commit
71ccbac47c
18 changed files with 1928 additions and 0 deletions
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18.editorconfig
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153.gitignore
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13Cargo.toml
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44Makefile
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1README.md
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89benchmark.py
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658poetry.lock
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67pyproject.toml
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36setup.cfg
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221src/lib.rs
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139src/simd.rs
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179src/tensor.rs
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137src/util.rs
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5tests/test_xsor.py
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29xsor/__init__.py
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103xsor/tensor.py
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1xsor/types.py
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35xsor/xsor.pyi
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root = true |
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[*] |
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charset = utf-8 |
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end_of_line = lf |
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indent_size = 4 |
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indent_style = space |
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insert_final_newline = true |
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trim_trailing_whitespace = true |
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|
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[*.{json,jsonnet,libsonnet,yml,yaml}] |
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indent_size = 2 |
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[*.tsv] |
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indent_style = tab |
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|
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[Makefile] |
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indent_style = tab |
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## |
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## Python |
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## |
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|
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# Byte-compiled / optimized / DLL files |
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__pycache__/ |
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*.py[cod] |
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*$py.class |
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|
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# C extensions |
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*.so |
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|
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# Distribution / packaging |
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.Python |
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build/ |
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develop-eggs/ |
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dist/ |
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downloads/ |
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eggs/ |
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.eggs/ |
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lib/ |
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lib64/ |
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parts/ |
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sdist/ |
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var/ |
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wheels/ |
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pip-wheel-metadata/ |
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share/python-wheels/ |
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*.egg-info/ |
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.installed.cfg |
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*.egg |
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MANIFEST |
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|
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# PyInstaller |
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# Usually these files are written by a python script from a template |
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# before PyInstaller builds the exe, so as to inject date/other infos into it. |
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*.manifest |
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*.spec |
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|
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# Installer logs |
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pip-log.txt |
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pip-delete-this-directory.txt |
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|
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# Unit test / coverage reports |
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htmlcov/ |
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.tox/ |
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.nox/ |
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.coverage |
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.coverage.* |
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.cache |
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nosetests.xml |
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coverage.xml |
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*.cover |
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*.py,cover |
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.hypothesis/ |
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.pytest_cache/ |
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|
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# Translations |
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*.mo |
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*.pot |
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|
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# Django stuff: |
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*.log |
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local_settings.py |
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db.sqlite3 |
