* Auto-install SSM kernels (causal-conv1d, mamba-ssm) for inference loads
Mamba/SSM hybrids (Nemotron-H/Nano, Falcon-H1, Granite-4.0-H, ...) lazily import
mamba_ssm / causal_conv1d during from_pretrained, so loading them for chat failed
with 'mamba-ssm is required by the Mamba model but cannot be imported'. The training
worker already wheel-first installs these before a fine-tune; the inference worker
did not. Add utils/ssm_runtime.ensure_ssm_runtime and call it from the inference load
path so the same models load for inference. Training worker is untouched; a drift
test keeps the shared detection and pinned versions in lockstep.
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* ssm_runtime: invalidate import caches, skip MLX, cover LoRA base
- Invalidate importlib finder caches in _is_importable and after a successful
wheel install, so a kernel installed earlier in this same process is actually
importable when the modeling code lazy-imports it during from_pretrained.
- Skip the SSM kernel install entirely on the MLX (Apple Silicon) load path:
these are CUDA/ROCm Torch kernels with no MLX use and no macOS prebuilt wheel,
so the source build would fail before the MLX backend loads the model.
- For LoRA loads, also run detection over the resolved base model, since an
adapter id like 'me/my-lora' won't match the SSM heuristics but its SSM base
(Nemotron-H, ...) is what needs the kernels.
Adds tests for cache invalidation and the MLX-skip / LoRA-base worker wiring.
* Tighten SSM autoinstall comments
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* ssm_runtime: verify wheel imports, HIP-aware source build, build heartbeat
Address review feedback:
- Verify a prebuilt wheel actually imports before trusting it; a CUDA/ABI-mismatched
wheel now falls back to a source build instead of returning success and failing later
with the cryptic lazy-import error.
- HIP-aware source build: require hipcc on ROCm, inject clang --gcc-install-dir, and use
the 1800s timeout, mirroring the training worker (ROCm has no prebuilt wheel).
- Emit a status heartbeat every 60s during the source build so a long (ROCm) build does
not trip the orchestrator's 300s inactivity timeout.
Tests cover the wheel-not-importable fallback and the missing-hipcc ROCm bail.
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* Make causal-conv1d best-effort and harden the SSM source build
- causal-conv1d is a fast path: models that merely want it (Qwen3-Next, LFM2)
fall back to torch, so a failed install must not reject an otherwise loadable
chat model on Windows/CPU/macOS or an ABI without a wheel. Only a true SSM
model's mamba-ssm requirement stays fatal, matching the training worker which
treats causal-conv1d as best-effort.
- The source build is reached only when not importable, including a wheel that
installed but failed to import; add --reinstall/--force-reinstall so it
replaces the broken install instead of no-opping as already satisfied.
- Add --no-cache to the ROCm uv source build to avoid reusing stale artifacts
from a partial HIP build, mirroring the training worker.
* Address review: install SSM kernels before transformers, harden import + Windows
Codex:
- Install the SSM kernels before importing transformers. run_inference_process
imported core.inference.inference (which imports unsloth/transformers) before the
load, and a sidecar transformers can evaluate its optional-backend gates against
the import state; installing causal_conv1d/mamba_ssm afterwards left those gates
unsatisfied and a Nemotron/Falcon/Granite load still failed with "mamba-ssm is
required". The initial model's kernels are now installed in run_inference_process
before the ML import, via a shared _ensure_ssm_kernels helper; _handle_load keeps
calling it (idempotent) for a LoRA's base and for later in-process loads.
- _is_importable now treats any import failure as "not importable", not only
ImportError. An ABI-incompatible native kernel (undefined symbol after a torch/CUDA
upgrade) raises OSError/RuntimeError; letting those escape reported
ssm_runtime_install_failed instead of falling back to reinstall/source build.
- Skip causal-conv1d on Windows (no prebuilt wheel), mirroring the training worker.
