docs + research: NEW PPL RECORD 1.0040x, 18 archs validated, 9 HF uc-… #5
Workflow file for this run
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: Tests | |
| on: | |
| push: | |
| branches: [master, main] | |
| pull_request: | |
| branches: [master, main] | |
| jobs: | |
| test: | |
| runs-on: ubuntu-latest | |
| strategy: | |
| matrix: | |
| python-version: ["3.11", "3.12"] | |
| steps: | |
| - uses: actions/checkout@v4 | |
| - name: Set up Python ${{ matrix.python-version }} | |
| uses: actions/setup-python@v5 | |
| with: | |
| python-version: ${{ matrix.python-version }} | |
| - name: Install dependencies | |
| run: | | |
| pip install torch --index-url https://download.pytorch.org/whl/cpu | |
| pip install transformers numpy scipy tqdm | |
| - name: Import test (all modules) | |
| run: | | |
| python -c " | |
| import importlib, os, sys | |
| sys.path.insert(0, '.') | |
| errors = [] | |
| for f in sorted(os.listdir('ultracompress')): | |
| if f.endswith('.py') and f != '__init__.py': | |
| mod = f[:-3] | |
| try: | |
| importlib.import_module(f'ultracompress.{mod}') | |
| except Exception as e: | |
| errors.append((mod, str(e))) | |
| if errors: | |
| print(f'{len(errors)} modules failed to import:') | |
| for mod, err in errors: | |
| print(f' {mod}: {err}') | |
| else: | |
| print(f'All modules imported successfully!') | |
| # Don't fail on import errors for now (some need GPU) | |
| " | |
| - name: Unit tests (CPU) | |
| run: | | |
| python -c " | |
| import torch | |
| from ultracompress.moonshot import FractalBlock, FractalModel | |
| # Test FractalBlock | |
| block = FractalBlock(256, 4, ff_mult=1) | |
| x = torch.randn(1, 8, 256) | |
| gamma = torch.ones(256) | |
| beta = torch.zeros(256) | |
| out = block(x, gamma, beta) | |
| assert out.shape == x.shape, f'Block output shape mismatch: {out.shape}' | |
| print('FractalBlock: OK') | |
| # Test FractalModel | |
| model = FractalModel(256, 4, 2, 4, 1000, 1) | |
| tokens = torch.randint(0, 1000, (1, 8)) | |
| logits = model(tokens) | |
| assert logits.shape == (1, 8, 1000), f'Model output shape: {logits.shape}' | |
| print('FractalModel: OK') | |
| # Test return_hidden | |
| logits, hidden = model(tokens, return_hidden=True) | |
| assert len(hidden) == 8, f'Expected 8 hidden states, got {len(hidden)}' | |
| print('return_hidden: OK') | |
| print('All tests passed!') | |
| " |