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db.sqlite3-journal |
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|
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# Flask stuff: |
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instance/ |
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.webassets-cache |
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|
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# Scrapy stuff: |
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.scrapy |
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|
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# Sphinx documentation |
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docs/_build/ |
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|
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# PyBuilder |
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target/ |
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|
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# Jupyter Notebook |
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.ipynb_checkpoints |
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|
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# IPython |
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profile_default/ |
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ipython_config.py |
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|
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# pyenv |
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.python-version |
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|
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# pipenv |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. |
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# However, in case of collaboration, if having platform-specific dependencies or dependencies |
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# having no cross-platform support, pipenv may install dependencies that don't work, or not |
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# install all needed dependencies. |
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#Pipfile.lock |
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|
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow |
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__pypackages__/ |
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|
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# Celery stuff |
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celerybeat-schedule |
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celerybeat.pid |
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|
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# SageMath parsed files |
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*.sage.py |
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|
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# Environments |
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.env |
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.venv |
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env/ |
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venv/ |
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ENV/ |
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env.bak/ |
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venv.bak/ |
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|
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# Spyder project settings |
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.spyderproject |
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.spyproject |
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|
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# Rope project settings |
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.ropeproject |
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|
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# mkdocs documentation |
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/site |
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|
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# mypy |
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.mypy_cache/ |
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.dmypy.json |
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dmypy.json |
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|
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# Pyre type checker |
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.pyre/ |
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|
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## |
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## Rust |
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## |
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|
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# Generated by Cargo |
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# will have compiled files and executables |
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debug/ |
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target/ |
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|
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# Remove Cargo.lock from gitignore if creating an executable, leave it for libraries |
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# More information here https://doc.rust-lang.org/cargo/guide/cargo-toml-vs-cargo-lock.html |
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Cargo.lock |
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|
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# These are backup files generated by rustfmt |
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**/*.rs.bk |
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|
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# MSVC Windows builds of rustc generate these, which store debugging information |
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*.pdb |
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@ -0,0 +1,13 @@ |
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[package] |
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name = "xsor" |
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version = "0.1.0" |
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edition = "2021" |
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|
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[lib] |
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name = "xsor" |
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crate-type = ["cdylib"] |
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[dependencies] |
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anyhow = "1.0.79" |
