous heuristic (VRAM > 50 % of system RAM) false-positived on discrete
cards in low-RAM systems — e.g. RX 9060 XT 16 GB on a 16 GB or 24 GB machine
would trip the unified-memory path and log "unified memory host" when it should
say "discrete".
AMD iGPUs (gfx1150/gfx1151 Strix Halo, Strix Point, etc.) expose names with a
digit+M suffix ("AMD Radeon 890M"), while discrete cards use "RX NNNN [XT|XTX]"
naming. Matching that suffix is reliable across all current ROCm-capable AMD
consumer GPUs and does not require psutil.
Also includes the device name in the log line to ease future debugging.
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* fix(install/setup.ps1): force array on hipinfo gcnArchName parse to fix single-GPU arch truncation
When [regex]::Matches() finds exactly one match, PowerShell's pipeline
unwraps the result to a scalar string. Indexing a scalar string with [0]
returns the first *character*, so a one-GPU system would parse
gcnArchName "gfx1200" as "g", which is not in the supported arch map
and triggers the CPU-only fallback.
Wrapping with @() forces the result to remain an array regardless of
match count. On a single-GPU machine the arch is now correctly read as
"gfx1200" (or whatever the full name is) so the ROCm wheel index is
selected.
Reproducer: hipinfo exits 0 and outputs exactly one gcnArchName line.
Without @(), $_hipAllArches = "gfx1200" (String); $_hipAllArches[0] = 'g'.
With @(), $_hipAllArches = @("gfx1200") (Object[]); $_hipAllArches[0] = "gfx1200".
* fix(studio/rocm): classify unified-memory APU via VRAM/RAM ratio, not arch list
Replace the gcnArchName allowlist {gfx1150, gfx1151} with a
psutil-based heuristic: unified APUs expose the entire system RAM
as the HIP pool (ratio ≥ 0.90), discrete cards are well below that.
No arch name required — future APUs classify correctly without code changes.
Also removes the stale import re / \d[Mm]\b device-name regex that
5d84704 left behind, and logs vram/sys GiB for easier on-hardware
verification.
Addresses h34v3nzc0dex review: Radeon 8060S (gfx1151, 128 GiB
unified) now correctly gets 0.80 cap instead of 0.90.
* fix(studio/rocm): revert to gcnArchName for unified-memory APU classification
VRAM/RAM ratio >= 0.90 false-positives on machines where discrete VRAM
equals system RAM (e.g. RX 9060 XT 16 GB + 16 GB system RAM → ratio 1.0,
incorrectly classified as unified → wrong 0.80 cap applied).
gcnArchName is the correct signal: naming-independent, stable within a
product family, and already parsed throughout this PR. Unified set is
{gfx1150, gfx1151} (Strix Point + Strix Halo).
* fix(studio/llama-prebuilt): resolve hipinfo via HIP_PATH/ROCM_PATH on Windows
shutil.which("hipinfo") returns None when the HIP SDK bin dir is not on
PATH -- the HIP SDK installer sets HIP_PATH/ROCM_PATH but does not always
add the bin dir to PATH. This caused has_rocm=False in the prebuilt asset
selector, so AMD ROCm machines got the CPU llama.cpp zip instead of the
HIP one, silently running all chat inference on CPU.
Add _resolve_exe() that falls back to %HIP_PATH%\bin and %ROCM_PATH%\bin
when shutil.which() finds nothing, mirroring the same fallback already
present in setup.ps1.
* fix(studio/llama-prebuilt): pass --has-rocm from setup.ps1 to skip re-detection
The Python prebuilt installer re-detects ROCm independently via
shutil.which("hipinfo"), which fails when hipinfo is not on PATH
(HIP SDK sets HIP_PATH but doesn't always add the bin dir to PATH).
This caused has_rocm=False and downloaded the CPU llama.cpp zip even
on confirmed AMD ROCm machines.
setup.ps1 already performs reliable ROCm detection with its own
HIP_PATH/ROCM_PATH fallback. Add --has-rocm flag to
install_llama_prebuilt.py so setup.ps1 can forward its result directly,
and pass it whenever $HasROCm is true. The Python script then overrides
has_rocm=True in the HostInfo without re-probing.
* fix(studio/llama-prebuilt): add HIP asset to simple-policy Windows path
direct_upstream_release_plan (used by --simple-policy, which setup.ps1
always passes) only checked has_usable_nvidia on Windows and fell
straight to CPU for AMD ROCm machines, ignoring has_rocm entirely.
The --has-rocm override had no effect because the simple-policy code
path never reached resolve_asset_choice where has_rocm was checked.
Add an elif branch for has_rocm that tries the upstream HIP asset
(llama-TAG-bin-win-hip-radeon-x64.zip) before falling through to the
CPU fallback, consistent with the non-simple-policy path.
* fix(studio/setup.ps1): auto-remove mismatched llama.cpp install kind
When an existing llama.cpp install is the wrong kind for the current
GPU (e.g. windows-cpu on an AMD ROCm machine that should have
windows-hip), the prebuilt installer skips on tag match and never
upgrades. Read install_kind from UNSLOTH_PREBUILT_INFO.json before
invoking the installer and remove the directory if the kind doesn't
match, forcing a fresh download of the correct variant.
* fix(studio/setup.ps1): show live PyTorch install output in verbose mode for ROCm
The ROCm torch reinstall (setup.ps1 phase) always silently captured
output, so in --verbose mode the torch downgrade mid-install
(2.11.0+rocm → 2.10.0 → 2.11.0+rocm) looked like the final state was
2.10.0. Match the CPU/CUDA blocks which show live uv output when
$script:UnslothVerbose is set.
* fix(rocm/windows): set ROCBLAS_TENSILE_LIBPATH for bundled rocblas.dll
The llama.cpp ROCm prebuilt bundles rocblas.dll next to the binary but
not the Tensile kernel library files it depends on at runtime
(rocblas/library/TensileLibrary*.dat + *.hsaco). The bundled DLL
searches for these files relative to its own location by default, i.e.
<binary_dir>/rocblas/library/, which does not exist in the prebuilt
install tree. This causes a silent crash on the very first GEMM
(prefill) with no output from llama-server, seen by the caller as
WinError 10054 / 10061. Model load and the single-token warmup pass
because they use simpler code paths that do not trigger rocBLAS GEMM.
Fix: set ROCBLAS_TENSILE_LIBPATH in the subprocess env to
<HIP_PATH>/bin/rocblas/library so the bundled DLL finds the kernel
files from the system ROCm installation. Uses setdefault so a user-
supplied env var is never overwritten. No-ops on CUDA and CPU (no
HIP_PATH) and on Linux (win32 branch only).
Reproducer log:
rocBLAS error: Cannot read .../Release/rocblas/library/TensileLibrary.dat
rocBLAS error: Could not initialize Tensile host:
directory_iterator: The system cannot find the path specified.
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* fix(install.sh): restore gfx token dedup in Strix multi-GPU awk indexer
536a54df removed the per-source `| awk '!seen[$0]++'` dedup from the
_gfx_all collection step but left the indexer awk as bare NF, so on a
mixed-arch host (e.g. dGPU gfx1100 + Strix iGPU gfx1151) where
rocminfo emits each gfx token twice (Name: field + ISA triple),
HIP_VISIBLE_DEVICES=1 indexed vals[1] = the second gfx1100 occurrence
instead of gfx1151, triggering the Strix routing on the wrong GPU.
Add !seen[$0]++ to the indexer awk so duplicate tokens from the same
GPU collapse to one entry before the HIP_VISIBLE_DEVICES index is
applied -- matching exactly what the Python side does with dict.fromkeys()
in _detect_amd_gfx_codes(). The comment above the block ("skip
duplicates") already documented this as the intended behaviour.
* fix(studio/install): correct _TOTAL progress count on Windows
base_total += 3 fired for all non-macOS platforms including Windows,
but flash-attn (line 1620) and ROCm torch final (line 1705) are both
guarded by 'not IS_WINDOWS and not IS_MACOS', so on Windows with torch
enabled _TOTAL was 13 while only 11 _progress() calls actually execute.
Split into +1 for the ROCm torch check (all non-macOS) and +2 for the
two Linux-only steps, so Windows gets _TOTAL=11 and Linux gets 14.
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* fix(install.ps1): enforce torch>=2.11.0 for gfx120X and Strix on Windows
The AMD arch-specific index (repo.amd.com/rocm/whl/gfx120X-all/ and
gfx1151/) publishes torch wheels from 2.7.1 through 2.11.0. Without a
version floor pip can resolve to torch 2.10.0+rocm7.12 on RDNA 4
(gfx120X) or torch 2.10.0+rocm7.1 on Strix (gfx1151/gfx1150), both of
which have a null-pointer crash in torch._C._grouped_mm (TheRock
issues #5284 / #3284). torch 2.11.0+rocm7.13 contains the fix.
Add $ROCmTorchFloor alongside $ROCmIndexUrl: set to torch>=2.11.0 for
the two affected arch families, null for all others. Wire it into the
uv pip install call so the broken wheels are never selected.
* fix(rocm/windows): address Codex nits - deterministic DLL suffix, CUDA llama.cpp kind, HIP_VISIBLE_DEVICES arch indexing
- install_python_stack.py / worker.py: _detect_bnb_rocm_dll_ver() and the
inline worker probe now collect ALL libbitsandbytes_rocm*.dll suffixes and
return max() by numeric value instead of stopping at the first glob hit.
Filesystem glob order is not guaranteed; this ensures '713' always wins
over '72' when both variants are present in the wheel.
- setup.ps1 (expectedKind): add 'windows-cuda' branch so NVIDIA hosts are
not treated as 'windows-cpu'. Previously an existing windows-cuda prebuilt
was always considered a mismatch on non-ROCm machines, forcing an
unnecessary re-download on every update.
- setup.ps1 (amd-smi gfx arch): collect ALL gfx tokens from amd-smi list
output in GPU order and honour HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES
when selecting which arch to use. On mixed-arch AMD systems where the
visible GPU is not the first enumerated one, this prevents installing an
incompatible wheel index. Falls back to index 0 (same as before) when the
visibility var is unset or is a comma-separated list.
- test_rocm_support.py: add test_picks_highest_suffix_when_multiple_dlls to
cover the multi-DLL case that was previously untested.
* fix(rocm): misleading amd-smi log, BNB spec consistency, torch ceiling for AMD index
amd.py: split 'returncode != 0 or not stdout' into two separate branches.
Previously, exit-0 with empty output logged 'amd-smi returned code 0' (which
reads as success, not a warning) and incorrectly incremented the circuit-breaker
counter. Now: non-zero exit logs the code and counts toward the limit as before;
empty stdout on exit 0 logs at DEBUG level and does not penalise the counter
(amd-smi --json always emits at least [] on exit 0, so this branch is rare and
is not a tool failure).
main.py: replace spec.origin / os.path.dirname() with
spec.submodule_search_locations to match install_python_stack.py and worker.py.
For normal wheel installs both approaches reach the same directory, but using
submodule_search_locations is the canonical way and handles editable bitsandbytes
installs correctly. Also use max() by numeric suffix (same as the other two sites)
instead of a sort-then-break loop.
install.ps1: add <2.12.0 ceiling to the torch constraint for gfx120X (RDNA 4)
and gfx1151/gfx1150 (Strix). AMD actively publishes new versions on their
per-arch index; without a ceiling, a future 2.12.0+rocmX.Y wheel would be
pulled in automatically before being validated on these architectures. The
ceiling matches the existing Linux install_python_stack.py constraint for the
same arches. Bump both when 2.12.x is confirmed working.
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* fix(rocm): torch floor in setup.ps1, torchvision pin for Strix, rocmsdk in _hip_ver_at_least
setup.ps1: add \ (mirrors install.ps1) and derive \
from it. Previously the AMD index install called 'Fast-Install torch torchvision
torchaudio --force-reinstall --index-url \' with no version
constraint, so pip could resolve torch 2.10.0+rocm7.12 for gfx1151/gfx1200 --
the exact broken wheel the PR is meant to avoid. Now gfx120X and Strix enforce
'torch>=2.11.0,<2.12.0', matching install.ps1 and the Linux constraint.
install_python_stack.py: pin torchvision and torchaudio in _strix_override_pkgs.
The Strix Linux override uses --index-url (exclusive, no PyPI fallback); bare
unversioned 'torchvision' and 'torchaudio' could resolve a build from AMD's
index targeting a different torch major, causing ABI/version mismatches at
runtime. Now pinned to '>=0.26.0,<0.27.0' and '>=2.11.0,<2.12.0' respectively,
matching _ROCM_TORCH_CONSTRAINT['rocm7.2'].
worker.py: extend _hip_ver_at_least to handle AMD SDK wheel version strings.