A causal-conv1d-only model (Qwen3-Next/LFM2) no longer drops a chat load into a
multi-minute untimed source build; it uses the torch fallback. mamba-ssm is still
attempted for true SSM hybrids.
Tests: test_ssm_runtime.py +5 (broken-kernel exceptions read as not-importable;
causal-conv1d skipped on win32 while mamba-ssm still installs). 36 passed.
* Trim comments to be more succinct
* Run security gates before installing SSM kernels
The SSM kernel auto-install is name-based (model_is_ssm is a substring match, no
config fetch), so a model id merely containing an SSM substring triggered a
native-package install (possibly a slow source build) before the malware and
remote-code consent gates ran. Extract those gates into _run_security_gates and
call it before the kernel install in both the pre-import path of
run_inference_process and in _handle_load, so a blocked or nonexistent model is
refused before any build. The gates are metadata-only and do not import
transformers, so they are safe to run before the pre-import install.
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* Resolve remote LoRA bases before importing transformers
_resolve_base_model only reads a local adapter_config.json, so a remote LoRA
adapter whose own id has no SSM substring but whose base is a Nemotron/Falcon/
Granite model had its base discovered only by ModelConfig in _handle_load, after
transformers was imported and its optional-backend availability snapshotted, so
the SSM kernel install there was too late. Add _remote_lora_base, a metadata-only
adapter_config.json fetch (no huggingface_hub / transformers import), and use it
in the pre-import path so the base is gated and its kernels pre-installed.
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* Gate only loaded roots, tier on the resolved base, read offline LoRA cache
Three follow-ups to the pre-import resolution:
- The security gate reused the SSM target list, which for a local full fine-tune
includes the config.json-recorded base. That base is never loaded, so scanning
it could falsely block a safe local checkpoint. Gate only the model plus a
genuine LoRA base (matching _handle_load's mc.is_lora), separate from the
broader SSM-install list.
- Tier activation ran on the raw adapter id, so a remote LoRA whose base needs a
sidecar transformers version imported the default and failed. Resolve the base
once up front and activate on it.
- _remote_lora_base bailed on offline before checking the hub cache, missing a
cached adapter's base. Read the cached adapter_config.json when offline or when
the fetch fails.
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* Keep the pre-import gate transformers-free; harden remote LoRA resolution
The pre-import security gate called security_load_subdirs, which imports
model_config and thus transformers, snapshotting optional-backend availability
before the SSM kernels are installed and defeating the ordering. Add
compute_subdirs to _run_security_gates and pass False in the preflight so it scans
from the root only (transformers-free); _handle_load still runs the authoritative
gate with full subdir scoping after the import.
_remote_lora_base now skips existing local relative paths (is_local_path) so a
checkpoint like outputs/run1 is never treated as a Hub repo, and distinguishes a
definitive 404 (not a LoRA -> None) from transient/offline failures (read the
cache), so a repo that is now a full model no longer resolves a stale cached base.
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* Probe a real model id for SSM kernels; respect HF_ENDPOINT
model_is_ssm is a substring match, so an arbitrary name could false-match and
force a mamba-ssm install that fails the load for a non-SSM model:
- a LoRA adapter id like user/falcon-h1-lora (the SSM-relevant code is the base's);
- a local checkpoint under an SSM-named parent dir, e.g. /runs/falcon-h1/llama-ckpt.
Add ssm_probe_identifier, which resolves the base (or a bare local checkpoint's
basename) and feed that to ensure_ssm_runtime from both the pre-import path and
_handle_load, so detection runs against a real model id, never an adapter id or
parent folders.
_remote_lora_base now honors HF_ENDPOINT so enterprise/mirror deployments resolve
the adapter base instead of always hitting huggingface.co.