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num = "0.4.1" |
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pyo3 = { version = "0.20.2", features = ["extension-module"] } |
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PWD := $(shell pwd) |
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POETRY := poetry |
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PYTHON := $(POETRY) run python |
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PYTEST := $(POETRY) run pytest |
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PYSEN := $(POETRY) run pysen |
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MODULE := xsor |
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|
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.PHONY: all |
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all: format lint test |
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|
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.PHONY: build |
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build: |
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$(POETRY) run maturin build --release |
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|
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.PHONY: test |
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test: |
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PYTHONPATH=$(PWD) $(PYTEST) |
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|
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.PHONY: lint |
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lint: |
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PYTHONPATH=$(PWD) $(PYSEN) run lint |
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|
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.PHONY: format |
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format: |
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PYTHONPATH=$(PWD) $(PYSEN) run format |
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|
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.PHONY: clean |
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clean: clean-pyc clean-build |
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|
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.PHONY: clean-pyc |
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clean-pyc: |
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rm -rf .pytest_cache |
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rm -rf .mypy_cache |
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find . -name '*.pyc' -exec rm -f {} + |
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find . -name '*.pyo' -exec rm -f {} + |
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find . -name '*~' -exec rm -f {} + |
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find . -name '__pycache__' -exec rm -fr {} + |
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|
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.PHONY: clean-build |
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clean-build: |
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rm -rf build/ |
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rm -rf dist/ |
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rm -rf *.egg-info/ |
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rm -rf pip-wheel-metadata/ |
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# xsor |
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import time |
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import numpy |
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import xsor |
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def numpy_add() -> None: |
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a = numpy.array(range(100000), dtype=numpy.float32) |
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b = numpy.array(range(100000), dtype=numpy.float32) |
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a + b |
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def numpy_mul() -> None: |
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a = numpy.array(range(100000), dtype=numpy.float32) |
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b = numpy.array(range(100000), dtype=numpy.float32) |
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a * b |
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def numpy_sum() -> None: |
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a = numpy.array(range(100000), dtype=numpy.float32) |
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a.sum() |
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def numpy_matmul() -> None: |
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a = numpy.array(range(100000), dtype=numpy.float32).reshape((100, 1000)) |
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b = numpy.array(range(100000), dtype=numpy.float32).reshape((1000, 100)) |
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a @ b |
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def numpy_add_scalar() -> None: |
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a = numpy.array(range(100000), dtype=numpy.float32) |
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a + 1.0 |
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def xsor_add() -> None: |
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a = xsor.tensor(range(100000)) |
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b = xsor.tensor(range(100000)) |
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a + b |
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def xsor_mul() -> None: |
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a = xsor.tensor(range(100000)) |
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b = xsor.tensor(range(100000)) |
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a * b |
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def xsor_sum() -> None: |
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a = xsor.tensor(range(100000)) |
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a.sum() |
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def xsor_matmul() -> None: |
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a = xsor.tensor(range(100000), (100, 1000)) |
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b = xsor.tensor(range(100000), (1000, 100)) |
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a @ b |
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def xsor_add_scalar() -> None: |
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a = xsor.tensor(range(100000)) |
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a + 1.0 |
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N = 1000 |
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for title, numpy_fn, xsor_fn in ( |