The fallback regex r'rocm(\d+)\.(\d+)' cannot match '2.9.0+rocmsdk20251116'
(no rocmX.Y component), so the function always returned False on SDK/Radeon
wheels -- installing the Python _grouped_mm workaround on wheels that already
have the working HIP kernel. Added a second check: if the version string
contains '+rocmsdk', assume >= 7.13 (the rocmsdk format post-dates the
gfx120X null-kernel fix) and skip the fallback.
* fix(rocm): warn on OOB HIP_VISIBLE_DEVICES, bail on empty numeric_ids mask
- setup.ps1: when HIP/ROCR_VISIBLE_DEVICES names an index beyond the
detected GPU count, emit a yellow warning and fall back to GPU 0
instead of silently reading allGfxArches[-1] (wrong arch)
- hardware.py _reconcile_primary_rocm_unified_memory: distinguish
numeric_ids=None (no env var, use torch ordinal 0) from numeric_ids=[]
(empty mask / HIP_VISIBLE_DEVICES=-1, no GPU visible); bail out early
in the empty case to avoid querying torch.device(0) incorrectly
* fix(rocm): gate StubSubpackageFinder on win32 ROCm, add gcnArchName fallbacks
- worker.py _StubSubpackageFinder: the meta_path append was running on
every platform on every call to run_training_process; moved it inside
the if _is_win32_rocm: block since stubs are only seeded there and the
finder is a pure accumulation on Linux/Windows CUDA
- worker.py OOM guard: AMD SDK / Radeon wheels may not populate
gcnArchName, causing Strix Halo to be misclassified as discrete and
get the 0.90 cap (12.8 GB OS headroom) instead of 0.80 (25.6 GB);
now tries gcn_arch_name / arch_name / gfx_arch_name variants first,
then falls back to device-name matching (890M -> Strix Halo,
880M -> Strix Point) with a debug log when the fallback fires
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* fix(rocm): pin torchvision/torchaudio in setup.ps1, remove -Unique from arch array
- setup.ps1 ROCm torch install: torchvision and torchaudio were passed
bare alongside pinned torch>=2.11.0,<2.12.0 for gfx1151/gfx1200 arches.
AMD publishes packages independently so a future torchvision 0.27 (for
torch 2.12) on the same arch index would cause pip ResolutionImpossible
or an ABI-incompatible install. Added torchvisionFloorMap and
torchaudioFloorMap mirroring install_python_stack.py's strix override
(torchvision>=0.26.0,<0.27.0, torchaudio>=2.11.0,<2.12.0) and derived
ROCmVisionSpec/ROCmAudioSpec used in all three Fast-Install call sites.
- setup.ps1 amd-smi arch detection: Select-Object -Unique was collapsing
same-arch multi-GPU arrays (e.g. two gfx1151 APUs -> 1-element array)
causing HIP_VISIBLE_DEVICES=1 to trigger a false out-of-range warning
and fall back to GPU 0 even though the correct GPU would have been at
index 1. Removed -Unique; added comment noting the positional-index
assumption and its non-contiguous-GPU limitation.
* fix(rocm): add 8060s/8050s to OOM guard device-name fallback, extract classifier helper
Path 3 of the OOM guard device-name fallback only checked for 890m/880m
(gfx1150 Strix Point SKU names). Strix Halo (gfx1151) ships as Radeon 8060S
(Ryzen AI MAX+ 395) and Radeon 8050S (cut-down SKU) -- neither matches, so
the fallback returned is_unified=False and applied the 0.90 fraction instead
of 0.80, leaving ~12.8 GiB OS headroom on a 128 GiB pool instead of ~25.6 GiB.
Fix: add 8060s and 8050s to the name-match set. Also correct the comment that
mislabelled 890M as a Strix Halo name (it is Strix Point).
Refactor: extract the three-path classifier into _rocm_classify_unified_memory()
so it can be unit-tested directly. Add 31 test cases in test_rocm_oom_guard.py
covering all three paths and the regression case (Radeon 8060S Graphics).
Reported-by: h34v3nzc0dex
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* fix(rocm): pass explicit dtype on bf16-unsupported hardware (RDNA2)
dtype=None lets unsloth auto-detect the model dtype. On RDNA2 (gfx103x,
e.g. RX 6600) is_bfloat16_supported() incorrectly returns True, so unsloth
picks bf16 and the first bf16 kernel dispatch triggers:
LLVM ERROR: Cannot select: intrinsic %llvm.amdgcn.fdot2.bf16.bf16
Replace every dtype=None in load_model() with _auto_dtype which resolves
to None when bf16 is supported (all modern NVIDIA + RDNA3+) and
torch.float16 otherwise. This gives RDNA2 users a working float16
training path without touching NVIDIA behaviour at all.
Fixes: https://github.com/unslothai/unsloth/issues/5337
* fix: reduce log noise for expected non-issues on Windows ROCm
Three log lines fired at warning/error level for conditions that are
completely expected on a Windows HIP SDK-only setup:
amd.py
- amd-smi WinError 2 (FileNotFoundError): downgrade warning -> debug.
amd-smi ships with Adrenalin, not the HIP SDK; absence is normal.
- 'disabling' message: downgrade warning -> info with clearer text
'not available (not installed; expected on HIP SDK-only systems);
GPU VRAM polling disabled'
hardware.py
- torch.distributed.Store missing: downgrade warning -> debug.
The distributed stub added in this PR intentionally omits Store; the
attention-impl fallback to eager is expected and non-actionable.
worker.py
- causal-conv1d: add early Windows exit (info) in both
_ensure_causal_conv1d_fast_path and _causal_conv1d_install hook;
no cp313/win_amd64 wheel exists, so the install always fails.
- FLA: add early Windows exit (info) in
_ensure_flash_linear_attention_unconditional; triton dependency has
no cp313/win_amd64 wheel.
- Defense-in-depth: _install_package_wheel_first non-HIP PyPI failure
logs info+debug on Windows instead of error; FLA failure logs
info+debug on Windows instead of warning.
* [AMD] FIx installation of bitsandbytes when it's from .dev and skip rebuilding llama.cpp if we build it manually.
* fix: use force_pip for Windows ROCm bitsandbytes prebuilt wheel install
uv rejects the bnb continuous-release wheel due to filename/metadata
version mismatch (1.33.7.preview vs 0.50.0.dev0). Switch to force_pip=True
(pip bypass) instead of the UV_SKIP_WHEEL_FILENAME_CHECK env var workaround
-- cleaner and consistent with how the Linux path handles it.
BNB_ROCM_VERSION is still set post-install to the detected DLL suffix so
the worker subprocess loads the correct libbitsandbytes_rocm{VER}.dll even
when torch.version.hip reports a newer HIP version than the wheel ships.
* fix: three small correctness fixes found in PR review
- _install_bnb_windows_rocm: use UV_SKIP_WHEEL_FILENAME_CHECK=1 with
try/finally instead of force_pip=True so the env var is always
restored and the failing CI test passes
- _determine_attention_impl_for_gpu_estimate: gate torch._C distributed
stubs on IS_ROCM so Windows CUDA users keep the real extension
- install.ps1 amd-smi fallback: collect all gfx tokens and index by
HIP_VISIBLE_DEVICES, matching the hipinfo path on multi-GPU hosts
* fix: stub torchao in export subprocess on Windows ROCm
On Windows, the ROCm build of PyTorch ships without the distributed
C extension (torch._C._distributed_c10d). torchao, which is pulled in
transitively by transformers.quantizers at import time, walks into
torch.distributed._functional_collectives -> distributed_c10d and
crashes with:
No module named 'torch._C._distributed_c10d'; 'torch._C' is not a package
This only affected the export subprocess because the training subprocess
already applied an identical torchao stub (introduced separately to fix
the same root cause). The export subprocess had no such guard and died
during 'Importing Unsloth...' before any model loading could happen.
Fix: apply the same _StubSubpackageFinder / torchao stub pattern to the
export subprocess entry point, gated on Windows ROCm detection, before
any import of transformers or unsloth_zoo.
Root cause tracked in ROCm/TheRock#3284 (libuv / torch.distributed
missing on Windows ROCm builds).
Ref: https://github.com/ROCm/TheRock/issues/3284
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* install.sh, setup.sh: add GPU arch step logging to match PS1 scripts
Both shell scripts were missing the step "gpu" terminal log block that
install.ps1 and setup.ps1 emit. This adds equivalent output: GPU label
with gfx arch (e.g. "AMD ROCm (gfx1151)"), ROCm root path, hipconfig
version, and marketing name substep. Includes the same gfx arch detection
chain (rocminfo → amd-smi list → amd-smi static --asic), UNSLOTH_ROCM_GFX_ARCH
env override, and name-based arch inference table (Strix Halo/Point, RDNA 3/4)
as the PS1 versions. install.sh also replaces bare echo blocks for the AMD
ROCm and CPU-only cases with formatted substep output.
* Fix BNB_ROCM_VERSION gate, ROCm GPU mask preference, APU unified memory and Release build for PR #5301
- main.py: gate BNB_ROCM_VERSION on the rocm bnb DLL or HIP_PATH/ROCM_PATH instead of importing torch on every Windows host
- hardware.py: prefer HIP/ROCR visible-device masks only on ROCm hosts so a stale mask cannot override CUDA_VISIBLE_DEVICES on NVIDIA
- llama_cpp.py: set GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 only for unified-memory APUs (gfx1150/gfx1151)
- setup.sh: pass -DCMAKE_BUILD_TYPE=Release for the HIP source build
- add test_amd_apu_unified_memory.py
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* fix: guard recompile_limit + fix AMD VRAM monitor fallback
trainer.py: torch._dynamo.config.recompile_limit does not exist in
some ROCm torch builds (e.g. pytorch.org/whl/rocm6.2 wheels). Guard
the assignment so training doesn't crash on RDNA2/RDNA3.
hardware.py: when amd-smi/nvidia-smi is unavailable or returns no
usable data (HIP SDK-only Windows, Docker, unexpected JSON format),
the existing fallback used torch.cuda.memory_allocated() which is
process-specific and reads near-zero even with a fully loaded model.
Switch to torch.cuda.mem_get_info() via _torch_get_per_device_info()
which reports system-wide VRAM occupancy so the GPU monitor shows
real usage on all AMD systems without requiring amd-smi.
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* fix: Windows VRAM monitor via Performance Counter API
When amd-smi/nvidia-smi is unavailable on Windows, query dedicated GPU
VRAM via Windows Performance Counters (same source as Task Manager).
This gives system-wide cross-process usage, fixing the near-zero reading
caused by torch.cuda.mem_get_info only seeing the Studio server process.
Linux fallback path unchanged (mem_get_info is system-wide on ROCm).
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* fix: rename to _rocm_windows_perf_counter_vram_gb, scope to IS_ROCM
Function is AMD ROCm specific — amd-smi absent on Windows when only the
HIP SDK is installed. Scoped to IS_ROCM so NVIDIA Windows path is
untouched (nvidia-smi handles that case).
* fix: AMD VRAM monitor — Linux DRM sysfs + Windows perf counter
Linux: read /sys/class/drm/card*/device/mem_info_vram_used|total for
system-wide GPU memory across all processes. No tools required, always
present on Linux AMD systems.
Windows: Windows Performance Counter API (already added).
Both paths are gated on IS_ROCM and only fire when amd-smi is absent.
torch mem_get_info remains as last resort (process-local).
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* fix: AMD GPU monitor — utilization, temperature, and power for Windows and Linux fallback paths
- Windows: GPU utilization via \GPU Engine(*engtype_3D*)\Utilization Percentage perf counter
- Windows: temperature and power via ADL (atiadlxx.dll, ships with Adrenalin)
- Linux: GPU utilization via DRM sysfs gpu_busy_percent
- Linux: temperature via hwmon temp1_input (millidegrees C)
- Linux: power via hwmon power1_average / power1_input (microwatts)
All paths are no-op fallbacks (None) when the source is unavailable.
Mirrors what nvidia-smi provides on the CUDA path.
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* fix: remove ADL ctypes — does not support AMD iGPU (Strix Halo)
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Erland366 <erland.pg366@gmail.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* fix: honor --ctx-size and other forwarded args from `unsloth studio run` in Studio's context-fit logic
* refactor: extract resolve_requested_ctx as single source of truth
The test helper was reimplementing the two-line
'ctx_override = parse_ctx_override(...); requested_ctx = ctx_override
if ctx_override is not None else n_ctx' pattern locally, so the test
asserted against its own reimplementation rather than production logic.
Extract the conditional into resolve_requested_ctx and have both the
production caller and the test use it.
* fix(studio): honor pass-through cache type flags in KV VRAM estimate
Studio's KV cache VRAM estimate computed from the first-class
cache_type_kv even when the user passed -ctk/--cache-type-k/-ctv/
--cache-type-v via extras. Those flags reached llama-server fine
(last-wins on the CLI) but the pre-launch estimate kept using the
default f16 bytes-per-element, so GPU placement decisions could be
off when the user lowered cache precision via pass-through.
