* tests: read checked-in files as UTF-8 instead of the platform default
Path.read_text() with no encoding uses locale.getpreferredencoding(), which
is UTF-8 on the Linux runners and cp1252 on a stock Windows install. Nine
module-level reads of checked-in source files were relying on that default.
studio/backend/routes/inference.py carries the DeepSeek tool-call token
regexes, so it holds U+FF5C and U+2581. Under cp1252 that read raised
UnicodeDecodeError on byte 0x81 at position 97806, and because the reads run
at import time it took test_cancel_atomicity.py and test_cancel_id_wiring.py
out at collection, not as failures. Green on CI, permanently broken for a
Windows contributor running the suite locally.
Adds a guard: at module scope there is no tmp_path fixture, so a bare
read_text()/write_text()/open() there is always touching a checked-in file.
That makes the rule mechanical enough to enforce with no allowlist, while
staying quiet about temp-dir I/O inside test bodies where the platform
default is harmless.
The repo already spells this correctly in 464 other places; this only stops
the stragglers coming back.
* tests: cover import-time helper reads and keep the guard py3.9-safe
Follows up on the Codex review:
- add `from __future__ import annotations`, since `str | None` in
`_offender` is evaluated at import on Python 3.9 and pyproject declares
requires-python ">=3.9,<3.15".
- widen the guard from module scope to import time. Class bodies and the
bodies of module-level helpers called from an executing statement run
during collection too, so `CODE = _extract_mixed_precision_code()` was
the same hazard as an inline read. `if __name__ == "__main__":` blocks
are skipped: pytest never executes them.
- scan studio/backend/tests/ as well as tests/. Both trees are collected
on Windows by separate CI jobs, and the offender that started this,
test_tool_xml_strip.py reading routes/inference.py, lives there.
Widening it surfaced seven more import-time reads of checked-in sources;
all now name utf-8.
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* Harden the import-time encoding guard for PR #7438
Close the detector gaps raised in review, all of which I reproduced against
the actual AST before changing anything.
False negatives (the guard let a real hazard through):
- _is_main_guard ignored the comparison operator, so if __name__ != "__main__"
counted as script-only even though its body runs at import.
- The else arm of a main guard was discarded with the rest of the If node.
- Decorators and argument defaults on a module-level def were skipped with the
body, though both are evaluated when the def executes.
- Path.open() in text mode was invisible; only builtin open() was matched.
- encoding = None and encoding = "locale" both re-select the platform default,
but the keyword merely being present counted as pinned.
False positives (the guard would have blocked a compliant contributor):
- A non-literal mode fell through to the "r" default, so open(p, mode) was
flagged even when mode is "rb", where adding encoding= is a ValueError and
there is no edit that satisfies the rule.
- Same for open(*args) and a **kwargs splat, which hide the mode and can hide
an encoding.
- Lambda bodies and comprehension elements were walked even though neither runs
at definition.
Verified: still reports the same 22 offenders on unpatched main, green on this
branch and on the tree merged with latest main (557 files), and an adversarial
corpus of 33 cases now scores zero false positives and zero false negatives.
Also corrected two docstring claims: neither collecting job runs on Windows,
and the read is governed by locale.getencoding().
* Walk eager comprehensions and treat io.open as the builtin
Two regressions from the previous commit, both reproduced against the AST
before changing anything.
Lumping list, set and dict comprehensions in with generator expressions was
wrong. Only a genexp is lazy; the other three run their element expression,
their filters and their nested iterators immediately, so
CONTENTS = [p.read_text() for p in PATHS] at module scope is an import-time
read the guard was silently missing. Comprehensions are now walked in full and
only the genexp keeps the outermost-iterable-only treatment.
io was also in the not-a-path-opener list, but io.open is the builtin, with the
same mode position and the same platform default. io.open(CHECKED_IN_FILE) is
exactly the hazard this guard exists for, so it is matched now, with binary
modes and a pinned encoding still exempt. tarfile.open and fitz.open stay
exempt since neither has an encoding to name.
Verified: 13 targeted cases covering all five eager comprehension forms and
io.open in text, binary and pinned shapes all classify correctly; still 22
offenders on unpatched main; green on this branch and on the tree merged with
latest main.
* Close three more walker gaps in the import-time guard
All three reproduced against the AST first.
A generator expression handed straight to a call is consumed there, so
DATA = "".join(p.read_text() for p in paths) runs its element at import. Only
an unconsumed genexp bound to a name stays lazy, so the walker now follows the
consumed ones in full and keeps the outermost-iterable-only treatment for the
rest.
if "__main__" == __name__ is an equivalent and accepted spelling of the main
guard, but requiring __name__ on the left meant its body was treated as
import-time code. That is a false positive on a block pytest never runs, so
both operand orders are recognised now.
The helper table was built from module-level defs only, so a def in a class
body invoked while the class is constructed was never followed, contradicting
the walker's stated coverage of class bodies. Helpers are now collected from
the module body and from class bodies at any nesting.
Verified: 15 targeted cases including all three fixes and the earlier ones
still classify correctly; still 22 offenders on unpatched main; green on this
branch and on the tree merged with latest main.