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("ADD", numpy_add, xsor_add), |
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("MUL", numpy_mul, xsor_mul), |
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("SUM", numpy_sum, xsor_sum), |
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("MATMUL", numpy_matmul, xsor_matmul), |
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("ADD_SCALAR", numpy_add_scalar, xsor_add_scalar), |
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): |
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print(f"[ {title} ]") |
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print(" numpy ... ", end="") |
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start = time.time() |
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for _ in range(N): |
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numpy_fn() |
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end = time.time() |
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print(f"{end - start:.3f}s") |
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print(" xsor ... ", end="") |
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start = time.time() |
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for _ in range(N): |
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xsor_fn() |
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end = time.time() |
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print(f"{end - start:.3f}s") |
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print() |
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# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. |
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|
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[[package]] |
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name = "black" |
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version = "23.12.1" |
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description = "The uncompromising code formatter." |
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optional = false |
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python-versions = ">=3.8" |
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[package.dependencies] |
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click = ">=8.0.0" |
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mypy-extensions = ">=0.4.3" |
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packaging = ">=22.0" |
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pathspec = ">=0.9.0" |
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platformdirs = ">=2" |
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tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} |
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typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""} |
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|
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[package.extras] |
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colorama = ["colorama (>=0.4.3)"] |
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d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"] |
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jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] |
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uvloop = ["uvloop (>=0.15.2)"] |
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|
|||
[[package]] |
|||
name = "click" |
|||
version = "8.1.7" |
|||
description = "Composable command line interface toolkit" |
|||
optional = false |
|||
python-versions = ">=3.7" |
|||
files = [ |
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{file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"}, |
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{file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"}, |
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] |
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[package.dependencies] |
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colorama = {version = "*", markers = "platform_system == \"Windows\""} |
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|
|||
[[package]] |
|||
name = "colorama" |
|||
version = "0.4.6" |
|||
description = "Cross-platform colored terminal text." |
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optional = false |
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" |
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files = [ |
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{file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, |
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{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, |
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] |
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|
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[[package]] |
|||
name = "colorlog" |
|||
version = "4.8.0" |
|||
description = "Log formatting with colors!" |
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optional = false |
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python-versions = "*" |
|||
files = [ |
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{file = "colorlog-4.8.0.tar.gz", hash = "sha256:59b53160c60902c405cdec28d38356e09d40686659048893e026ecbd589516b1"}, |
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] |
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[package.dependencies] |
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colorama = {version = "*", markers = "sys_platform == \"win32\""} |
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[[package]] |
|||
name = "dacite" |
|||
version = "1.8.1" |
|||
description = "Simple creation of data classes from dictionaries." |
|||
optional = false |
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python-versions = ">=3.6" |
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files = [ |
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{file = "dacite-1.8.1-py3-none-any.whl", hash = "sha256:cc31ad6fdea1f49962ea42db9421772afe01ac5442380d9a99fcf3d188c61afe"}, |
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] |
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|
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[package.extras] |
|||
dev = ["black", "coveralls", "mypy", "pre-commit", "pylint", "pytest (>=5)", "pytest-benchmark", "pytest-cov"] |
|||
|
|||
[[package]] |
|||
name = "exceptiongroup" |
|||
version = "1.2.0" |
|||
description = "Backport of PEP 654 (exception groups)" |
|||
optional = false |
|||
python-versions = ">=3.7" |
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files = [ |
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{file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"}, |