Adds parse_cache_override + resolve_cache_type_kv in llama_server_args.py
(mirroring parse_ctx_override / resolve_requested_ctx), wires both into
load_model alongside the existing ctx resolution, and adds focused
unit tests for the parser + resolver.
Follow-up to @rolandtannous review on #5815.
---------
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Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* clear mrope state after generation
* move clear mrope to here
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* added remote MCP server support
* trim
* added tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* increased timeout
* disabling MCP chat toggle
* Fix MCP OpenAI function-name validation + cancel propagation for PR #5750
OpenAI requires function.name to match ^[a-zA-Z0-9_-]{1,64}$ before
streaming starts. The existing 64-char length check is necessary but
not sufficient: MCP servers can return tool names containing '.', '/',
spaces, etc. that would 400 the whole chat request. Validate the
composed mcp__<server_id>__<tool> name against the regex, skip + warn
on miss, and drop duplicate tool names from the same server (which
would also 400 the request as "duplicates").
Also propagate the agentic-loop cancel_event into MCP tool execution
so a /cancel POST during a long-running MCP call (e.g. GitHub MCP
search across a large repo) actually interrupts the in-flight HTTP
call instead of waiting out the 300 s timeout. The watcher polls the
threading.Event at 50 ms cadence inside the asyncio loop (matches
routes/inference.py's existing cancel-watcher cadence) and races
against the call task with asyncio.wait FIRST_COMPLETED.
Tests added:
- test_mcp_specs_skip_invalid_openai_function_names: drops bad chars
- test_mcp_specs_skip_empty_tool_name
- test_mcp_specs_drops_duplicate_names
- test_call_tool_sync_respects_pre_set_cancel_event
Also fix test_desktop_auth.py's router stub that listed every existing
router but missed mcp_servers_router, so importing main.py fails after
this PR adds it to routes/__init__.py.
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* PR #5750 round 2: OAuth cleanup on delete/url-change + mcp_enabled standalone
Round 2 of cross-platform validation surfaced two more P1 findings:
1. OAuth tokens never get cleared. fastmcp keys tokens by MCP URL, not by
server row, and delete / URL change / use_oauth toggle only updated
the SQLite row. Re-registering the same URL would silently reuse the
old account's credentials. Adds clear_oauth_tokens_async() in
mcp_client.py and calls it from the delete + put route handlers when
the row had use_oauth=True and either the URL changes or OAuth is
turned off.
2. mcp_enabled=true was ignored unless the caller also sent
enable_tools=true. The frontend always sends both together so the UI
path was fine, but a direct API caller sending only mcp_enabled would
silently get no MCP tools, which contradicts the field's documented
"append tools from every enabled MCP server" behavior. Loosens the
use_tools gate in both the GGUF and safetensors paths so mcp_enabled
opens the tool loop on its own; when the caller did not also opt
into built-ins, the built-in list starts empty.
Tests added:
- test_clear_oauth_tokens_async_no_op_safe
- test_delete_server_calls_oauth_cleanup_when_oauth_was_on
- test_delete_server_skips_oauth_cleanup_when_oauth_off
- test_update_server_clears_oauth_on_url_change
- test_update_server_clears_oauth_when_oauth_disabled
26 backend MCP tests pass; full studio/backend suite 1710 passed locally.
Cross-platform CI (Linux, macOS, Windows) green on staging fork.
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* PR #5750 round 3: reject null bool updates + /test surfaces 400
Round 3 of cross-platform validation:
1. PUT /api/mcp/servers/<id> would 500 with TypeError when the body
explicitly set is_enabled or use_oauth to null. Pydantic accepts
None for an Optional[bool] and _changes_from_payload then passed
None into mcp_servers_db.update_server, which int(None)d. Reject
explicit null at the validation layer with 400 instead.
2. POST /api/mcp/servers/test caught HTTPException under
"except Exception", so an invalid URL came back as HTTP 200 with
{"ok": false, "error": "400: ..."} instead of a real 400. The
create + update paths return 400 for the same input. Move
validation outside the transport try/except so it surfaces 400.
Tests added:
- test_changes_from_payload_rejects_null_is_enabled
- test_changes_from_payload_rejects_null_use_oauth
- test_test_endpoint_surfaces_url_validation_as_400
* PR #5750 round 4: hyphenated MCP tool names + empty-tool-list gate
Round 4 surfaces two more interaction bugs between the new MCP path
and existing safetensors tool plumbing:
1. OpenAI accepts ^[a-zA-Z0-9_-]{1,64}$ for function.name, and round 1
widened the MCP regex to that set, so MCP tools can now be advertised
as `mcp__srv__list-issues`. But the XML tool-call parser in
tool_call_parser.py used `\w+` (no hyphen), so the model could call
the tool but Studio could not parse the call. Same in
routes/inference.py's `_TOOL_XML_RE` stripper, which would leave
hyphenated tool-call XML in the visible content. Both regexes now
use `[\w-]+`.
2. safetensors_agentic treats `tools=[]` as "allow all" (documented
contract, exercised by test_empty_tools_list_does_not_enforce_allowlist).
When a caller sends `enable_tools=true` + `enabled_tools=[]` +
`mcp_enabled=true` and MCP discovery returns 0, the resolved tool
list is genuinely empty and built-in tools (web_search / python /
terminal) could execute via the model's emitted call. Fix at the
route gate instead of breaking the documented contract: set
`use_tools=False` when the resolved list is empty, in both GGUF and
safetensors paths. Existing callers who omit `enabled_tools` still
get ALL_TOOLS and are unaffected.
Tests added (32 total):
- test_tool_xml_parser_handles_hyphenated_function_names
- test_tool_xml_strip_handles_hyphenated_function_names
- test_safetensors_agentic_empty_allowlist_still_means_allow_all
(documents the contract round 4 preserved)
1716 passed locally; cross-platform CI on staging fork still green.
* PR #5750 round 5: GGUF allow-list + CLI policy + hyphenated params + cancel race
Round 5 of parallel-reviewer aggregation surfaced six additional
findings; five are real and fixed here:
1. Hyphenated MCP parameter names (`<parameter=issue-number>`) were
dropped by the XML parser's `\w+` regex. Extended to `[\w-]+` in
both core/inference/tool_call_parser.py and core/tool_healing.py.
The latter is GGUF's own copy of the parser/strip patterns and was
missed by round 4.
2. core/tool_healing.py's `strip_tool_call_markup` still used
`<function=\w+>` so hyphenated MCP tool-call XML leaked into the
GGUF visible content even after round 4 fixed the shared parser.
3+4. `mcp_enabled` re-opened the tool loop even when the operator
passed `unsloth run --disable-tools` (CLI policy False). Round 2's
`(_tools_on or payload.mcp_enabled)` gate ignored the raw process
policy. Now reads `state.tool_policy.get_tool_policy()` and gates
mcp_enabled on `_cli_policy is not False`. Applied to both GGUF
and safetensors paths.
5. GGUF's agentic loop called `execute_tool(tool_name, ...)` without
checking the model-emitted name against the per-request tool list,
while the safetensors loop already enforces this. Added the same
allow-list check so a model that hallucinates a filtered MCP name
or a built-in the caller opted out of returns "not enabled" instead
of executing.
Bonus P2 fixes:
- `call_tool_sync` now checks `cancel_event.is_set()` BEFORE
creating the call task, so a pre-set cancellation does not open
the HTTP transport.
- `clear_oauth_tokens_async` moved the OAuth import + construction
inside the protected try block; a fastmcp.client.auth load error
used to escape and 500 the delete / update route.
NOT fixed (verified false or out of scope):
- finding #10 "structured_content vs structuredContent": fastmcp's
CallToolResult dataclass uses snake_case (verified live against
structured-only tool result; fields are
`dict_keys(['content', 'structured_content', 'meta', 'data', 'is_error'])`).
- finding #11 "asyncio.run from running loop": call_tool_sync is
invoked from `asyncio.to_thread` worker threads which have no
event loop; asyncio.run() is safe there.
Tests added (37 total): hyphenated param names, tool_healing strip,
GGUF allow-list gate, cancel pre-set short-circuit, OAuth cleanup
constructor-error swallowing. 1721 passed locally, no regressions.
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* Studio: add Gemini provider with web_search, code_execution, prompt caching, and Nano Banana image generation
Wires Google's native Gemini API into Studio's external-provider stack
so users can pick gemini-2.5-pro / gemini-2.5-flash / gemini-2.5-flash-image
(Nano Banana) alongside the existing OpenAI / Anthropic / OpenRouter
providers. Gemini does not speak OpenAI Chat Completions on its primary
endpoint; the new `_stream_gemini` async generator translates between
the two shapes the same way `_stream_anthropic` handles the Messages API.
Backend:
- New `_stream_gemini` translator in external_provider.py. Converts
OpenAI messages -> Gemini `contents` + `systemInstruction`; maps
generationConfig (temperature / topP / topK / maxOutputTokens);
forwards `tools: [{googleSearch: {}}]` for web_search and
`{codeExecution: {}}` for code_execution; passes `cachedContent`
through for prompt caching; sets `responseModalities=[TEXT, IMAGE]`
for Nano Banana image generation.
- Translates streamed `GenerateContentResponse` SSE frames back into
OpenAI chat.completion.chunk frames (text deltas, function_call ->
tool_calls deltas, inlineData -> image_b64 tool_end envelope, usage
chunk before [DONE]).
- Registry entry switched to native base URL
`https://generativelanguage.googleapis.com/v1beta` with
`openai_compatible: False` and the `x-goog-api-key` auth header.
Model lineup curated to current 2.5 / 2.0 family + Nano Banana.
Frontend:
- Provider-capability matrix: Gemini supports temperature, top_p, top_k,
presence_penalty (matches generationConfig); min_p / repetition_penalty
hidden because the API does not accept them.
- `providerSupportsBuiltinWebSearch` / `providerSupportsBuiltinCodeExecution`
/ `providerSupportsBuiltinImageGeneration` extended for Gemini.
- Prompt caching toggle now also lit on Gemini.
Tests:
- 21 new tests in `test_gemini_provider.py` using httpx.MockTransport.
Cover request body shape conversion, URL/header wiring, web_search
forwarded as googleSearch, function-call translation both directions,
prompt caching passthrough, image generation emitting image_b64,
grounded-search citations -> tool_end, finish_reason mapping, and
vision data URL -> inlineData translation.
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* Studio: forward presence_penalty to Gemini and recover function name from tool_call_id
Two follow-up fixes for the Gemini provider:
* Thread presence_penalty into _stream_gemini and set
generationConfig.presencePenalty when non-zero. The OpenAI-side
capability matrix already exposes the slider for Gemini, so the
value was being collected and silently dropped on the way out.
* When an OpenAI role=tool message omits 'name' and only carries
'tool_call_id', recover the function name from the matching
functionCall on the prior assistant turn. Gemini 400s on an empty
functionResponse name.
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* Studio: surface Gemini code execution parts as code_execution tool events
The Gemini stream parser only handled text/functionCall/inlineData
parts, so when the user toggled the Code pill on a Gemini model the
sandbox output (executableCode + codeExecutionResult parts) was
dropped on the floor while adjacent text reached the UI. Reviewers
flagged this as the headline feature being silently broken.
Translate both parts into the existing code_execution tool envelope
that CodeExecutionToolUI already consumes for OpenAI / Anthropic:
* executableCode -> tool_start with kind=code_execution and the
source code under arguments.code. We mint a tool_call_id and
stash it so the matching result block can pair to it.
* codeExecutionResult -> tool_end on that id with the stdout under
result. Non-OK outcomes (OUTCOME_FAILED / OUTCOME_DEADLINE_EXCEEDED)
are prefixed onto the text so the failure is visible.
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* Studio: native Gemini model catalog, function-call ids, and honest cache claim
Three follow-ups to the Gemini provider PR after the codex pass:
* list_models() now translates Gemini's native /v1beta/models
payload ({models[{name, baseModelId, displayName,
supportedGenerationMethods}]}) into the OpenAI-compatible shape
Studio expects. Without this the picker stayed empty for Gemini
and fell back to hardcoded defaults. Embedding-only models are
filtered out.
* Forward the OpenAI tool_call id into Gemini's functionCall.id
and mirror it onto functionResponse.id. Two parallel calls to
the same function name can now be paired unambiguously on the
follow-up turn.
* Drop Gemini from the prompt-caching capability set. The wire
flow requires a separate cachedContents POST first and the
boolean Studio emits today is a no-op; the toggle should not
advertise a feature it cannot apply. Leaves a pointer to the
docs for the eventual two-step orchestration.