* Handle positional read_text encodings, lazy generators and nested helpers
* Guard reads reached from test bodies, unbound Path calls and __file__ paths
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* Follow derived paths, skip lazy generator helpers, cover compressed openers
* Guard the CLI tests, helper parameters and unbound Path arguments
* Discover test roots and follow literal, in-place and tuple-derived paths
* Identify module openers by import, unwrap starred paths, pin subprocess snippets
* Resolve import origins, seed helper locals, follow named generators and parametrize
* Scope imports lexically, list tracked test files, bind unpacked names
* Resolve aliased openers, keyword-only params, destructured targets, next()
* Pin the encoding on subprocess snippets, workflow lint and CLI output for PR #7438
* Harden the CLI encoding guard against detached streams for PR #7438
* Tighten the encoding guard's path and scope analysis for PR #7438
* Resolve path provenance more precisely and keep POSIX stream encodings for PR #7438
* Resolve qualified path classes and scope conditional imports for PR #7438
* Scope CLI stream setup to the entry point and align two encoding pairs for PR #7438
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Expose MLX grad value clipping in Studio
* update test
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* dataset ordering + wd
* fix mlx smoke step expectations
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* cast norm activation output back to original input dtype
* address mlx studio review feedback
* Fix present-but-None seed override for PR #5656
studio/backend/core/training/worker.py
`config.get("model_random_state", random_seed)` only fills the
default when the key is absent. When a caller passes
`config["model_random_state"] = None` explicitly (which happens
any time a JSON payload sends an explicit `null`), the old code
forwarded `None` to FastMLXModel and disabled deterministic init
silently. Same for `lora_random_state`. Treat absent and explicit
None the same way: fall back to random_seed.
studio/backend/tests/test_training_raw_support.py
Update the source-string assertions to match the new lines.
* Guard optional MLXTrainingConfig fields and normalize random_seed for PR #5656
The MLX worker now passes `cast_norm_output_to_input_dtype` and
`dataset_order` only when the linked unsloth-zoo dataclass actually
declares them. Released zoo trees that predate the paired PR can still
construct `MLXTrainingConfig` without raising
`TypeError: unexpected keyword argument`. Once the dependency floor is
bumped to a release that contains both fields, the feature-detect
guards become no-ops.
`random_seed = config.get("random_seed", 3407)` was unguarded against
explicit `None` from raw / backend callers. The same value seeded the
trainer and was the fallback target for `model_random_state` /
`lora_random_state`. Normalize once at the top of the function and use
the normalized value everywhere so an explicit `None` cannot reach
FastMLXModel / get_peft_model / MLXTrainingConfig.
Existing seed source-pattern test updated to match the new normalize
helper. New test asserts the feature-detection guards exist and that
the unconditional kwargs do not include the gated fields.
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* Normalize seed / cast / max_grad_value at TrainingBackend for PR #5656
Round-3 review consensus: the per-field guards that landed in the MLX
worker only protect the MLX path. The same `TrainingBackend.start_training`
config still reaches the CUDA/text trainer at `worker.py:2267`, the
embedding LoRA init at `worker.py:2450`, and embedding TrainingArguments
at `worker.py:2624` with raw `None` values, so an explicit
`random_seed=None` from a raw / backend caller still breaks non-MLX
training even after the previous fix.
Move the normalization into `TrainingBackend.start_training` itself,
where it runs once for every training mode:
- `_coerce_seed(value)`: explicit `None`, non-int, or absent all become
3407. Every downstream worker now sees an int.
- `_coerce_optional_bool(value, default)`: explicit `None` falls back
to `default` instead of `bool(None) == False`. Also normalizes the
common raw-config / YAML string aliases ("true" / "false" / "0" /
"1"). Used for `cast_norm_output_to_input_dtype`.
- `_coerce_optional_nonneg_float(name, value)`: rejects negative
numerics from raw / backend callers, matching the Pydantic
`ge=0` constraint the HTTP route already enforces. Used for
`max_grad_value`.
worker.py MLX path: the existing `bool(config.get(key, True))` for
`cast_norm_output_to_input_dtype` was changed to also fall back on
explicit `None`, so direct worker callers (bypassing
`TrainingBackend.start_training`) are equally safe. `max_grad_value`
also raises on negative values inside the worker for the same reason.
TrainingStartRequest.random_seed default bumped from 42 to 3407 so
direct REST callers that omit the field receive the same default as
the Studio frontend and the MLX worker.
New regression test exercises the three new helpers across explicit
None, valid values, string aliases, and negative-value rejection.
* Tighten feature-detect test paren tracking for PR #5656
The block-extraction used , which stops at the
first inner closing paren (e.g. )
and would silently miss a future unconditional
/ added later in the same dict literal. Switched to
proper paren-depth tracking so the unconditional block is checked end-to-end.
* Shorten verbose comments in MLX Studio backend
* Handle MLX Studio EOS appending by mode
* Wire MLX leaf norm clipping through Studio
* Respect VLM layer filters for explicit LoRA targets
Rationale / guardrails for the local Studio/vision push:
When callers provide explicit VLM LoRA target_modules together with layer filters, FastVisionModel still needs to route the explicit targets through get_peft_regex. Otherwise the layer filters are ignored and adapters can be attached outside the requested language/vision scope.
Do not revert this to plain list(target_modules) for explicit module lists. The CUDA/Studio-facing contract is that explicit targets and layer filters compose: target_modules selects module names, while finetune_language_layers / finetune_vision_layers / finetune_attention_modules / finetune_mlp_modules constrain where those targets are allowed.
The regression test covers the language-only explicit q_proj case and source-checks that explicit targets are wrapped through get_peft_regex when filters are active.
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* Refresh MLX smoke clip-config note for leaf_norm default
Trim the 11-line comment block to 5 lines and correct the stale claim
that MLXTrainingConfig defaults to max_grad_value=1.0. The new default
is max_grad_leaf_norm=1.0 (same memory profile as elementwise but
direction-preserving). The smoke still pins max_grad_value=1.0
explicitly to keep the 13-seed pass-rate fixture stable.
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* Forward max_grad_leaf_norm through the training route and warn when layer filters constrain explicit target_modules for PR #5656
---------
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Co-authored-by: Daniel Han-Chen <info@unsloth.ai>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Leo Borcherding