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{file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"}, |
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] |
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|
|||
[package.extras] |
|||
test = ["pytest (>=6)"] |
|||
|
|||
[[package]] |
|||
name = "flake8" |
|||
version = "6.1.0" |
|||
description = "the modular source code checker: pep8 pyflakes and co" |
|||
optional = false |
|||
python-versions = ">=3.8.1" |
|||
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{file = "ujson-5.9.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5964ea916edfe24af1f4cc68488448fbb1ec27a3ddcddc2b236da575c12c8ae"}, |
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{file = "ujson-5.9.0-cp311-cp311-win32.whl", hash = "sha256:70e06849dfeb2548be48fdd3ceb53300640bc8100c379d6e19d78045e9c26120"}, |
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{file = "ujson-5.9.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:323279e68c195110ef85cbe5edce885219e3d4a48705448720ad925d88c9f851"}, |
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{file = "ujson-5.9.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f91719c6abafe429c1a144cfe27883eace9fb1c09a9c5ef1bcb3ae80a3076a4e"}, |
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{file = "ujson-5.9.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b1c0991c4fe256f5fdb19758f7eac7f47caac29a6c57d0de16a19048eb86bad"}, |
|||
{file = "ujson-5.9.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2a8ea0f55a1396708e564595aaa6696c0d8af532340f477162ff6927ecc46e21"}, |
|||
{file = "ujson-5.9.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:07e0cfdde5fd91f54cd2d7ffb3482c8ff1bf558abf32a8b953a5d169575ae1cd"}, |
|||
{file = "ujson-5.9.0.tar.gz", hash = "sha256:89cc92e73d5501b8a7f48575eeb14ad27156ad092c2e9fc7e3cf949f07e75532"}, |
|||
] |
|||
|
|||
[[package]] |
|||
name = "unidiff" |
|||
version = "0.7.5" |
|||
description = "Unified diff parsing/metadata extraction library." |
|||
optional = false |
|||
python-versions = "*" |
|||
files = [ |
|||
{file = "unidiff-0.7.5-py2.py3-none-any.whl", hash = "sha256:c93bf2265cc1ba2a520e415ab05da587370bc2a3ae9e0414329f54f0c2fc09e8"}, |
|||
{file = "unidiff-0.7.5.tar.gz", hash = "sha256:2e5f0162052248946b9f0970a40e9e124236bf86c82b70821143a6fc1dea2574"}, |
|||
] |
|||
|
|||
[metadata] |
|||
lock-version = "2.0" |
|||
python-versions = ">=3.10,<4.0" |
|||
content-hash = "a344b17318ae47d45ed500dc385ae16ecade1b3f1c125fb50eeb9f02934f7b15" |
|||
@ -0,0 +1,67 @@ |
|||
[tool.poetry] |
|||
name = "xsor" |
|||
version = "0.0.1" |
|||
description = "" |
|||
authors = ["altescy <altescy@fastmail.com>"] |
|||
|
|||
[[tool.poetry.source]] |
|||
name = "pypi" |
|||
priority = "primary" |
|||
|
|||
[tool.poetry.dependencies] |
|||
python = ">=3.10,<4.0" |
|||
|
|||
[tool.poetry.group.dev.dependencies] |
|||
python-language-server = "^0.36.2" |
|||
pytest = "^7.3.1" |
|||
pysen = "^0.10.4" |
|||
black = "^23.3.0" |
|||
isort = "^5.12.0" |
|||
flake8 = "^6.0.0" |
|||
mypy = "^1.2.0" |
|||
maturin = "^1.4.0" |
|||
|
|||
|
|||
[tool.poetry.group.benchmark.dependencies] |
|||
numpy = "*" |
|||
|
|||
[tool.pysen] |
|||
version = "0.10" |
|||
|
|||
[tool.pysen-cli] |
|||
settings_dir = "." |
|||
|
|||
[tool.pysen.lint] |
|||
enable_black = true |
|||
enable_flake8 = true |
|||
enable_isort = true |
|||
enable_mypy = true |
|||
mypy_preset = "strict" |
|||
line_length = 120 |
|||
py_version = "py310" |
|||
[[tool.pysen.lint.mypy_targets]] |
|||
paths = ["."] |
|||
|
|||
[tool.pysen.lint.source] |
|||
includes = ["."] |
|||
excludes = [".venv/"] |
|||
|
|||
|
|||
[tool.black] # automatically generated by pysen |
|||
# pysen ignores and overwrites any modifications |
|||
line-length = 120 |
|||
target-version = ["py310"] |
|||
|
|||
[tool.isort] # automatically generated by pysen |
|||
# pysen ignores and overwrites any modifications |
|||
default_section = "THIRDPARTY" |
|||
ensure_newline_before_comments = true |
|||
force_grid_wrap = 0 |
|||
force_single_line = false |
|||
include_trailing_comma = true |
|||
line_length = 120 |
|||
multi_line_output = 3 |
|||
use_parentheses = true |
|||
[build-system] |
|||
requires = ["maturin>=0.12,<0.13"] |
|||
build-backend = "maturin" |
|||
@ -0,0 +1,36 @@ |
|||
[flake8] |
|||
# automatically generated by pysen |
|||
# pysen ignores and overwrites any modifications |
|||
# e203: black treats : as a binary operator |
|||
# e231: black doesn't put a space after , |
|||
# e501: black may exceed the line-length to follow other style rules |
|||
# w503 or w504: either one needs to be disabled to select w error codes |
|||
ignore = E203,E231,E501,W503 |
|||
max-line-length = 120 |
|||
select = B,B950,C,E,F,W |
|||
|
|||
[mypy] |
|||
# automatically generated by pysen |
|||
# pysen ignores and overwrites any modifications |
|||
check_untyped_defs = True |
|||
disallow_any_decorated = False |
|||
disallow_any_generics = False |
|||
disallow_any_unimported = False |
|||
disallow_incomplete_defs = True |
|||
disallow_subclassing_any = True |
|||
disallow_untyped_calls = True |
|||
disallow_untyped_decorators = False |
|||
disallow_untyped_defs = True |
|||
ignore_errors = False |
|||
ignore_missing_imports = True |
|||
no_implicit_optional = True |
|||
python_version = 3.10 |
|||
show_error_codes = True |
|||
strict_equality = True |
|||
strict_optional = True |
|||
warn_redundant_casts = True |
|||
warn_return_any = True |
|||
warn_unreachable = True |
|||
warn_unused_configs = True |
|||
warn_unused_ignores = False |
|||
|
|||
@ -0,0 +1,221 @@ |
|||
#![feature(portable_simd)]
|
|||
#![feature(iter_collect_into)]
|
|||
|
|||
use pyo3::prelude::*;
|
|||
|
|||
mod simd;
|
|||
mod tensor;
|
|||
mod util;
|
|||
|
|||
use simd::{
|
|||
add_f32_vector_simd, div_f32_tensor_simd, matmul_f32_tensor_simd, mul_f32_tensor_simd,
|
|||
sub_f32_vector_simd, sum_f32_vector_simd,
|
|||
};
|
|||
use tensor::Tensor;
|
|||
use util::broadcast_tensors;
|
|||
|
|||
#[pyclass(name = "TensorF32")]
|
|||
struct PyTensorF32 {
|
|||
tensor: Tensor<f32>,
|
|||
}
|
|||
|
|||
#[pymethods]
|
|||
impl PyTensorF32 {
|
|||
#[new]
|
|||
fn __new__(data: Vec<f32>, shape: Vec<usize>) -> Self {
|
|||
assert_eq!(data.len(), shape.iter().product());
|
|||
Self {
|
|||
tensor: Tensor { data, shape },
|
|||
}
|
|||
}
|
|||
|
|||
#[staticmethod]
|
|||
fn zeros(shape: Vec<usize>) -> Self {
|
|||
Self {
|
|||
tensor: Tensor::zeros(&shape),
|
|||
}
|
|||
}
|
|||
|
|||
#[staticmethod]
|
|||
fn ones(shape: Vec<usize>) -> Self {
|
|||
Self {
|
|||
tensor: Tensor::ones(&shape),
|
|||
}
|
|||
}
|
|||
|
|||
#[staticmethod]
|
|||
fn eye(size: usize) -> Self {
|
|||
Self {
|
|||
tensor: Tensor::eye(size),
|
|||
}
|
|||
}
|
|||
|
|||
fn __repr__(&self) -> String {