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* Studio: distinct tool_calls index per emitted Gemini function call
Codex flagged that the Gemini stream parser hardcoded
tool_calls[0].index to 0 on every emitted functionCall. OpenAI
reassemblers key tool_calls by index when joining deltas, so two
parallel function calls in one assistant turn collapsed onto a
single slot and the second call's arguments overwrote the first.
Track the running count via len(emitted_function_call_ids) - 1
and emit it as the per-call index. The dedupe guard above (skip
when fc_id already in the set) means the index is monotonic and
stable for the lifetime of the stream. Regression test asserts
[0, 1] across two parallel calls in one candidate parts list.
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* Studio: surface Gemini 3.5/3.1/3 + Nano Banana 2/Pro and plumb thinking budget
`gemini-2.0-flash` / `gemini-2.0-flash-exp` were retired by Google in 2026
(`/v1beta/models/gemini-2.0-flash:streamGenerateContent` returns HTTP 404
"no longer available to new users"), and the picker had nothing past the
2.x family. Verified against the live ListModels catalog: drop the retired
ids from `default_models` + allowlist and surface the chat-capable
3.5 / 3.1 / 3 families plus the Nano Banana image trio.
Also plumb `enable_thinking` / `reasoning_effort` into Gemini's
`generationConfig.thinkingConfig`. Without this, Gemini 3.5 Flash,
gemini-pro-latest, and the 3.x previews silently spend the caller's
`max_tokens` budget on hidden "thoughts" before emitting any visible
answer -- the chat shows a truncated stub like "The capital of" and
streams stop. Mapping:
- enable_thinking=False / reasoning_effort=none -> thinkingBudget=0
(Flash tier; Pro tier coerces to a small positive budget because
the API 400s on 0 with "This model only works in thinking mode")
- minimal/low/medium/high -> 512/2048/8192/24576 budget tokens
- max/xhigh -> -1 (dynamic)
- default (neither knob set) -> thinkingConfig omitted, model decides
Frontend `getExternalReasoningCapabilities` now surfaces a
`reasoning_effort` picker for every Gemini chat id (Pro tier hides the
"none" option; image-tier ids stay knob-less). Adds 6 unit tests
covering Flash/Pro effort mapping, the off-toggle coercion on Pro,
default omission, and the nano-banana-pro-preview alias routing
through the image modalities path. 28 -> 34 tests in
`test_gemini_provider.py`, all green; full backend suite still passes
(1459/1460; the unrelated test_help_output flake is pre-existing and
not in any file this PR touches).
Live verification against generativelanguage.googleapis.com on
2026-05-24 with `_stream_gemini` directly:
text gemini-3.5-flash single PASS multi PASS
text gemini-3.1-pro-preview single PASS multi PASS
text gemini-3.1-flash-lite single PASS multi PASS
text gemini-3-pro-preview single PASS multi PASS
text gemini-3-flash-preview single PASS multi PASS
text gemini-2.5-pro single PASS multi PASS
text gemini-2.5-flash single PASS multi PASS
text gemini-2.5-flash-lite single PASS multi PASS
text gemini-flash-latest single PASS multi PASS
text gemini-flash-lite-latest single PASS multi PASS
text gemini-pro-latest single PASS multi PASS
image gemini-2.5-flash-image PASS (1082 KB png returned)
image gemini-3.1-flash-image-preview PASS (Nano Banana 2)
image gemini-3-pro-image-preview PASS (Nano Banana Pro)
tool web_search PASS
tool code_execution PASS
-> 16/16 e2e through the actual ExternalProviderClient code path.
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* Studio: tighten Gemini provider after review (PR #5720)
Fixes a batch of bugs surfaced by a second-pass review on top of the
3.5/3.1/3 + Nano Banana 2/Pro additions in c6724dbd.
Backend (external_provider.py):
- Constructor normalises legacy /v1beta/openai base URLs to /v1beta so
Gemini providers saved before the native switch keep working without
a manual re-config.
- Skip thinkingConfig, googleSearch, and codeExecution on image-tier
models (-image / nano-banana). The image responseModalities path is
mutually exclusive with text-tool wiring and stale UI state would
otherwise 400 the turn.
- _PRO_THINKING_PREFIXES now includes gemini-3.5-pro and uses anchored
prefix matching (exact id or "<prefix>-...") so the image-tier
gemini-3-pro-image-preview cannot accidentally match the pro guard.
- Gemini 3 functionCall thoughtSignature is round-tripped through the
tool_calls envelope via extra_content.google.thought_signature on
emit, and replayed as a sibling of functionCall on the next request.
- finishReason swaps STOP -> tool_calls when any functionCall was
emitted on the same turn so OAI clients trigger tool execution
(matches the OpenAI Chat Completions contract).
- usageMetadata.thoughtsTokenCount is rolled into output_tokens and
surfaced on output_tokens_details.reasoning_tokens so total_tokens
reflects the full billable spend instead of dropping the hidden
reasoning slice.
Registry (providers.py):
- Drop gemini-3-pro-preview from default_models. Google shut it down
on 2026-03-09 and auto-redirects to gemini-3.1-pro-preview; we
surface the canonical id only.
- Add model_id_deny_exact = ("gemini-3-pro-preview",) so the live
ListModels fetch does not re-surface the redirect alias.
Route schema (models/inference.py):
- enable_prompt_caching widened to Optional[Union[bool, str]] so the
/v1/chat/completions caller can pass a Gemini cachedContent resource
name (e.g. cachedContents/abc123). Without this widening _stream_gemini
s string cachedContent passthrough was unreachable from the public
route (bool_parsing 422). stream_chat_completion signature mirrors.
Frontend (provider-capabilities.ts, chat-page.tsx, chat-adapter.ts):
- providerSupportsBuiltinImageGeneration now also recognises
nano-banana ids (nano-banana-pro-preview was hidden from the image
pill before).
- providerSupportsBuiltinWebSearch takes the model id so Gemini image
models hide the Search pill (mirrors the backend skip).
- providerSupportsBuiltinCodeExecution uses the same isGeminiImageModel
guard for nano-banana ids.
- GEMINI_THINKING_PRO_PREFIXES gains gemini-3.5-pro; gemini-3-pro
tightened to gemini-3-pro-preview to avoid the image-id overlap.
- Updated 3 callers of providerSupportsBuiltinWebSearch to thread the
selected model id through.
Tests (test_gemini_provider.py): 34 -> 42, all green
- test_image_models_skip_thinking_config
- test_image_models_drop_text_only_tools
- test_gemini_35_pro_recognized_as_pro_thinking
- test_legacy_openai_base_url_normalized
- test_finish_reason_swaps_to_tool_calls_when_function_call_emitted
- test_thought_signature_round_trips_into_gemini_function_call
- test_thought_signature_emitted_in_tool_call_delta
- test_usage_chunk_includes_thoughts_tokens
Verification:
- Backend pytest 1518/1519 passing (one unrelated Qwen3.5 flash-attn
test fails on main as well; nothing in this PR touches that path).
- Frontend npx tsc -b clean.
- Live e2e 16/16 against generativelanguage.googleapis.com through the
patched _stream_gemini code path (all 11 chat models single + multi
turn, all 3 image models returned image bytes, web_search and
code_execution tools both emit the expected envelope).
- Live /api/providers/models against the patched backend surfaces 16
ids (gemini-3-pro-preview correctly filtered via deny_exact).
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* Studio: address second-pass review findings on Gemini (PR #5720)
Round-2 reviewer.py flagged a phantom web_search card on image
turns (12/12 reviewers), route-layer stripping of tool_calls /
tool_call_id / name, an over-narrow image-mode tool guard, and
silent safety blocks. This patch fixes all four.
Backend (external_provider.py):
- web_search_active is now derived from the outbound tools_array
(whether googleSearch was actually forwarded), not the raw
enabled_tools intent. Image-mode turns dropped the tool above so
the inbound stream no longer emits a phantom "search complete"
tool_start / tool_end on those turns.
- text_tools_allowed now uses is_image_model (covers both `-image`
/ `nano-banana` picker models AND text models that requested
`image_generation` via enabled_tools). Verified against the live
Gemini API which rejects both googleSearch and codeExecution
alongside responseModalities=["TEXT","IMAGE"] with explicit 400s
("Search as tool is not enabled for this model", "Code execution
is not enabled for this model").
- promptFeedback.blockReason is surfaced as a 400 content-filter
error chunk instead of returning an empty successful assistant
response. The streaming loop closes the response before exiting.
Route (routes/inference.py):
- _build_external_messages now propagates tool_calls (assistant),
tool_call_id, and name (tool result) through every code path
(string content, multimodal content, non-vision fallback). Without
this Gemini 3 function-call round trips lost their thoughtSignature
+ tool_call_id at the route boundary, and functionResponse.name
arrived empty on the second turn.
- Assistant messages with content=None and tool_calls populated are
preserved as a synthetic empty-string content turn so the
Gemini translator can rebuild the functionCall part.
Tests (test_gemini_provider.py): 42 -> 45, all green
- test_image_models_suppress_phantom_web_search_card
- test_image_generation_tool_drops_text_tools
- test_prompt_feedback_block_reason_surfaces_as_error
Verification:
- Backend pytest 1736 / 1736 (the two pre-existing unrelated fails
on main, test_help_output and Qwen3.5 flash-attn pin, are skipped).
- Frontend npx tsc -b clean.
- Live e2e 16/16 against generativelanguage.googleapis.com:
11 chat models single + multi turn, 3 image models returning
image bytes, web_search and code_execution both PASS.
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* Studio: fix third-pass Gemini findings (PR #5720)
Round 3 review follow-ups:
Backend (studio/backend/core/inference/external_provider.py):
- Close response AND aiter_lines iterator in a finally so normal,
prompt-block, and cancellation exits all clean up (eliminates the
RuntimeWarning about aclose never being awaited).
- Pair the synthetic web_search tool_start with a tool_end on the
promptFeedback.blockReason path so the UI does not leave a stuck
"searching..." spinner after the error toast.
- Preserve native id and thoughtSignature on executableCode and
codeExecutionResult tool events under google.native_part, and pair
the tool_end on the code-exec id so multi-turn code-execution
replays do not lose Gemini-required history.
- Carry part-level thoughtSignature on text deltas via
delta.extra_content.google.thought_signature and on inline image
tool_end via google.thought_signature so Gemini 3 image editing
and tool turns round-trip the signature on the next request.
- Guess remote image_url MIME from the URL path so PNG / WebP / GIF
inputs are not silently relabeled as JPEG.
- Roll usageMetadata.toolUsePromptTokenCount into translated input
tokens and surface thoughtsTokenCount as
completion_tokens_details.reasoning_tokens in _build_usage_chunk.
- Only normalize the Google-hosted /v1beta/openai legacy base URL;
custom proxies whose paths happen to end in /openai are left
untouched.
- Forward ChatCompletionRequest.tools and tool_choice through
stream_chat_completion into _stream_gemini, translating to
tools[].functionDeclarations and toolConfig.functionCallingConfig.
Frontend:
- chat-adapter: when Gemini image-generation is enabled for the turn,
also disable Search and Code so the request, builder, and active
pills agree with what the backend actually sends (the backend
already strips text tools when image_generation is in enabled_tools).
- chat-adapter: consume OpenAI-shape delta.tool_calls chunks so
Gemini function-call deltas without text surface as tool-call parts.
- shared-composer: disable Search and Code pills while Gemini image
mode is active so the UI matches the request.
Tests (studio/backend/tests/test_gemini_provider.py): adds coverage
for proxy base-url gating, remote image MIME inference,
toolUsePromptTokenCount, reasoning_tokens propagation, prompt-block
web_search tool_end pairing, native code-exec id/thoughtSignature
metadata, inline image thoughtSignature, text-chunk extra_content,
OpenAI tools/tool_choice translation, and image-model tool drop.
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* Studio: Gemini 3 thinkingLevel + image-model Search grounding (PR #5720)
Gemini 3.x migrated to a string `thinkingConfig.thinkingLevel`
(MINIMAL/LOW/MEDIUM/HIGH) and rejects `thinkingBudget`+`thinkingLevel`
in the same request. Gemini 3 also cannot turn thinking fully off, so
the lowest position is "minimal" (Flash) or "low" (Pro rejects
"minimal").
- external_provider._stream_gemini: split thinking translation by
family. Gemini 3.x (3 / 3.1 / 3.5 + gemini-pro-latest /
gemini-flash-latest / gemini-flash-lite-latest) emits
thinkingConfig.thinkingLevel; effort none/off coerces to "low" on
Pro and "minimal" on Flash. Gemini 2.5 stays on thinkingBudget.
- external_provider._stream_gemini: allow `tools: [{googleSearch: {}}]`
on the Gemini 3 image family (gemini-3-pro-image-preview,
gemini-3.1-flash-image-preview, nano-banana-pro). Google's docs
document Search grounding on these. codeExecution stays blocked
on image mode (still mutually exclusive with responseModalities).
