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5 commits

Author SHA1 Message Date
Sanat Bhargava
1fc8bf53c7
Add Hugging Face dataset streaming mode to Studio (#4946)
* Add HF dataset streaming mode to Studio

* Added default value for datasetStreaming in training-config-store.ts

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* Handle None max_steps for streaming validation

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* studio: fast-fail streaming validation and guard incompatible modes

Reject dataset_streaming at the API boundary when hf_dataset is empty,
the dataset is vision/audio, or max_steps is not set. Probe eval split
with get_dataset_split_names before the streaming load so typos fail
immediately instead of mid-training. Guard column_names=None after map
on iterables. Hide the UI toggle for non-text configurations and clear
the stale flag when config becomes incompatible.

* studio: add streaming dataset tests, iterable helper, and streaming template/format support (WIP)

Work-in-progress on top of feat/studio-dataset-streaming-mode (PR #4946):
- new test_training_streaming.py and iterable.py dataset helper
- streaming support in chat_templates.py and format_conversion.py
- additional streaming guards in trainer.py / models / routes
- frontend streaming wiring in params-section and training-config-store

Committed to preserve uncommitted work before merging latest main.

* studio: fix review-team findings for streaming + main merge

BLOCKER: streaming + raw-text/CPT crashed on len(IterableDataset). Guard it in the
start route (reject format_type=="raw" or training_type=="Continued Pretraining")
and in isStreamingSupported (datasetFormat !== "raw").

Also:
- models/training.py: validate hf_dataset/subset/split (charset+length, block ..//);
  cap dataset slice indices (le=1e9); note validator ordering
- chat_templates.py: guard _apply_custom_mapping .map() for streaming
- trainer.py: warn when packing+streaming
- training-config-store.ts: persist-migration bump to v11 (standalone datasetStreaming
  backfill); add isVisionModel to NON_PERSISTED; toast on silent streamingCompatiblePatch
  mutations in the 4 indirect setters
- tests: route rejections (max_steps, raw/cpt), slice cap, unsafe hf_dataset

* studio: enable raw-text/CPT dataset streaming + streaming UX polish

- raw_text: keep the lazy filter but skip len()-based row counting for
  IterableDatasets so raw-text / CPT can stream; guard the eval-size log
- routes/trainer: drop the raw/CPT streaming block; add a defensive
  not-streaming guard on the eval auto-split (train_test_split)
- dataset-section: streaming toggle is visible-but-disabled and lists the
  exact unmet requirement(s) in its tooltip; block embedding models
- training-start-overlay: show "streaming (no full download)" instead of a
  stuck download bar for streaming runs
- trim the streaming test suite to the high-value cases

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* studio: address streaming review (MLX/embedding guards, sliced eval split, rehydrate timing)

- routes: reject dataset_streaming for embedding training and on Apple Silicon
  (MLX); both loaders materialize the full dataset instead of streaming
- trainer: validate the base eval split name so streaming eval accepts HF slice
  syntax such as "validation[:1000]"
- training-config-store: defer the onRehydrateStorage setState to a microtask so
  it doesn't hit the store's TDZ during synchronous hydration
- test: streaming start rejects embedding models

* studio: harden HF dataset streaming (column_names, split slicing, empty/eval bounds, gating)

Address a deeper streaming review:
- raw_text: resolve_column_names() guards IterableDataset.column_names=None
  (from_generator / unresolved features) so raw-text and CPT streaming no longer
  raise TypeError before training
- models/routes: reject HF slice syntax in train_split/eval_split when streaming
  (load_dataset(streaming=True) raises "Bad split"); reject mixed sources
  (local/S3) and embedding/MLX streaming at the API, not just in the UI
- trainer: an empty post-slice/filter stream fails preflight with a clear message;
  streaming eval is capped (STREAMING_EVAL_MAX_SAMPLES) so each eval terminates;
  the manual-slice shortcut falls back to a regular load when train_split is sliced
- format_conversion: streaming conversions preflight the first mapped row so
  format errors surface before training, not mid-iteration
- frontend: block streaming on Apple Silicon; clear datasetStreaming when a
  dataset is detected as image/audio at start