|
|||
format!("{}", &self.tensor)
|
|||
}
|
|||
|
|||
fn neg(&self) -> Self {
|
|||
Self {
|
|||
tensor: self.tensor.neg(),
|
|||
}
|
|||
}
|
|||
|
|||
fn add_tensor(&self, other: &Self) -> Self {
|
|||
let tensors: Vec<Tensor<f32>>;
|
|||
let (left, right) = if self.tensor.shape == other.tensor.shape {
|
|||
(&self.tensor, &other.tensor)
|
|||
} else {
|
|||
tensors = broadcast_tensors(&[&self.tensor, &other.tensor]);
|
|||
(&tensors[0], &tensors[1])
|
|||
};
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: add_f32_vector_simd(&left.data, &right.data),
|
|||
shape: left.shape.clone(),
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
fn sub_tensor(&self, other: &Self) -> Self {
|
|||
let tensors: Vec<Tensor<f32>>;
|
|||
let (left, right) = if self.tensor.shape == other.tensor.shape {
|
|||
(&self.tensor, &other.tensor)
|
|||
} else {
|
|||
tensors = broadcast_tensors(&[&self.tensor, &other.tensor]);
|
|||
(&tensors[0], &tensors[1])
|
|||
};
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: sub_f32_vector_simd(&left.data, &right.data),
|
|||
shape: left.shape.clone(),
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
fn mul_tensor(&self, other: &Self) -> Self {
|
|||
let tensors: Vec<Tensor<f32>>;
|
|||
let (left, right) = if self.tensor.shape == other.tensor.shape {
|
|||
(&self.tensor, &other.tensor)
|
|||
} else {
|
|||
tensors = broadcast_tensors(&[&self.tensor, &other.tensor]);
|
|||
(&tensors[0], &tensors[1])
|
|||
};
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: mul_f32_tensor_simd(&left.data, &right.data),
|
|||
shape: left.shape.clone(),
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
fn div_tensor(&self, other: &Self) -> Self {
|
|||
let tensors: Vec<Tensor<f32>>;
|
|||
let (left, right) = if self.tensor.shape == other.tensor.shape {
|
|||
(&self.tensor, &other.tensor)
|
|||
} else {
|
|||
tensors = broadcast_tensors(&[&self.tensor, &other.tensor]);
|
|||
(&tensors[0], &tensors[1])
|
|||
};
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: div_f32_tensor_simd(&left.data, &right.data),
|
|||
shape: left.shape.clone(),
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
fn add_scalar(&self, scalar: f32) -> Self {
|
|||
Self {
|
|||
tensor: self.tensor.add_scalar(scalar),
|
|||
}
|
|||
}
|
|||
|
|||
fn sub_scalar(&self, scalar: f32) -> Self {
|
|||
Self {
|
|||
tensor: self.tensor.sub_scalar(scalar),
|
|||
}
|
|||
}
|
|||
|
|||
fn mul_scalar(&self, scalar: f32) -> Self {
|
|||
Self {
|
|||
tensor: self.tensor.mul_scalar(scalar),
|
|||
}
|
|||
}
|
|||
|
|||
fn div_scalar(&self, scalar: f32) -> Self {
|
|||
Self {
|
|||
tensor: self.tensor.div_scalar(scalar),
|
|||
}
|
|||
}
|
|||
|
|||
fn matmul(&self, other: &Self) -> Self {
|
|||
let (left, right) = (&self.tensor, &other.tensor);
|
|||
assert_eq!(left.shape.len(), 2, "Left tensor must be 2D");
|
|||
assert_eq!(right.shape.len(), 2, "Right tensor must be 2D");
|
|||
assert_eq!(left.shape[1], right.shape[0], "Inner dimensions must match");
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: matmul_f32_tensor_simd(&left.data, &right.data, &left.shape, &right.shape),
|
|||
shape: vec![left.shape[0], right.shape[1]],
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
fn sum(&self) -> Self {
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: vec![sum_f32_vector_simd(&self.tensor.data)],
|
|||
shape: vec![],
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
fn get(&self, index: Vec<usize>) -> Self {
|
|||
let flattened_index = util::get_flattened_indice(&self.tensor.shape, &index);
|
|||
let value = self.tensor.data[flattened_index];
|
|||
Self {
|
|||
tensor: Tensor {
|
|||
data: vec![value],
|
|||
shape: vec![],
|
|||
},
|
|||
}
|
|||
}
|
|||
|
|||
#[getter]
|
|||
fn shape(&self) -> Vec<usize> {
|
|||
self.tensor.shape.clone()
|
|||
}
|
|||
|
|||
#[getter]
|
|||
fn data(&self) -> Vec<f32> {
|
|||
self.tensor.data.clone()
|
|||
}
|
|||
|
|||
#[getter]
|
|||
fn size(&self) -> usize {
|
|||
self.tensor.data.len()
|
|||
}
|
|||
|
|||
#[getter]
|
|||
fn ndim(&self) -> usize {
|
|||
self.tensor.shape.len()
|
|||
}
|
|||
|
|||
#[getter]
|
|||
fn value(&self) -> f32 {
|
|||
assert_eq!(self.tensor.data.len(), 1);
|
|||
self.tensor.data[0]
|
|||
}
|
|||
|
|||
fn reshape(&self, shape: Vec<usize>) -> Self {
|
|||
Self {
|
|||
tensor: self.tensor.reshape(&shape),
|
|||
}
|
|||
}
|
|||
}
|
|||
|
|||
#[pymodule]
|
|||
fn xsor(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
|
|||
m.add_class::<PyTensorF32>()?;
|
|||
Ok(())
|
|||
}
|
|||
@ -0,0 +1,139 @@ |
|||
use std::simd::Simd;
|
|||
|
|||
pub fn sum_f32_vector_simd(a: &[f32]) -> f32 {
|
|||
let chunk_size = 4;
|
|||
let chunks = a.len() / chunk_size;
|
|||
let mut sum = Simd::<f32, 4>::splat(0.0);
|
|||
for i in 0..chunks {
|
|||
let a = Simd::<f32, 4>::from_slice(&a[i * chunk_size..]);
|
|||
sum += a;
|
|||
}
|
|||
let mut result = sum.as_array().iter().sum::<f32>();
|
|||
for i in (chunks * chunk_size)..a.len() {
|
|||
result += a[i];
|
|||
}
|
|||
result
|
|||
}
|
|||
|
|||
pub fn add_f32_vector_simd(a: &[f32], b: &[f32]) -> Vec<f32> {
|
|||
assert_eq!(a.len(), b.len(), "Vectors must be of the same length");
|
|||
let mut result = Vec::with_capacity(a.len());
|
|||
if a.is_empty() {
|
|||
return result;
|
|||
}
|
|||
let chunk_size = 4;
|
|||
let chunks = a.len() / chunk_size;
|
|||
for i in 0..chunks {
|
|||
let a = Simd::<f32, 4>::from_slice(&a[i * chunk_size..]);
|
|||
let b = Simd::<f32, 4>::from_slice(&b[i * chunk_size..]);
|
|||
let c = a + b;
|
|||
result.extend_from_slice(c.as_array());
|
|||
}
|
|||
for i in (chunks * chunk_size)..a.len() {
|
|||
result.push(a[i] + b[i]);
|
|||
}
|
|||
result
|
|||
}
|
|||
|
|||
pub fn sub_f32_vector_simd(a: &[f32], b: &[f32]) -> Vec<f32> {
|
|||
assert_eq!(a.len(), b.len(), "Vectors must be of the same length");
|
|||
let mut result = Vec::with_capacity(a.len());
|
|||
if a.is_empty() {
|
|||
return result;
|
|||
}
|
|||
let chunk_size = 4;
|
|||
let chunks = a.len() / chunk_size;
|
|||
for i in 0..chunks {
|
|||
let a = Simd::<f32, 4>::from_slice(&a[i * chunk_size..]);
|
|||
let b = Simd::<f32, 4>::from_slice(&b[i * chunk_size..]);
|
|||
let c = a - b;
|
|||
result.extend_from_slice(c.as_array());
|
|||
}
|
|||
for i in (chunks * chunk_size)..a.len() {
|
|||
result.push(a[i] - b[i]);
|
|||
}
|
|||
result
|
|||
}
|
|||
|
|||
pub fn mul_f32_tensor_simd(a: &[f32], b: &[f32]) -> Vec<f32> {
|
|||
assert_eq!(a.len(), b.len(), "Vectors must be of the same length");
|
|||
let mut result = Vec::with_capacity(a.len());
|
|||
if a.is_empty() {
|
|||
return result;
|
|||
}
|
|||
let chunk_size = 4;
|
|||
let chunks = a.len() / chunk_size;
|
|||
for i in 0..chunks {
|
|||
let a = Simd::<f32, 4>::from_slice(&a[i * chunk_size..]);
|
|||
let b = Simd::<f32, 4>::from_slice(&b[i * chunk_size..]);
|