- provider-capabilities.ts: mirror the Gemini 3 effort ladders in
resolveGeminiReasoningCapabilities (Pro: low/medium/high; Flash:
minimal/low/medium/high; 2.5 Flash keeps the off-position).
- provider-capabilities.ts: providerSupportsBuiltinWebSearch now
returns true on the documented Gemini 3 image models so the pill
is reachable; older image ids (gemini-2.5-flash-image) still hide.
Tests: splits the existing thinkingBudget cases by family (Gemini 3
checks thinkingLevel; Gemini 2.5 keeps thinkingBudget), adds positive
googleSearch coverage for Gemini 3 image models and negative
googleSearch coverage for legacy image models.
References:
- https://ai.google.dev/gemini-api/docs/thinking
- https://ai.google.dev/gemini-api/docs/gemini-3
- https://ai.google.dev/gemini-api/docs/models/gemini-3-pro-image-preview
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* Studio: attach Gemini code_execution inline images to the code card (PR #5720)
When a text Gemini turn wires codeExecution and the sandbox produces a
matplotlib plot, the inline image part ships right after the
codeExecutionResult. Previously this surfaced as a separate empty
image_generation card. Track the most recent code_execution
tool_call_id + result text and, when an inline image follows with
code_execution active, emit a second tool_end on the same id that
appends the image as a data: URI under the `__IMAGES__:` marker the
chat-adapter already understands.
Image-picker turns (`-image` / `nano-banana`) keep the standalone
image_generation envelope so Nano Banana outputs render the same way.
Tests: covers the merged code-execution card emission with no
standalone image_generation event when code_execution is the active
tool.
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* Studio: fix fourth-pass Gemini findings (PR #5720)
Round 4 review follow-ups:
Backend:
- `_is_openai_compatible` + `_auth_headers` detect Gemini connections
pointed at a custom OpenAI-compatible proxy (non-Google host whose
path ends in `/openai`) and route them through the OpenAI-compat
surface with `Authorization: Bearer ...` instead of the native
`_stream_gemini` translator + `x-goog-api-key`. Google-hosted Gemini
keeps the native dispatch path it migrated to in this PR.
- `_stream_gemini` thinkingLevel handling for Gemini 3 Pro now coerces
both "minimal" and "medium" effort to "low" / "high" respectively
(Pro tier only accepts low/high per
https://ai.google.dev/gemini-api/docs/thinking).
- `providers.py` `default_models` restores the advertised
`gemini-3.5-pro` and the rolling `gemini-pro-latest` /
`gemini-flash-latest` / `gemini-flash-lite-latest` aliases that the
allowlist already admits.
Frontend:
- chat-adapter: lean on `providerSupportsBuiltinWebSearch` (which
already encodes the Gemini 3 image-model Search allowance) instead
of blanket-disabling Search whenever Gemini image mode is active.
Code execution stays blocked because Gemini image mode rejects it.
- shared-composer: mirror the same gate -- only the Code pill is
unconditionally disabled in Gemini image mode; the Search pill is
driven by `supportsBuiltinWebSearch`.
- provider-capabilities: Gemini 3 Pro reasoning levels now expose only
"low" and "high" (no Medium pill) to match the API.
Tests: covers the Gemini 3 Pro medium / minimal coercion, the custom
proxy OAI-compat dispatch + Authorization Bearer auth, and the
native-vs-proxy detection. Also closes the mocked httpx.AsyncClient
inside the test event loop so the Python 3.13 `aclose was never
awaited` warning no longer fires.
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* Studio: fix fifth-pass Gemini findings (PR #5720)
Round 5 review follow-ups:
Backend:
- `_is_openai_compatible` + `_auth_headers` now treat ANY non-Google
Gemini base URL as OpenAI-compat (LiteLLM / custom OAI gateways /
OpenAI-compat vLLM routers), not just paths ending in `/openai`.
Pre-existing saved Gemini proxies on `/v1` keep working.
- Gemini 3 thinkingLevel coercion narrowed to the documented
inconsistencies: only "minimal" is coerced to "low" on Pro tier.
"medium" passes through (Gemini 3.1 Pro accepts it per
https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-1-pro).
- `_stream_gemini` only flips `responseModalities=[TEXT,IMAGE]` when
the selected model is image-capable. A stale
`enabled_tools=["image_generation"]` on a text model is silently
dropped instead of producing an invalid Gemini request.
- `_stream_gemini` validates the model id against
`[A-Za-z0-9._-]+` before URL interpolation so a model like
`../cachedContents/x` cannot redirect the request to an unintended
endpoint with the configured API key attached.
- Empty-text Gemini parts that still carry `thoughtSignature` emit a
content-free delta with `extra_content.google.thought_signature` so
Gemini 3 turns that end with a signature-only fragment do not lose
the replay state.
- ConnectError / ReadTimeout / generic HTTPError paths in
`_stream_gemini` now close the synthetic web_search tool_start
with a matching tool_end before the error chunk so the UI does not
leave a stuck "searching..." card on transport failure.
- `providers.py` default_models drop the non-existent
`gemini-3.5-pro` (Google launched only `gemini-3.5-flash` at
I/O 2026; Pro tier remains `gemini-3.1-pro-preview`).
- `routes/inference.py` only forwards `payload.top_k` when the caller
explicitly set it on the request (Pydantic `model_fields_set`).
Omitted top_k stays omitted, restoring the pre-PR behavior where
Gemini uses its server default.
- `ChatCompletionRequest.enable_prompt_caching` adds a `mode="before"`
validator that coerces the canonical string literals "true"/"false"
back to bool so historical opt-out callers keep working after the
field widened to `Union[bool, str]` for Gemini cache resource names.
Frontend:
- `providerSupportsBuiltinWebSearch` / Code / Image now accept the
saved connection `baseUrl` and return false for custom OAI-compat
Gemini proxies. Backend skips `_stream_gemini` for those bases, so
native tool envelopes never reach them; hiding the pills keeps the
request, builder, and UI consistent.
- `provider-capabilities.ts` Gemini 3 Pro effort ladder restores
`["low", "medium", "high"]` to match Google's documented levels.
- Call sites in `chat-page.tsx` and `chat-adapter.ts` pass through
`provider.baseUrl` so the proxy gate fires.
Tests: covers Gemini 3 Pro medium pass-through, custom proxy dispatch
on `/v1` and `/openai` bases, path-traversal model id rejection,
top_k omission when not explicit, text-model image_generation drop,
empty-text + thoughtSignature surfacing, and
enable_prompt_caching string coercion.
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* Studio: fix sixth-pass Gemini findings (PR #5720)
Round 6 review follow-ups:
Frontend:
- chat-adapter `delta.tool_calls` accumulates fragments by `id` /
`index` instead of pushing a new tool-call card per chunk. The
standard OpenAI Chat Completions stream contract sends `id`/`name`
on the first chunk and partial `function.arguments` on subsequent
chunks; our previous handler parsed each fragment as a standalone
tool call. Local llama.cpp and OAI-compat providers that stream
fragments now reassemble into a single function-call part.
- chat-adapter also preserves `extra_content` on streamed tool-call
deltas so Gemini 3 `thoughtSignature` survives to the next turn.
- provider-capabilities Gemini 3 Pro restores "medium" in the
reasoning-effort ladder (Google's official Gemini API thinking
doc lists low/medium/high for Gemini 3.1 Pro; my earlier round 4
coercion was wrong).
- provider-capabilities orders `gemini-2.5-flash-lite` ahead of the
broader `gemini-2.5-flash` prefix so Flash-Lite falls into the
"no native thinking knob" branch as documented.
* Studio: round-trip Gemini tool_calls and tool results (PR #5720)
Recurring round 3-6 P1: the chat-adapter renders Gemini function-call
parts and code-execution events but `toOpenAIMessage` only serialized
text + image content, so the next turn lost the assistant
`tool_calls[]` (including Gemini 3's required
`extra_content.google.thought_signature`) and the matching
`role="tool"` result. Gemini 3 multi-turn function calling and code
execution failed validation on the second turn.
Frontend:
- types/api.ts widens OpenAIChatMessage to permit `role="tool"`,
`tool_calls`, `tool_call_id`, `name`, and `content: null`. Adds
OpenAIToolCallPart with `extra_content` for the Gemini round-trip.
- chat-adapter: new `toOpenAIMessages` expands an assistant turn with
tool-call parts into [assistant w/ tool_calls + extra_content,
role=tool result, ...]. tool result content is JSON-serialized so
the backend translator can rebuild Gemini's `functionResponse`
shape.
- chat-adapter outbound history now uses `flatMap(toOpenAIMessages)`
so each assistant tool-call round-trips through the standard OAI
shape the backend's `_stream_gemini` already understands.
* Studio: replay Gemini code_execution and image native parts on history (PR #5720)
Multi-turn Gemini history previously lost the native executableCode,
codeExecutionResult, and inlineData parts because the outbound
translator regenerated a generic functionCall for every assistant
tool_call. Stow the native dict on tool_end (frontend) and replay it
verbatim with thoughtSignature (backend) so follow-up turns preserve
the prior execution and image generation state. Skip role="tool"
fan-out for server-side builtin tools so Gemini does not 400 on a
functionResponse with no matching user-declared function.
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* Studio: complete Gemini built-in tool replay round-trip (PR #5720)
Round 7 follow-up to the multi-turn native-part work. Three asymmetric
storage/consume gaps remained between the backend translator and the
chat adapter, so realistic Gemini follow-up turns degraded to generic
functionCalls instead of native history.
- Frontend collectAssistantToolCalls now drops web_search outright,
drops code_execution / image_generation when the native part is
missing, and promotes args.google to extra_content.google so the
backend native_part replay branch actually fires.
- Backend image_generation tool_end now emits google.native_part
with the inlineData (mimeType + base64) and thoughtSignature so the
follow-up image-edit turn can replay the prior image as a native
Gemini model part.
- Backend code-execution plot tool_end now stows google.native_part
with the inlineData so the merged code-exec card can round-trip
executableCode + codeExecutionResult + inlineData on the same id.
- Added regression tests for image-gen native-part replay and the
code-exec plot native_part stow.
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* Studio: round 8 Gemini follow-ups (PR #5720)
- Text-part thoughtSignature: stow on the assistant message during
streaming and replay onto the last text part on the next turn so
Gemini 3 strict function-calling does not reject history.
- Function declarations: recursively strip Gemini-unsupported OpenAPI
keys (additionalProperties, $schema, $defs, strict, etc.) so OpenAI
strict tools stop 400ing as INVALID_ARGUMENT on Gemini.
- OpenAI-compat fallback: forward tools/tool_choice so custom Gemini
proxies (LiteLLM, gateways) keep function-calling.
- enable_prompt_caching: cover the Pydantic v1 legacy off/on/f/n/t/y
string set so explicit opt-outs stay opt-out (Gemini was sending
cachedContent: "off" otherwise).
- Frontend collectAssistantToolCalls / collectToolResultMessages: use
google.native_part + result presence to disambiguate provider
builtins from same-named user-declared functions.
- Added regression tests for text-signature replay and schema
sanitization.
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* Studio: round 9 Gemini follow-ups (PR #5720)
Two round-9 convergent finds across the 12 reviewers:
- Server-side web_search was leaking onto the next turn as a fake
user functionCall/functionResponse. The previous heuristic (skip
builtin only when no native_part AND no result) let it through
because the synthetic tool card has a non-empty result string.
Always skip web_search by name on both serializers, accept that a
user-declared function literally named "web_search" must use a
different name.
- Assistant `extra_content` was dropped by ChatMessage validation
before _stream_gemini could replay text-part thought signatures.
Add the field to ChatMessage and forward it through
_build_external_messages so the multi-turn signature path actually
carries data.
Includes a regression test for the ChatMessage round-trip.
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* Studio: round 10 Gemini follow-ups (PR #5720)
Three convergent round-10 reviewer findings closed:
- Tag synthetic provider-side builtins with `args._server_tool=True`
via a central helper that runs in every `_emit_tool_event` /
`_emit_synthetic_tool_event` path. The frontend filter now skips
on that marker instead of on the public tool name, so local
llama.cpp `web_search` and OpenAI function tools literally named
`web_search` / `code_execution` / `image_generation` round-trip
cleanly while Gemini grounding / hosted code-exec / hosted image
cards stay skipped.
- Gate Gemini image-mode (responseModalities=[TEXT,IMAGE]) on the
Images pill (enabled_tools containing `image_generation`).
Selecting an image-capable model with the pill off no longer forces
image output the UI says is disabled.
- Frontend missing-key guard now exempts custom Gemini OAI-compat
proxies (LiteLLM, gateways) the same way the backend already
does, so a saved Gemini connection on `http://localhost:4000/v1`
with no API key stops being blocked.
Existing tests updated to pass `enabled_tools=["image_generation"]`
on image-mode capture paths.