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* Tighten comments in the pre-import SSM gate/install path
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* Route dense NemotronH models to the transformers 5.10 tier
Dense NemotronH models (e.g. unsloth/NVIDIA-Nemotron-3-Nano-4B) describe their
layer stack with a hybrid_override_pattern that includes '-' (MLP) layers.
transformers only learned to parse that ('-' -> 'mlp' in pattern_mapping, 'mlp'
in valid_types and MIXER_TYPES) in 5.10; on 5.3/5.5 the config raises
KeyError: '-'. The model also ships auto_map remote code, so training and
inference that approve trust_remote_code load fine, but a native (TRC=False)
load such as export hits the built-in parser and fails with
'Failed to load checkpoint: -'.
Detect dense NemotronH from config.json (a '-' in hybrid_override_pattern, or
'mlp' in an expanded layers_block_type) and route it to the 5.10 tier, where the
model loads natively without remote code. Pure-MoE NemotronH configs are
unaffected and keep their existing tier.
Covers both the local config.json and the remote HF-id paths, and adds tests for
the detector and the resulting tier selection.
* Tighten _nemotron_h_needs_mlp_support docstring
* Detect dense NemotronH in nested, cached, and resolved-away configs
Three gaps could still route a dense NemotronH (MLP '-' layers) to a tier
below 5.10 and hit KeyError: '-':
- VL wrappers (e.g. NemotronH_Nano_VL_V2) keep the dense language model under
llm_config/text_config; the detector only checked the top-level model_type.
Recurse into nested language configs.
- Offline or blocked config fetches returned None for an already-downloaded
repo. Read config.json from the HF hub cache before any network.
- A local checkpoint resolves to its base before tiering, so an offline/private
base discarded the local config that revealed the dense pattern. Prefer the
higher tier of the resolved base and the original path.
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* Harden NemotronH tier detection follow-ups
Address review of the nested/cached/resolved-away detection:
- The local re-check ran the full tier detector on the original path, so a bare
LoRA adapter under e.g. /runs/gemma-4-x/llama-lora could upgrade a default base
via directory-name substrings. Gate the re-check on a real local config.json so
it reads metadata, not path names.
- The HF hub cache was read before any network, so an online tier check could
serve stale config.json after the repo changed upstream. Consult the cache only
offline or after a failed fetch.
- Reading the cache imported huggingface_hub during tier detection, which runs
before a sidecar venv is activated and could pin the default-env hub into
sys.modules. Resolve the cache path with stdlib only.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Trim comments to be more succinct
* Select newest hub-cache snapshot by mtime and retry transient config fetches
The HF cache fallback in tier detection picked the lexicographically-first
snapshot when refs/main was absent (commit-pinned downloads), which can be an
older SHA than the Hub would load. Sort snapshots by mtime instead.
A transient online fetch failure cached the hub-cache fallback under the normal
(model_name, token) key, so a long-lived worker kept serving stale metadata even
after connectivity recovered. Return the fallback without memoizing it so the
next call retries the network.
* Harden config.json tier detection against auth failures and transient blips
- _load_config_json: a 401/403/404 from the raw Hub request is a definitive access
answer, not an outage. Return None instead of falling back to the HF hub cache, so
an unauthenticated or wrong-token request can never read another caller's cached
private metadata.
- _check_config_needs_510/550: only memoize the derived tier when the underlying
config read was definitive (local file, offline cache, or a completed fetch).
A transient fetch fallback is no longer pinned, so the tier is re-evaluated once
connectivity returns instead of staying stuck on the lower tier.
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* Tighten comments in tier-detection auth/cache paths
---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: log transformers version-switching decisions and stop swallowing MLX activation failures
Two logging gaps in dynamic transformers version switching (issue #6103):
1. get_transformers_tier returned a tier with no trace of why. Add an
info log at each decision point naming the model and the trigger
(which substring matched, or which config check fired), so a model
landing on the wrong tier is diagnosable.