|||
let c = a * b;
|
|||
result.extend_from_slice(c.as_array());
|
|||
}
|
|||
for i in (chunks * chunk_size)..a.len() {
|
|||
result.push(a[i] * b[i]);
|
|||
}
|
|||
result
|
|||
}
|
|||
|
|||
pub fn div_f32_tensor_simd(a: &[f32], b: &[f32]) -> Vec<f32> {
|
|||
assert_eq!(a.len(), b.len(), "Vectors must be of the same length");
|
|||
let mut result = Vec::with_capacity(a.len());
|
|||
if a.is_empty() {
|
|||
return result;
|
|||
}
|
|||
let chunk_size = 4;
|
|||
let chunks = a.len() / chunk_size;
|
|||
for i in 0..chunks {
|
|||
let a = Simd::<f32, 4>::from_slice(&a[i * chunk_size..]);
|
|||
let b = Simd::<f32, 4>::from_slice(&b[i * chunk_size..]);
|
|||
let c = a / b;
|
|||
result.extend_from_slice(c.as_array());
|
|||
}
|
|||
for i in (chunks * chunk_size)..a.len() {
|
|||
result.push(a[i] / b[i]);
|
|||
}
|
|||
result
|
|||
}
|
|||
|
|||
pub fn matmul_f32_tensor_simd(
|
|||
a: &[f32],
|
|||
b: &[f32],
|
|||
a_shape: &[usize],
|
|||
b_shape: &[usize],
|
|||
) -> Vec<f32> {
|
|||
assert_eq!(
|
|||
a_shape[1], b_shape[0],
|
|||
"Shapes do not align for matrix multiplication"
|
|||
);
|
|||
|
|||
let m = a_shape[0];
|
|||
let n = b_shape[1];
|
|||
let k = a_shape[1];
|
|||
|
|||
let chunk_size = 4;
|
|||
let chunks = k / chunk_size;
|
|||
|
|||
let mut result = vec![0.0; m * n];
|
|||
let mut bcol = vec![0.0; k];
|
|||
for j in 0..n {
|
|||
for (i, bcol) in bcol.iter_mut().enumerate() {
|
|||
*bcol = b[i * n + j];
|
|||
}
|
|||
for i in 0..m {
|
|||
let arow = &a[i * k..(i + 1) * k];
|
|||
let mut sum = Simd::<f32, 4>::splat(0.0);
|
|||
for chunk in 0..chunks {
|
|||
let a = Simd::<f32, 4>::from_slice(&arow[chunk * chunk_size..]);
|
|||
let b = Simd::<f32, 4>::from_slice(&bcol[chunk * chunk_size..]);
|
|||
sum += a * b;
|
|||
}
|
|||
let mut res = sum.as_array().iter().sum::<f32>();
|
|||
for chunk in (chunks * chunk_size)..k {
|
|||
res += arow[chunk] * bcol[chunk];
|
|||
}
|
|||
result[i * n + j] = res;
|
|||
}
|
|||
}
|
|||
|
|||
result
|
|||
}
|
|||
@ -0,0 +1,179 @@ |
|||
use num::Num;
|
|||
use std::fmt::{self, Display, Formatter};
|
|||
|
|||
#[derive(Debug, PartialEq)]
|
|||
pub struct Tensor<T> {
|
|||
pub data: Vec<T>,
|
|||
pub shape: Vec<usize>,
|
|||
}
|
|||
|
|||
impl<T> Tensor<T>
|
|||
where
|
|||
T: Copy,
|
|||
{
|
|||
pub fn new(data: &[T], shape: &[usize]) -> Self {
|
|||
assert_eq!(data.len(), shape.iter().product());
|
|||
Self {
|
|||
data: data.to_vec(),
|
|||
shape: shape.to_vec(),
|
|||
}
|
|||
}
|
|||
}
|
|||
|
|||
impl<T> Clone for Tensor<T>
|
|||
where
|
|||
T: Copy,
|
|||
{
|
|||
fn clone(&self) -> Self {
|
|||
Self {
|
|||
data: self.data.clone(),
|
|||
shape: self.shape.clone(),
|
|||
}
|
|||
}
|
|||
}
|
|||
|
|||
impl<T> Tensor<T>
|
|||
where
|
|||
T: Copy,
|
|||
{
|
|||
pub fn reshape(&self, shape: &[usize]) -> Self {
|
|||
assert_eq!(self.data.len(), shape.iter().product());
|
|||
Self::new(&self.data, shape)
|
|||
}
|
|||
}
|
|||
|
|||
impl<T> Display for Tensor<T>
|
|||
where
|
|||
T: Display,
|
|||
{
|
|||
fn fmt(&self, f: &mut Formatter<'_>) -> fmt::Result {
|
|||
write!(f, "{}", format_tensor(0, &self.shape, &self.data))
|
|||
}
|
|||
}
|
|||
|
|||
impl<T> Tensor<T>
|
|||
where
|
|||
T: Num + Copy,
|
|||
{
|
|||
pub fn zeros(shape: &[usize]) -> Self {
|
|||
let size = shape.iter().product();
|
|||
let data = vec![T::zero(); size];
|
|||
Self::new(&data, shape)
|
|||
}
|
|||
|
|||
pub fn ones(shape: &[usize]) -> Self {
|
|||
let size = shape.iter().product();
|
|||
let data = vec![T::one(); size];
|
|||
Self::new(&data, shape)
|
|||
}
|
|||
|
|||
pub fn eye(size: usize) -> Self {
|
|||
let mut data = vec![T::zero(); size * size];
|
|||
for i in 0..size {
|
|||
data[i * size + i] = T::one();
|
|||
}
|
|||
Self::new(&data, &[size, size])
|
|||
}
|
|||
|
|||
pub fn neg(&self) -> Self {
|
|||
let data: Vec<_> = self.data.iter().map(|a| T::zero() - *a).collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn add(&self, other: &Self) -> Self {
|
|||
let data: Vec<_> = self
|
|||
.data
|
|||
.iter()
|
|||
.zip(other.data.iter())
|
|||
.map(|(a, b)| *a + *b)
|
|||
.collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn sub(&self, other: &Self) -> Self {
|
|||
let data: Vec<_> = self
|
|||
.data
|
|||
.iter()
|
|||
.zip(other.data.iter())
|
|||
.map(|(a, b)| *a - *b)
|
|||
.collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn mul(&self, other: &Self) -> Self {
|
|||
let data: Vec<_> = self
|
|||
.data
|
|||
.iter()
|
|||
.zip(other.data.iter())
|
|||
.map(|(a, b)| *a * *b)
|
|||
.collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn div(&self, other: &Self) -> Self {
|
|||
let data: Vec<_> = self
|
|||
.data
|
|||
.iter()
|
|||
.zip(other.data.iter())
|
|||
.map(|(a, b)| *a / *b)
|
|||
.collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn add_scalar(&self, scalar: T) -> Self {
|
|||
let data: Vec<_> = self.data.iter().map(|a| *a + scalar).collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn sub_scalar(&self, scalar: T) -> Self {
|
|||
let data: Vec<_> = self.data.iter().map(|a| *a - scalar).collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn mul_scalar(&self, scalar: T) -> Self {
|
|||
let data: Vec<_> = self.data.iter().map(|a| *a * scalar).collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
|
|||
pub fn div_scalar(&self, scalar: T) -> Self {
|
|||
let data: Vec<_> = self.data.iter().map(|a| *a / scalar).collect();
|
|||
Self::new(&data, &self.shape)
|
|||
}
|
|||
}
|
|||
|
|||
fn format_tensor<T>(level: usize, shape: &[usize], data: &[T]) -> String |
|||
where
|
|||
T: Display,
|
|||
{
|
|||
if shape.is_empty() {
|
|||
return format!("{}", data[0]);
|
|||
}
|
|||
let dim = shape[0];
|
|||
let extra_size: usize = shape[1..].iter().product();
|
|||
let substrings = (0..dim)
|
|||
.map(|i| {
|
|||
format_tensor(
|
|||
level + 1,
|
|||
&shape[1..],
|
|||
&data[i * extra_size..(i + 1) * extra_size],
|
|||
)
|
|||
})
|
|||
.collect::<Vec<_>>();
|
|||
if shape.len() == 1 {
|
|||
return format!("[{}]", substrings.join(", "));
|
|||
}
|
|||
let newline = "\n";
|
|||
let indent = " ".repeat(level);
|
|||
let inner_delimiter = format!(",{}", newline);
|
|||
let outer_delimiter = format!("{}{}", newline, indent);
|
|||
format!(
|
|||
"[{}{}{}]",
|
|||
newline,
|
|||
substrings
|
|||
.iter()
|
|||
.map(|s| format!("{} {}", &indent, &s))
|
|||
.collect::<Vec<_>>()
|
|||
.join(&inner_delimiter),
|
|||
outer_delimiter,
|
|||
)
|
|||
}
|
|||
@ -0,0 +1,137 @@ |
|||
use std::collections::HashSet;
|
|||
|
|||
use super::tensor::Tensor;
|
|||
|
|||
pub fn get_flattened_indice(shape: &[usize], index: &[usize]) -> usize {
|
|||
assert_eq!(shape.len(), index.len());
|
|||
index.iter().enumerate().fold(0, |acc, (i, &v)| {
|
|||
acc + v * shape[i + 1..].iter().product::<usize>()
|
|||
})
|
|||
}
|
|||
|
|||