* Studio: round 11 Gemini follow-ups (PR #5720)
Four round-11 findings closed:
- Kimi _stream_kimi_web_search's local _synthetic_chunk helper now
runs through _stamp_server_tool_marker so Kimi search history is
not replayed as a fake user functionCall on the next turn (was an
asymmetric miss after the round-10 tagging work).
- OpenAI Responses path (/v1/responses for gpt-5.x) forwards
caller-supplied tools / tool_choice, translating the Chat
Completions function-tool shape into the Responses native shape.
Without this, standard OpenAI tools silently dropped on
Responses-routed traffic.
- Decoupled the Gemini image-tier model-id guards (text-tool /
thinking strip) from the Images pill flip
(responseModalities=[TEXT,IMAGE]). gemini-2.5-flash-image with
Search/Code on and the Images pill OFF no longer forwards
googleSearch + thinkingConfig (Gemini 400s on those for legacy
image ids).
- Gemini-only extra_content is now forwarded by
_build_external_messages only when provider_type=="gemini" so
Google's thought_signature does not leak into OpenAI / Mistral /
Kimi / OpenRouter request bodies as an unknown field.
Added a regression test for the image-tier strict-guard split and
extended the extra_content test to cover the non-Gemini suppression.
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* Studio: round 12 Gemini follow-ups (PR #5720)
Three round-12 convergent findings closed:
- extra_content leak to custom Gemini OAI-compat proxies (8/12
reviewers). _build_external_messages now gates extra_content on
the native generativelanguage.googleapis.com host, not just
provider_type=="gemini", so LiteLLM / custom gateways routed
through /chat/completions do not get an unknown top-level field.
- OpenAI Responses function-tool round-trip (5/12 reviewers). I
added user `tools` forwarding in round 11 but did not parse the
matching response.output_item.done items of type=function_call.
The parser now translates them into Chat Completions
delta.tool_calls and the terminal chunk reports
finish_reason="tool_calls" when the model invoked a user
function.
- Image-tier model with Images pill OFF (2/12). Google's image
models default to text+image when responseModalities is omitted,
so the previous fix silently still billed image output. Force
responseModalities=["TEXT"] when the Images pill is off and the
selected model is image-capable.
Updated the two pre-existing tests that pinned the synthetic-tool
arguments shape to include the new `_server_tool: True` marker, and
added a regression test for the Responses function-call output
translation.
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* Studio: round 13 Gemini/Responses follow-ups (PR #5720)
Three round-13 convergent findings closed:
- OpenAI Responses function_call indices: my round-12 translator
hardcoded every emitted tool_calls[*].index to 0, so parallel
function calls collapsed for index-keyed clients. Track and
increment function_call_index per emit (mirrors the Gemini
branch's distinct-index pattern). 10/12 reviewers flagged.
- _SERVER_SIDE_BUILTIN_TOOL_NAMES now includes web_fetch so
Anthropic-hosted web_fetch cards carry the _server_tool marker
and the frontend history serializer doesn't replay them as fake
user functions. 4 reviewers flagged.
- OpenAI Responses follow-up tool results now serialize as
Responses-shape function_call / function_call_output items keyed
by call_id, instead of Chat Completions role="tool" content.
Skips assistant tool_calls tagged with _server_tool so hosted
builtins don't round-trip as user functions. 2 reviewers flagged.
Updated the Anthropic code_execution and web_fetch test argument
pins to include the new _server_tool marker, and added two
regression tests (distinct indices on parallel function_call,
function_call_output round-trip).
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* Studio: round 14 Gemini follow-ups (PR #5720)
Three round-14 findings closed:
- Remote `image_url` translation (5 reviewers convergent). Public
HTTPS image URLs can't be sent as `fileData.fileUri` -- Gemini
reserves that path for Files API URIs and YouTube. Fetch the
bytes server-side and inline them as base64 `inlineData`,
mirroring the pre-PR OpenAI-compat behaviour. YouTube URLs and
generativelanguage.googleapis.com/v1beta/files/* stay as
`fileData`.
- Nullable JSON Schema type arrays. OpenAI strict tools commonly
use `"type": ["string", "null"]`; the Gemini sanitizer now
flattens that to `"type": "string", "nullable": true` so strict
function tools stop 400ing.
- Parallel functionResponses now ride on one user content block
with multiple `functionResponse` parts, matching Google's
parallel tool docs. Consecutive `role="tool"` messages merge
into the previous user turn instead of splitting into separate
Gemini user turns.
Three regression tests added (remote URL fetch + inline, Files
API / YouTube fileData preservation, schema nullable flattening,
parallel-tool grouping).
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* Studio: SSRF harden Gemini remote image fetch (PR #5720)
Round 15 convergent finding (12/12 reviewers). My round-14 fix to
download user-controlled image URLs for inlineData inlining was an
SSRF / data-exfiltration path: no scheme check, no private-host
guard, no size cap, no Content-Type validation, redirects could
bounce to internal services, and the full URL was logged.
Replace the inline fetch with `_safe_fetch_image_for_gemini`:
- Require https:// (reject http, file, data, ftp, etc).
- Resolve the hostname via socket.getaddrinfo and reject if ANY
resolved address is private / loopback / link-local / multicast /
reserved / unspecified (covers 127.0.0.0/8, 10/8, 172.16/12,
192.168/16, ::1, 169.254/16 metadata, RFC 6890).
- Block IP-literal URLs that resolve into those same ranges.
- Cap response body at 10 MB (Content-Length pre-check + streamed
byte counter).
- Require Content-Type to start with `image/`.
- Disable redirect following so a 302 to a private host can't slip
past the address check.
- Use a short 15s timeout and a tiny connection pool dedicated to
these fetches.
- Log only the host name + error class -- no full URL, no signed
querystring leak.
If the guard rejects, the image part is silently dropped (instead
of forwarding raw bytes or a fileData fallback). Files API URIs
and YouTube URLs still ride as `fileData.fileUri` unchanged.
Tests: replaced the live-fetch test with a `_safe_fetch_image_for_gemini`
monkeypatch, added four new SSRF-guard tests (non-https rejected,
loopback / private IP literals rejected, hostnames that resolve to
private IPs rejected).
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* Studio: round 16 Gemini follow-ups (PR #5720)
- IP-pinned image fetch (`_safe_fetch_image_for_gemini`): reuse the
validated-once-then-pin pattern from `tools._fetch_page_text` via
`asyncio.to_thread`, so DNS rebinding between validation and the
HTTP connect cannot redirect us at a private/metadata address.
Catch malformed-bracketed IPv6 urlparse errors. Follow up to 4
redirect hops with per-hop SSRF re-validation.
- Replace contains-substring detection of Gemini Files API + YouTube
URLs with parsed scheme/host/path checks, so attacker URLs like
`https://evil.example/path/youtube.com/x.png` no longer skip the
safe-fetch path and serialize as `fileData.fileUri`.
- `_build_external_messages`: strip per-tool-call `extra_content`
for non-native-Gemini providers; the Gemini-only
`thought_signature` payload was leaking through `tool_calls[]`
into /chat/completions on OpenAI, Anthropic, and custom Gemini
OAI-compat gateways.
- `_server_tool` marker now gated on the function name being one of
the canonical builtin names (`web_search`, `web_fetch`,
`code_execution`, `image_generation`) AND the marker being set,
so a user function whose schema happens to define an
`_server_tool` field is no longer dropped. Frontend filter mirrors
the same gate, plus a backward-compat fallback for pre-PR
persisted server-tool cards (no marker) routed via name +
native_part / web-tool heuristic.
- Gemini schema sanitizer collapses `anyOf: [{X}, {"type":"null"}]`
to `{X, "nullable": true}` so Optional[X] tool args from
OpenAI/Pydantic schemas no longer 400 the Gemini request.
- Frontend tool-result serializer emits `{"result":""}` for empty
string outputs so the ChatMessage validator does not reject
`role="tool"` with empty content.
- Coerce `medium` thinkingLevel to `high` for legacy
`gemini-3-pro*` / `gemini-3-pro-preview*` (only low/high
documented; shut down 2026-03-09); 3.1+ Pro still passes through.
- Hide Gemini native thinking ladder on custom OAI-compat Gemini
gateways by routing `getExternalReasoningCapabilities` through
`isGeminiCustomOpenAICompatBase(baseUrl)`; thread baseUrl through
all four call sites.
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* Studio: round 17 Gemini follow-ups (PR #5720)
- Frontend `collectAssistantToolCalls` and `collectToolResultMessages`
no longer drop unmarked `web_search` / `web_fetch` cards by name
alone: a user-defined function with one of those names must
round-trip. Pre-PR persisted `code_execution` / `image_generation`
cards still get filtered via a shape heuristic (kind/command/code/
prompt fields) instead of bare name.
- `_build_external_messages._filter_tool_calls` now drops marked
server-side builtin `tool_calls` entirely for non-native-Gemini
providers, not just their `extra_content`. An assistant turn whose
only payload was a marked builtin is dropped completely so the
receiving provider does not see an orphan tool_call.
- `_stream_anthropic` translates OpenAI top-level `tool_calls` into
Anthropic native `{type:"tool_use", id, name, input}` content
blocks, and translates `role="tool"` follow-ups into `role:"user"`
messages carrying a `tool_result` block. Anthropic's native
Messages API rejects the OpenAI shapes.
- `_safe_fetch_image_for_gemini_sync` factors URL validation through
`_safe_parse_https`, so malformed `port` access (e.g.
`https://host:bad/x.png`) and malformed redirect targets (e.g. a
302 to `https://[bad/x.png`) drop the image instead of raising mid-
request.
- `tool_choice="none"` now disables hosted builtins (Gemini
googleSearch / codeExecution and OpenAI Responses web_search /
shell / image_generation), not just user function declarations.
- Schema sanitizer handles multi-type `anyOf` with null
(`Union[str, int, None]`): keep the slim non-null anyOf and add
`nullable: true` so Gemini does not reject `{"type":"null"}`.
- Image fetch falls back to the caller-provided MIME (guessed from
URL extension) when the server omits Content-Type instead of
dropping the image as `non-image content-type=<none>`.
- Per-request aggregate caps on remote image inlining (8 images,
20MB total) so a single chat request cannot force unbounded
backend downloads.
- Frontend exposes the reasoning ladder for `gemini-2.5-flash-lite`
(`none/minimal/low/medium/high/max`) so the UI can drive the
thinkingBudget the backend already supports.
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* Studio: round 18 Gemini follow-ups (PR #5720)
- `tool_choice="none"` now opts out of hosted builtin tools on every
provider path, not just Gemini and OpenAI Responses. Anthropic
web_search / web_fetch / code_execution, Kimi `$web_search` early
return, and OpenRouter `plugins:[{id:"web"}]` are all gated on
`tool_choice_disabled`. Passing `enabled_tools=[...]` with
`tool_choice="none"` no longer triggers provider-side search /
code execution for any provider.
- `_stream_anthropic` accepts `tool_choice` and threads it through;
the dispatcher in `stream_chat_completion` forwards it.
- Frontend `isServerSideBuiltinToolPart` simplified to drop only on
(marker) OR (canonical name + native_part). The previous shape
heuristic on `args.kind`/`args.command`/`args.code`/`args.prompt`
dropped real user-declared `code_execution` / `image_generation`
functions. Pre-PR persisted hosted cards lacking the marker now
leak to non-native providers on switch -- preferred to silently
deleting legitimate function-call history.
- Backend `_is_marked_server_builtin_tool_call` and the OpenAI
Responses translator's matching filter accept BOTH `_server_tool`
marker AND `args.google.native_part` as durable provider-side
signals so Gemini code_execution / image_generation cards are
still dropped on a provider switch.
- Per-request remote image count cap now counts ATTEMPTS, not just
successful inlines, so 100 failing/slow URLs cannot each consume
the 15s fetch timeout. Data: URL images now share the same count
and byte caps as fetched remote URLs.
- OpenAI Responses translator tracks skipped server-builtin
`function_call` ids and drops their matching `role="tool"`
follow-ups, preventing orphan `function_call_output` items in the
outbound body.
- Gemini schema sanitizer preserves multi-type unions with null:
`{"type":["string","integer","null"]}` becomes
`anyOf:[{string},{integer}] + nullable:true` instead of being
flattened to the first non-null type.
- Gemini model id validation moved to the top of `_stream_gemini`
so an invalid model id rejects the request before any remote
image fetch / message translation side effect.
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* Studio: round 19 Gemini follow-ups (PR #5720)
- `_build_external_messages` now skips an empty assistant turn when
`_filter_tool_calls` drops every synthetic builtin tool_call (was
guarded only on the `content is None` branch; the string-content
and list-content branches still forwarded
`{"role":"assistant","content":""}` which several providers
reject). Also tracks the dropped server-builtin tool_call ids and
skips the matching `role="tool"` follow-ups so the receiving
provider does not see an orphan tool_result.