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* studio: fix CI for streaming PR (lint blocker + no-torch sandbox + preflight test)

- trainer.py: drop unused `IterableDataset` import (hoist safety-net blocker).
- test_training_streaming.py: only select real classes (isinstance type) when
  locating the trainer class, so a MagicMock-stubbed global is never passed to
  object.__new__ (fixes TypeError on the Python 3.10-3.13 jobs).
- no-torch import sandboxes (test_e2e_no_torch_sandbox.py,
  test_studio_import_no_torch.py): teach the chat_templates/format_conversion
  exec stubs and the full-import-chain copy list about the new `.iterable`
  module so the AFTER/runtime cases import without torch again.

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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: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
2026-06-22 17:48:18 +03:00
Daniel Han
a6dc10dad2
Reduce and tighten comments and docstrings across the test suite (#6429)
* Reduce and tighten comments and docstrings in tests

Shorten verbose comments and docstrings across the test suite without
changing any test logic. Remove narration that restates the next line,
collapse long module and test docstrings to a single line, and drop banner
separators. Keep regression context (issue and PR references, run ids),
skip reasons, mocking and timing rationale, license headers, lint and type
directives, and commented-out code.

Comments and docstrings only: an AST signature check confirms no code,
assertions, or string literals changed, and the suite byte-compiles cleanly.

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2026-06-18 01:07:09 -07:00
Daniel Han
187144d4e7
Reduce and tighten code comments and docstrings repo-wide (#6095)
Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
2026-06-08 23:09:51 -07:00
Daniel Han
3ce187da02
Formatting: ruff line-length 100, kwarg-spacing passes, drop blank after short local imports (#6079)
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.
2026-06-08 04:24:13 -07:00
Daniel Han
2ffc8d2cea
tests: add no-torch / Intel Mac test suite (#4646)
* tests: add no-torch / Intel Mac test suite

Add comprehensive test coverage for the no-torch / --no-torch installer
and Studio backend changes introduced in #4624.

Shell tests (tests/sh/test_mac_intel_compat.sh):
- version_ge edge cases (9 tests)
- Architecture detection + Python version resolution (4 tests)
- get_torch_index_url on Darwin (2 tests)
- UNSLOTH_NO_TORCH propagation via SKIP_TORCH (5 tests)
- E2E uv venv creation at Python 3.12 (3 tests)
- E2E torch skip with mock uv shim (4 tests)
- UNSLOTH_NO_TORCH env propagation (4 tests)
- --python override flag parsing + resolution (11 tests)
- --no-torch flag parsing (4 tests)
- SKIP_TORCH unification (3 tests)
- CPU hint printing (2 tests)

Python tests (tests/python/test_no_torch_filtering.py):
- _filter_requirements unit tests with synthetic + real requirements files
- NO_TORCH / IS_MACOS constant parsing
- Subprocess mock of install_python_stack() across platform configs
- install.sh --no-torch flag structural + subprocess tests

Python tests (tests/python/test_studio_import_no_torch.py):
- AST checks for data_collators.py, chat_templates.py, format_conversion.py
- Parametrized venv tests (Python 3.12 + 3.13) for no-torch exec
- Dataclass instantiation without torch
- format_conversion convert functions without torch
- Negative controls (import torch fails, torchao fails)

Python tests (tests/python/test_e2e_no_torch_sandbox.py):
- Before/after import chain tests
- Edge cases (broken torch, fake torch, lazy import)
- Hardware detection without torch
- install.sh logic tests (flag parsing, version resolution)
- install_python_stack filtering tests
- Live server startup tests (opt-in via @server marker)

* fix: address review comments on test suite

- Fix always-true assertion in test_studio_import_no_torch.py (or True)
- Make IS_MACOS test platform-aware instead of hardcoding Linux
- Restore torchvision + torchaudio in server test cleanup (not just torch)
- Include server stderr in skip message for easier debugging

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2026-03-27 02:33:45 -07:00