2. The MLX fast-path in run_training_process activated the transformers
version inside a bare 'except Exception: pass', silently swallowing
failures while the non-MLX path reports them. A missing or broken
version venv (e.g. Gemma-4 needing 5.5.0) left no trace and only a
confusing downstream crash. Extract a small _activate_transformers_version_or_warn
helper that logs a warning on failure while keeping the non-fatal
fall-through, and call it from the MLX path.
Adds tier-selection logging tests and helper warn/silent tests.
* Studio: clarify path-prepend log, warn on venv version mismatch, log per-package install progress
Completes the remaining logging items of #6103 in studio/backend/utils/transformers_version.py:
- activate_transformers_for_subprocess: the early "Activated transformers X.X.X" line was misleading because at that point only the venv directory has been prepended to sys.path, not imported. It now says it prepended the venv to sys.path and notes the loaded version is confirmed later by "Subprocess loaded transformers ...".
- _venv_dir_is_valid: a detected version mismatch is logged at warning instead of info, since it immediately triggers a full venv wipe and reinstall that should be visible in the logs.
- _ensure_venv_dir: log each package as it starts installing with an N/M progress counter, so a slow runtime install is not mistaken for a hang (pip/uv output is piped and only surfaced on error).
Adds tests covering all three behaviours; pre-existing unused imports are left untouched.
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* Studio: make tier log-capture tests independent of import order
The new issue #6103 caplog assertions in test_transformers_version.py
relied on the module-level sys.modules.setdefault("loggers", stub)
winning the import race. In a full backend pytest run another module
(for example test_log_filter_no_truncation, collected earlier) imports
the real loggers first, so the setdefault is a no-op and
transformers_version.logger becomes a structlog/stdout logger that
caplog cannot capture -- the tier, activation, venv-mismatch and
install-progress log assertions then fail even though the line was
emitted.
Bind a real stdlib logger to transformers_version.logger for the
duration of each test via an autouse fixture, so the module logs through
logging and caplog captures them regardless of collection order.
* Studio: log local checkpoint tier decisions and warn on MLX inference activation
- get_transformers_tier: the local config.json fast path returned a tier
without logging it, so local checkpoints stayed opaque while HF ids were
traceable. Log each decision there too, with a caplog regression test.
- inference worker: the MLX path swallowed _activate_transformers_version
failures with a bare except, the same gap issue #6103 fixed for training.
Warn instead, keeping the non-fatal fall-through.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* 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
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* Route Gemma 4 assistant models to transformers 5.10
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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.
* split venv_t5 into venv_t5_530 and venv_t5_550 for tiered transformers 5.x support
* fix bfloat16 crash on T4 for FORCE_FLOAT32 models and disable trust_remote_code auto-enable for native t5 models
* revert FORCE_FLOAT32 dtype change
* restrict trust_remote_code auto-enable to Nemotron models only
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* use config.json model_type for tier detection, add unsloth/nvidia namespace guard
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* Revert "[pre-commit.ci] auto fixes from pre-commit.com hooks"
This reverts commit fb43d468e2.
* Revert "use config.json model_type for tier detection, add unsloth/nvidia namespace guard"
This reverts commit fc49ae2453.
* add unsloth/nvidia namespace guard to Nemotron trust_remote_code auto-enable
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* reorder tier checks: all substring matches before config.json fetches
* extract shared activate_transformers_for_subprocess into transformers_version.py
* narrow Nemotron trust_remote_code to nemotron_h/nemotron-3-nano, add to export worker
* clean venv_t5 dirs before re-install in setup.sh, clarify version alias comment
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* run venv_t5 migration outside deps fast-path gate in both setup scripts
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* feat: support full model GGUF export, disable incompatible methods in UI
* fix: resolve base model from config.json for venv_t5 export switching
* feat: detect BNB-quantized models and disable all export methods for quantized non-PEFT checkpoints
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* fix: relocate Ollama Modelfile alongside GGUFs during non-PEFT export cleanup
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Daniel Han