- OpenRouter `web_search_active` (the synthetic tool_start /
tool_end emitter) is now also gated on `tool_choice_disabled` so
a request with `tool_choice="none"` does not surface a fake
web_search card in the chat UI even though the plugin was
correctly stripped from the outbound body.
- `_stream_anthropic` translates an OpenAI role="tool" with list
content (`content=[{"type":"text","text":"..."}]`) into a native
`tool_result` block on a user message; previously only the
string-content shape was translated, so list-content tool results
were forwarded as invalid `role:"tool"` messages.
- Gemini `data:` URL image_url parts now require an `image/*` MIME
type; a `data:text/html;base64,...` is dropped instead of being
forwarded as `inlineData.mimeType="text/html"` (Gemini rejects
the malformed image part). Symmetric with the fetched-remote
image fetch path that already rejects non-image Content-Type.
- YouTube `fileData.fileUri` now declares `video/mp4` as the
mimeType instead of `image/jpeg` guessed from the URL path. The
YouTube/fileData input is the documented Gemini video path; the
guessed image MIME made valid YouTube inputs malformed.
- OpenAI Responses translator preserves `response.output` ordering
on assistant turns that emitted both text and a function_call:
assistant text is now serialized BEFORE the function_call item
so the subsequent function_call_output (the matching role=tool
follow-up) lands in the right position. Previously the order
was function_call -> assistant text -> function_call_output,
which can confuse multi-turn function-calling flows.
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* Studio: round 20 Gemini follow-ups (PR #5720)
Convergent reviewer findings from round 20:
- tool_choice="none" no longer flips responseModalities=[TEXT,IMAGE]
on image-tier Gemini models. Forced-function tool_choice (e.g.
{type:function, function:{name:lookup}}) also drops hosted Search /
code execution from the Gemini body so the caller's pinned user
function is not silently joined by hosted builtins.
- Gemini code-execution thoughtSignature replay now uses an ordered
parts list (native_part.parts[]) so per-part signatures stay
attached to the exact part Gemini emitted. The previous merged
shape fanned one top-level thoughtSignature across executableCode
+ codeExecutionResult + inlineData and tripped Gemini 3 strict
validators. Backward-compat fallback keeps pre-round-21 persisted
history working: a legacy native_part with a single subpart still
replays the signature on that subpart; merged legacy objects pin
the signature to executableCode only.
- Remote-image fetch threads the remaining per-request byte budget
into _safe_fetch_image_for_gemini, so over-budget URLs are
refused via Content-Length pre-check / short read instead of
fully downloaded then discarded after the aggregate cap check.
- Gemini role=tool with OpenAI list-form content
([{type:text,text:result}]) now flattens text parts before
building functionResponse.response.result; previously the parts
arrived as the result value instead of the actual tool output.
- Frontend chat-adapter merges native_part by concatenating parts
lists (preserving per-part thoughtSignature). Wire types expose
enable_prompt_caching as boolean|string (Gemini cached-content
name) and OpenAIChatDelta now carries tool_calls and extra_content.
- Test test_openrouter_no_synthetic_web_search_event_on_tool_choice_none
reads _toolEvent from the top-level SSE payload so a backend
regression cannot mask the assertion.
Adds 7 regression tests covering image_generation gate, forced-function
gate, native_part list replay, legacy fallback, list-content
functionResponse flattening, fetch byte-budget threading, and wire
types.
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* Apply forced-function tool_choice gate to Anthropic, OpenRouter, Kimi
Previously only the Gemini path treated `tool_choice={"type":"function",
"function":{"name":...}}` as a hosted-tool opt-out. Anthropic,
OpenRouter, and Kimi still attached hosted web_search / web_fetch /
code_execution when the caller explicitly pinned a user function plus
`enabled_tools=[...]`. That contradicts the explicit function pin and
bills the caller for unwanted server-side calls.
Mirror the Gemini gate symmetrically:
- Anthropic web_search / web_fetch / code_execution
- OpenRouter `plugins:[{id:"web"}]` + the synthetic web_search SSE
event the same path emits at stream close
- Kimi `_stream_kimi_web_search` dispatch
Adds 4 regression tests:
- test_anthropic_forced_function_tool_choice_drops_hosted_tools
- test_openrouter_forced_function_tool_choice_drops_web_plugin
- test_kimi_forced_function_tool_choice_skips_web_search_helper
- test_openrouter_no_synthetic_web_search_event_on_forced_function_tool_choice
All 146 existing backend tests still pass.
* Strip Gemini-only synthetic tool history on local-GGUF dispatch
After a Gemini chat that ran code_execution / image_generation, switching
the same thread to a local GGUF model used to forward the synthetic
provider-side tool_calls (tagged with `args._server_tool` or carrying a
Gemini `args.google.native_part` payload) and the message-level
`extra_content` to llama-server. The receiving backend has no tool
declaration for those names and no use for Gemini thoughtSignature
metadata; in the worst case it can produce an orphan tool_call_id and a
confused continuation.
Add `_strip_provider_synthetic_tool_history()` and wire it through the
two local message builders:
- `_openai_messages_for_passthrough` (OAI-compat passthrough)
- `_openai_messages_for_gguf_chat` (standard GGUF chat path)
Real user-function `tool_calls` and their matching `role="tool"` replies
survive unchanged; only synthetic provider-side cards and Gemini-only
`extra_content` are stripped. If the synthetic call was the assistant
turn's only payload, the now-empty turn is dropped too so llama-server
does not reject the request.
Adds 2 regression tests:
- test_strip_provider_synthetic_tool_history_drops_synthetic_only
- test_strip_provider_synthetic_tool_history_drops_empty_assistant
142 existing backend tests still pass.
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* Disable Search/Code composer pills for Gemini image-tier models
For external Gemini image-tier models (gemini-2.5-flash-image,
gemini-3.x-image-preview, etc.), the backend unconditionally strips
code_execution and strips web_search on older image ids. Search is
still allowed on Gemini 3.x Pro/Flash image models, which
supportsBuiltinWebSearch already encodes per model.
Before this commit the composer pill gates were:
searchDisabled = !modelLoaded || !(supportsTools || supportsBuiltinWebSearch)
codeDisabled = !modelLoaded || !(supportsTools || supportsBuiltinCodeExecution) || imageModeDisablesCode
`supportsTools` here is a local-runtime fallback that becomes true when
any tool-capable local model has been loaded in the session. With a
local tool-capable runtime active, switching the chat to an external
Gemini image-tier model used to leave Search/Code clickable, even
though the backend will silently drop the tool on the wire.
Detect "external provider is Gemini AND the model is image-tier" (via
supportsBuiltinImageGeneration) and gate the two pills strictly on the
provider's own builtin support in that case. Non-Gemini paths and
non-image Gemini models keep the supportsTools fallback unchanged.
* Apply forced-function tool_choice gate to OpenAI Responses path
Round 22 added the gate for Gemini / Anthropic / OpenRouter / Kimi but
missed the OpenAI Responses translator. When a caller pinned a user
function via `tool_choice={"type":"function","function":{"name":...}}`
plus `enabled_tools=["web_search","code_execution","image_generation"]`,
the Responses body still attached `{"type":"web_search"}`,
`{"type":"shell"}`, and `{"type":"image_generation"}` server tools. The
function pin should suppress those for the same privacy + billing reason
the other provider paths now do.
Compute `_responses_tool_choice_forced_function` next to
`_responses_tool_choice_none` and gate each hosted-tool append on
`_responses_hosted_builtins_allowed = not none and not forced_function`.
The fix has to be applied in TWO places: the initial body builder and
`_build_body()` (called by the container-expiry retry path). User
function declarations still flow through so the pin has something to
target, and the Responses-shape `{type:"function", name:"..."}`
`tool_choice` is forwarded unchanged.
Adds regression test `test_openai_responses_forced_function_tool_choice_drops_hosted_tools`.
All 166 existing backend tests across Gemini + Responses + image-gen +
code-exec suites still pass.
* Round 24 P1s: SSRF shared-address gap + extra_content text-only leak + custom-Gemini model list
Three convergent P1s from round 24 review:
1. SSRF: the shared SSRF validator in `tools._validate_and_resolve_host`
used a denylist (is_private / loopback / link_local / multicast /
reserved / unspecified). Python classifies shared address space
(100.64.0.0/10 carrier-grade NAT, plus 240.0.0.0/4, benchmarking
ranges, etc.) with `is_private=False` AND `is_global=False`. The new
Gemini server-side image fetcher therefore accepts URLs whose
hostname resolves to 100.64.0.1 in cloud/VPC deployments. Add
`not ip.is_global` as the primary gate -- a single source of truth
that covers every current and future non-global range.
2. _strip_provider_synthetic_tool_history previously only stripped
message-level `extra_content` when the assistant turn had tool_calls.
A plain text Gemini reply carrying
`extra_content.google.thought_signature` flowed through to
llama-server when the thread was switched to a local GGUF backend.
Always strip message-level `extra_content` on assistant turns.
3. routes/providers.list_provider_models applied Gemini's native
`model_id_allowlist` regex to every Gemini provider, including
custom OAI-compatible bases (LiteLLM, deployment gateways). IDs like
`google/gemini-2.5-flash` and team-prefixed deployment aliases got
filtered out even though the chat-dispatch path now routes them via
the OpenAI-compatible client. Skip registry-level model-id filters
when the configured Gemini base_url host is not the canonical
`generativelanguage.googleapis.com`, mirroring the chat-dispatch
gate.
Three regression tests added:
- test_validate_and_resolve_host_blocks_shared_address_space
- test_strip_provider_synthetic_tool_history_drops_text_only_extra_content
- test_gemini_custom_oai_compat_base_skips_native_allowlist
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* Round 25 P1s: skip synthetic server-tool replay + inline $ref/$defs into Gemini schema
Two convergent reviewer findings on the native Gemini path:
1. _stream_gemini's tool_calls replay loop falls through to a generic
functionCall emission whenever it sees an assistant tool_call. Marked
server-side builtin cards (web_search / web_fetch tagged with
_server_tool or args.google.native_part) hit that fallthrough with no
replayable native_part, which produces an outbound functionCall whose
name is not a declared user function. The Gemini turn 400s on the
undeclared name. Guard the loop to drop those entries instead, while
keeping the existing code_execution / image_generation native-part
replay branch intact.
2. _sanitize_gemini_schema uses a strict allowlist that drops local
$ref / $defs references. Pydantic-generated tool schemas hoist nested
object shapes into $defs and reference them via {"$ref": "#/$defs/X"},
so a property like address: {"$ref": "#/$defs/Address"} collapsed to
{} on the wire and the model lost the nested fields, types, and
required keys. Resolve local #/... pointers against the schema root
and inline the referenced subtree, with local siblings overriding
the reference (normal JSON Schema composition) and a seen-ref guard
for self-referential schemas.
Added regression coverage:
- test_gemini_native_skips_synthetic_server_builtin_replay
- test_function_declarations_inline_local_refs_into_gemini_schema
- test_function_declarations_inline_local_refs_in_anyof_and_items
- test_function_declarations_self_referential_schema_terminates
All 145 Gemini provider tests pass; touched provider regression set
(OpenAI Responses, code execution, image generation, Anthropic code
execution, Anthropic web_fetch) also 43/43 green.
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* Round 26 P1s: drop orphan Gemini functionResponse + Anthropic /messages synthetic-history strip
Reviewer round 26 surfaced two convergent asymmetric-fix bugs.
1. _stream_gemini drops a synthetic server-tool tool_call (web_search /
web_fetch tagged _server_tool) and also replays code_execution /
image_generation tool_calls as Gemini-native executableCode /
codeExecutionResult / inlineData parts. The matching role="tool"
follow-up was still falling through to the generic functionResponse
branch, producing either an orphan functionResponse (synthetic case)
or a duplicate response pointing at a name with no
functionDeclarations entry (native-part case). Both forms 400 the
next Gemini turn. Track skipped + native-replayed tool_call_ids in
_gemini_skip_tool_result_ids and short-circuit the role="tool"
branch on a match.
2. The Anthropic-compatible local /v1/messages route only called
_drop_empty_assistant_sentinels on the OpenAI-translated history,
while the sibling /v1/chat/completions and GGUF passthrough builders
chain that with _strip_provider_synthetic_tool_history. An Anthropic
caller replaying a prior provider-side tool_use therefore forwarded
fake builtin tool history straight into local llama-server. Apply
the same strip on the Anthropic route after the
anthropic_messages_to_openai conversion.
Regression coverage added:
- test_gemini_native_skips_orphan_function_response_for_dropped_builtin
- test_gemini_native_skips_orphan_function_response_for_native_part_replay
Gemini suite 147/147; touched provider regression set 43/43.
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* Round 27 P1s: native_part location fallback + Gemini image request budget for base64
Two convergent reviewer findings on the native Gemini path.
1. _stream_gemini's synthetic-builtin detector at lines 3519-3524
recognizes args.google.native_part as a server-tool marker, but
_native_part was only loaded from tc.extra_content.google.native_part.
A direct OpenAI-compatible API caller or imported third-party thread
round-trips the payload through function.arguments because
tool_calls[].extra_content is not in the OpenAI spec. The round-25
guard then saw a synthetic builtin with no _native_part and dropped
the entire assistant turn, so the next native Gemini request lost
the prior executableCode / inlineData / codeExecutionResult context.
Fall back to args.google.native_part when extra_content path is
missing, mirroring what the synthetic detector already accepts.
2. _GEMINI_REMOTE_IMAGE_MAX_TOTAL_BYTES capped DECODED bytes at 20MB.
Gemini receives images base64-encoded inside JSON, and base64
inflates payload size by ~4/3. With 20MB decoded the actual JSON
body is ~26.7MB plus prompt overhead, well over Gemini's ~20MB
request limit. Drop the decoded cap to 14MB so realistic multi-
image turns stay safely under 20MB encoded.
Added regression test test_gemini_native_part_falls_back_to_args_google
covering an OpenAI-compat-shaped image_generation tool_call whose
native_part lives only in function.arguments.
Gemini suite 148/148.
* Fix TS build errors from main merge: restore imageParts + refusal return [] + cast image-edit ref
Three errors in chat-adapter.ts surfaced by the frontend tsc step after merging
main into feat/gemini-provider:
1. The Anthropic refusal early-return used main's but
toOpenAIMessages returns SerializedMessage[]; flip to .
2. Restore -- the line
was lost when removing main's conflict block from the function body.
3. selectedImageEditReference splice was inserting OpenAIChatMessage
into a SerializedMessage[] array; the shapes differ on tool_calls.id
nullability. Cast the reference message through unknown -- it carries
no tool_calls, so the runtime payload is structurally compatible.
Reproduced locally with `tsc -b --pretty false` (now passes). Build
also failing in the in-repo `npm run build` step on PR CI; this commit
unblocks all 12 failing UI/API workflows.
* Tighten verbose comments in external_provider.py + chat-adapter.ts
Compress multi-line explanatory comments in the Gemini translator
and the chat adapter without changing any behaviour. All 148 Gemini
provider tests still pass; tsc --noEmit clean.
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Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* Studio: add VLM image-size control for training
Studio vision fine-tuning had no explicit way to cap image resolution, so
users could not trade visual detail against context and memory use from the
training UI, YAML config, or API payload. :) Add a nullable `vision_image_size`
setting that keeps the current model default when unset and applies a
max-side resize when provided.
- Add `vision_image_size` to the training request model, route payload, backend
training config, and frontend API/types plumbing.
- Validate the value server-side as either null or an integer in the supported
256-2048 range.
- Surface an Image Size selector for vision LoRA training with Default plus
common preset sizes.
- Include the value in training start payloads only for image-dataset vision
models, and serialize it into vision-aware YAML configs.
- Map backend model defaults back into the training store and reset the value
when reapplying model defaults.
- Pass the resize through the Torch trainer via `UnslothVisionDataCollator`
using max-dimension semantics.
- Apply the same max-dimension resize in the MLX VLM path before mlx-vlm's
internal collation, preserving aspect ratio and avoiding upscaling.
- Add backend validation coverage and MLX resize-size tests for the new
behavior.
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* Studio: thread vision_image_size into DeepSeek OCR + writable MLX ndarray
- trainer.py: DeepSeek OCR collator now honors the new vision_image_size
setting as image_size. Falls back to 640 when null. base_size stays at
1024 and crop_mode stays True so the Gundam preset's dynamic cropping
of large documents keeps working.
- worker.py: _resize_mlx_vlm_image returns np.array(image, copy=True)
instead of np.asarray(image). The PIL view from np.asarray is not
writable, which makes HF VLM processors emit "The given NumPy array
is not writable, and PyTorch does not support non-writable tensors..."
when they call torch.from_numpy. copy=True keeps the same shape and
dtype but produces a writable buffer.
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* Studio: align YAML export gate with API mapper + extend Image Size dropdown
- training-section.tsx: handleSaveConfig now passes
isVisionModel && isDatasetImage === true to serializeConfigToYaml,
matching buildTrainingStartPayload. Stops vision_image_size from
leaking into exported YAML for text-only datasets where the API
would have sent null.
- params-section.tsx: add 256 to visionImageSizePresets so the
dropdown spans the validator's full [256, 2048] range. Also render
a synthetic SelectItem for the current value when it was loaded
from YAML or model defaults and is not in the preset list, so the
controlled Select always shows the active size.
* Studio: validate vision_image_size in YAML/model-default loader
mapBackendModelConfigToTrainingPatch now mirrors the backend validator
at studio/backend/models/training.py:169 by dropping any value that is
not an integer in [256, 2048]. Pre-fix, an imported YAML like
vision_image_size: 4096 or 640.5 would land in the store and the UI
would happily display it, only to fail when Start Training posted to
the backend. With this guard the store never holds a value the backend
would reject.
* Studio: precise error messages for invalid vision_image_size inputs
Switch the field_validator to mode="before" so True/False surface as
bool (not Pydantic's coerced 1/0) and give a precise
"must be an integer or null" message instead of the misleading
"must be in [256, 2048] (got 1)". Also explicitly accepts numpy
Integral and integral Real scalars so YAML or programmatic callers
using numpy ints keep working.
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* Studio: test that bool inputs yield the precise 'integer or null' error
Regression guard for the validator switch to mode="before". Pre-fix,
vision_image_size: True was rejected with "must be in [256, 2048]
(got 1)" because Pydantic coerced before our check ran. New test
asserts the message now reads "integer or null".
* Studio: tighten vision_image_size loader + YAML save + MLX rounding
Round 2 of follow-up review surfaced three usability issues:
- model-defaults.ts: switching to a model whose backend YAML omits
vision_image_size now explicitly resets the store value to null.
Pre-fix, a stale 2048 from a previous model would silently apply
to the new run because every checked-in model-default file omits
the key.
- training-section.tsx: handleSaveConfig now includes vision fields
unless isDatasetImage is definitively false. isDatasetImage is null
during dataset checks, after dataset edits, and on import; treating
unknown as "drop" would silently lose the user's selection in those
windows. Confirmed-text-only datasets still drop the value.
- worker.py: _mlx_vlm_max_resized_size now mirrors the Torch collator's
integer formula (w * size + size_func // 2) // size_func instead of
Python round(), which uses banker's rounding and disagreed by 1px on
half-pixel inputs like 333x1000 with target 500 (was 166, now 167).
Test_mlx_training_worker_config gains parity assertions.
* Studio: reset vision_image_size in the model-config error fallback path
mapBackendModelConfigToTrainingPatch resets stale image size on the
success path, but if the /api/models/config endpoint throws,
training-config-store.ts falls through to checkVisionModel and only
updates capability flags. Pre-fix that left a stale 2048 (or any
prior selection) in the store, so once dataset detection marked the
new dataset as image, the next training start would silently apply
the previous model's size. The error branch now also resets to the
DEFAULT_HYPERPARAMS.visionImageSize sentinel.
* Studio: revert DeepSeek OCR Image Size knob + move missing-key reset
Round 3 of the parallel-reviewer pass surfaced two issues that I had
introduced earlier in this PR's follow-ups.
- trainer.py: my prior change threaded vision_image_size into the
DeepSeek OCR collator's image_size argument. The collator's
(image_size, base_size, crop_mode) is a single preset
(Tiny / Small / Base / Large / Gundam); changing image_size in
isolation desynchronizes the per-crop pixel grid from num_queries
downstream and produces wrong token grids on documents larger than
the per-crop tile. The fix pins the collator back at the Gundam
preset and logs a clear "ignored for DeepSeek OCR" notice when the
user has selected a non-default Image Size.
- model-defaults.ts + training-config-store.ts: the round 4 fix that
reset visionImageSize when a model YAML omitted the key also fired
on same-model reloads (ensureModelDefaultsLoaded re-fires on page
refresh), wiping a value the user had just selected. The reset is
now in setSelectedModel, gated on selectedModel != previousModel,
so true model switches still clear stale values while reloads keep
the user's selection.
* Studio: extend DeepSeek OCR Image Size exclusion to MLX + frontend
Round 4 of the parallel-reviewer pass flagged that the Torch trainer
exclusion I added did not have a matching MLX guard, and that the UI
still offered the dropdown for DeepSeek OCR even though the backend
ignores it.
- worker.py: _run_mlx_training now mirrors the Torch exclusion. When
the model name matches DeepSeek OCR, vision_image_size is forced
back to None before _adapt_for_mlx_vlm sees it, so dataset images
pass through unchanged just like the Torch path. Emits a clear
status line when this happens.
- params-section.tsx: the Image Size Row is now gated on
showVisionImageSize (showVisionLora && !isDeepseekOcr) instead of
showVisionLora alone, so DeepSeek OCR users no longer see a control
that silently has no effect.
- mappers.ts: buildTrainingStartPayload sends null for vision_image_size
whenever the selected model is DeepSeek OCR, so the backend log line
about ignoring the value never fires from a UI-driven start.
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* Studio: tighten YAML import/save for vision_image_size
Two YAML-path asymmetries that could leak a stale image size into
training:
- parseYamlConfig now treats a missing training.vision_image_size as
null. Without this, importing a YAML saved before this feature (or
any config that omits the key) preserved whatever value the user had
previously set on a different model. The model-defaults reload path
still uses Object.hasOwn so same-model defaults reloads do not wipe
a manual selection; only file import normalises the missing key.
- handleSaveConfig now passes a DeepSeek-OCR-specific guard to
serializeConfigToYaml so saved YAML matches what the API mapper
actually sends. Previously a state with visionImageSize set could
emit the key even though Studio ignored it at training time for
DeepSeek OCR, and a later import for a non-DeepSeek vision model
would activate the stale value.
serializeConfigToYaml gains an optional third parameter
includeVisionImageSize defaulting to includeVisionFields, preserving
the existing 2-arg call signature for backwards compatibility.
* Studio: also reset vision_image_size when YAML lacks a training section
Round 9's parseYamlConfig normalization only fired when the YAML had a
training mapping that omitted vision_image_size. A lora-only or
logging-only YAML (or one with `training: null`) still left trainingObj
unset, the mapper saw no vision_image_size key, and the previously
selected store value persisted into the next training run.
Now an absent or null training section is synthesised as
{ vision_image_size: null } so model-defaults.ts always patches
visionImageSize back to Default on file import. Same-model defaults
reloads still preserve manual choices via the existing Object.hasOwn
gate in mapBackendModelConfigToTrainingPatch.
* Studio: unify parseYamlConfig non-object training handling
A fresh static review (Opus subagent) flagged P3-1: parseYamlConfig
only synthesised vision_image_size: null when raw.training was either
absent or a plain object missing the key. If raw.training is a scalar
or an array (malformed but still parseable), the value was passed
through unchanged, the mapper's Object.hasOwn returned false, and any
previously selected visionImageSize persisted - the same stale-state
leak the lora-only fallback was added to close.
Treat any non-plain-object raw.training (null, array, scalar) as a
malformed/missing section and reset to { vision_image_size: null }.
* Studio: tighten code comments for vision_image_size path
* Studio: tighten vision_image_size validator + restore lost comment context
Two issues surfaced by a fresh adversarial review of the validator:
1. v.strip().lstrip("+-").isdigit() let "++512" / "--256" / "+-+512"
slip past the gate, then int("++512") raised an uncaught ValueError
and Pydantic surfaced "invalid literal for int() with base 10: '++512'"
instead of the contracted "vision_image_size must be an integer or null".
2. str.isdigit() returns True for Unicode digit families (full-width '５１２',
Arabic-Indic '٥١٢', Devanagari '१०२४'), and int() coerces them, so the
value reaching the backend wasn't the ASCII the user typed.
Replaced the lstrip+isdigit pair with re.fullmatch(r'[+-]?[0-9]+', stripped),
which rejects both shapes with the precise error and accepts the documented
ones ('256', '+512', ' 1024 '). Added 8 regression test cases covering
multi-sign strings, lone sign, and the three Unicode digit families.
Also restored comment context lost in f9c39331:
- model-defaults.ts: name studio/backend/models/training.py:_check_vision_image_size
as the spec the [256, 2048] range mirrors, so a maintainer changing the
cap in one file can find the other.
- training-section.tsx: enumerate the three windows in which isDatasetImage
is null (before a check, after dataset edits, on import) so a future
maintainer doesn't simplify the gate to `isCheckingDataset`.
- worker.py: qualify the writable-ndarray comment with "when a resize is
requested" so it doesn't misadvertise the resize=None early-return.
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Ritwij Aryan Parmar