pub fn get_nested_indice(shape: &[usize], index: usize) -> Vec<usize> {
|
|||
let mut index = index;
|
|||
let mut nested_index = vec![0; shape.len()];
|
|||
for i in (0..shape.len()).rev() {
|
|||
let size = shape[i];
|
|||
nested_index[i] = index % size;
|
|||
index /= size;
|
|||
}
|
|||
nested_index
|
|||
}
|
|||
|
|||
pub fn broadcast_shapes(shapes: &[&[usize]]) -> Vec<usize> {
|
|||
let max_ndim = shapes.iter().map(|s| s.len()).max().unwrap();
|
|||
let shapes: Vec<Vec<usize>> = shapes
|
|||
.iter()
|
|||
.map(|s| {
|
|||
let mut s = s.to_vec();
|
|||
while s.len() < max_ndim {
|
|||
s.insert(0, 1);
|
|||
}
|
|||
s
|
|||
})
|
|||
.collect();
|
|||
let mut shape = vec![1; max_ndim];
|
|||
for dim in 0..max_ndim {
|
|||
let sizes: HashSet<usize> = shapes.iter().map(|s| s[dim]).collect();
|
|||
assert!(
|
|||
sizes.len() <= 2 || (sizes.len() == 2 && sizes.contains(&1)),
|
|||
"Shapes are not broadcastable"
|
|||
);
|
|||
let max_size = sizes.iter().max().unwrap();
|
|||
shape[dim] = *max_size;
|
|||
}
|
|||
shape
|
|||
}
|
|||
|
|||
pub fn broadcast_to<T>(tensor: &Tensor<T>, shape: &[usize]) -> Tensor<T>
|
|||
where
|
|||
T: Copy,
|
|||
{
|
|||
assert!(tensor.shape.len() <= shape.len(), "Shape is too small");
|
|||
if tensor.shape == shape {
|
|||
return tensor.clone();
|
|||
}
|
|||
if tensor.shape.is_empty() {
|
|||
return Tensor {
|
|||
data: vec![tensor.data[0]; shape.iter().product()],
|
|||
shape: shape.to_vec(),
|
|||
};
|
|||
}
|
|||
let shape = broadcast_shapes(&[&tensor.shape, shape]);
|
|||
let diff_ndim = shape.len() - tensor.shape.len();
|
|||
let expanded_shape: Vec<usize> = shape
|
|||
.iter()
|
|||
.enumerate()
|
|||
.map(|(i, &v)| if i < diff_ndim { 1 } else { v })
|
|||
.collect();
|
|||
let dims_to_expand: HashSet<usize> = shape
|
|||
.iter()
|
|||
.enumerate()
|
|||
.filter(|(i, &v)| *i < diff_ndim || v != tensor.shape[*i - diff_ndim])
|
|||
.map(|(i, _)| i)
|
|||
.collect();
|
|||
let size = shape.iter().product();
|
|||
let mut data = vec![tensor.data[0]; size];
|
|||
for index in 0..size {
|
|||
let nested_index = get_nested_indice(&shape, index)
|
|||
.iter()
|
|||
.enumerate()
|
|||
.map(|(i, &v)| if dims_to_expand.contains(&i) { 0 } else { v })
|
|||
.collect::<Vec<usize>>();
|
|||
let flattened_index = get_flattened_indice(&expanded_shape, &nested_index);
|
|||
data[index] = tensor.data[flattened_index];
|
|||
}
|
|||
Tensor { data, shape }
|
|||
}
|
|||
|
|||
pub fn broadcast_tensors<T>(tensors: &[&Tensor<T>]) -> Vec<Tensor<T>>
|
|||
where
|
|||
T: Copy,
|
|||
{
|
|||
let shape = broadcast_shapes(
|
|||
&tensors
|
|||
.iter()
|
|||
.map(|t| t.shape.as_slice())
|
|||
.collect::<Vec<_>>(),
|
|||
);
|
|||
tensors
|
|||
.iter()
|
|||
.map(|t| broadcast_to(t, &shape))
|
|||
.collect::<Vec<_>>()
|
|||
}
|
|||
|
|||
#[cfg(test)]
|
|||
mod tests {
|
|||
use super::*;
|
|||
|
|||
#[test]
|
|||
fn test_get_flattened_indice() {
|
|||
assert_eq!(get_flattened_indice(&[2, 3], &[1, 2]), 5);
|
|||
assert_eq!(get_flattened_indice(&[2, 3], &[0, 0]), 0);
|
|||
assert_eq!(get_flattened_indice(&[2, 3], &[1, 0]), 3);
|
|||
}
|
|||
|
|||
#[test]
|
|||
fn test_get_nested_indice() {
|
|||
assert_eq!(get_nested_indice(&[2, 3], 5), vec![1, 2]);
|
|||
assert_eq!(get_nested_indice(&[2, 3], 0), vec![0, 0]);
|
|||
assert_eq!(get_nested_indice(&[2, 3], 3), vec![1, 0]);
|
|||
}
|
|||
|
|||
#[test]
|
|||
fn test_broadcast_shapes() {
|
|||
assert_eq!(broadcast_shapes(&[&[1, 2, 3], &[1, 2, 3]]), vec![1, 2, 3]);
|
|||
assert_eq!(broadcast_shapes(&[&[2, 3], &[3, 2, 1]]), vec![3, 2, 3]);
|
|||
}
|
|||
|
|||
#[test]
|
|||
fn test_broadcast_to() {
|
|||
let tensor = Tensor::new(&[1, 2, 3], &[3]);
|
|||
assert_eq!(
|
|||
broadcast_to(&tensor, &[2, 3]),
|
|||
Tensor::new(&[1, 2, 3, 1, 2, 3], &[2, 3]),
|
|||
);
|
|||
}
|
|||
}
|
|||
@ -0,0 +1,5 @@ |
|||
import xsor |
|||
|
|||
|
|||
def test_version() -> None: |
|||
assert xsor.__version__ == "0.0.1" |
|||
@ -0,0 +1,29 @@ |
|||
from typing import Sequence |
|||
|
|||
from xsor.tensor import Tensor |
|||
|
|||
__version__ = "0.0.1" |
|||
__all__ = ["Tensor", "tensor", "zeros", "ones", "eye"] |
|||
|
|||
|
|||
def tensor( |
|||
data: Sequence[float], |
|||
shape: Sequence[int] | None = None, |
|||
) -> Tensor: |
|||
return Tensor(data, shape) |
|||
|
|||
|
|||
def zeros(shape: Sequence[int]) -> Tensor: |
|||
return Tensor.zeros(shape) |
|||
|
|||
|
|||
def ones(shape: Sequence[int]) -> Tensor: |
|||
return Tensor.ones(shape) |
|||
|
|||
|
|||
def eye(size: int) -> Tensor: |
|||
return Tensor.eye(size) |
|||
|
|||
|
|||
def sum(tensor: Tensor) -> Tensor: |
|||
return tensor.sum() |
|||
@ -0,0 +1,103 @@ |
|||
from __future__ import annotations |
|||
|
|||
from typing import Sequence, Tuple |
|||
|
|||
from .xsor import TensorF32 |
|||
|
|||
|
|||
class Tensor: |
|||
def __init__( |
|||
self, |
|||
data: Sequence[float], |
|||
shape: Sequence[int] | None = None, |
|||
): |
|||
if shape is None: |
|||
shape = (len(data),) |
|||
self._tensor = TensorF32(data, shape) |
|||
|
|||
@classmethod |
|||
def _from_tensor(cls, tensor: TensorF32) -> Tensor: |
|||
output = cls((0,), ()) # type: ignore[arg-type] |
|||
output._tensor = tensor |
|||
return output |
|||
|
|||
@property |
|||
def data(self) -> Sequence[float]: |
|||
return self._tensor.data |
|||
|
|||
@property |
|||
def size(self) -> int: |
|||
return self._tensor.size |
|||
|
|||
@property |
|||
def shape(self) -> Tuple[int, ...]: |
|||
return tuple(self._tensor.shape) |
|||
|
|||
@property |
|||
def ndim(self) -> int: |
|||
return self._tensor.ndim |
|||
|
|||
@property |
|||
def value(self) -> float: |
|||
if self.size != 1: |
|||
raise ValueError("Tensor.value is only available for scalar tensors") |
|||
return self._tensor.value |
|||
|
|||
def __repr__(self) -> str: |
|||
return f"Tensor({self._tensor}, shape={self.shape})" |
|||
|
|||
def __neg__(self) -> Tensor: |
|||
return self._from_tensor(self._tensor.neg()) |
|||
|
|||
def __add__(self, other: int | float | Tensor) -> Tensor: |
|||
if isinstance(other, (int, float)): |
|||
return self._from_tensor(self._tensor.add_scalar(float(other))) |
|||
return self._from_tensor(self._tensor.add_tensor(other._tensor)) |
|||
|
|||
def __radd__(self, other: int | float | Tensor) -> Tensor: |
|||
return self + other |
|||
|
|||
def __sub__(self, other: int | float | Tensor) -> Tensor: |
|||
if isinstance(other, (int, float)): |
|||
return self._from_tensor(self._tensor.sub_scalar(float(other))) |
|||
return self._from_tensor(self._tensor.sub_tensor(other._tensor)) |
|||
|
|||
def __rsub__(self, other: int | float | Tensor) -> Tensor: |
|||
return -(self - other) |
|||
|
|||
def __mul__(self, other: int | float | Tensor) -> Tensor: |
|||
if isinstance(other, (int, float)): |
|||
return self._from_tensor(self._tensor.mul_scalar(float(other))) |
|||
return self._from_tensor(self._tensor.mul_tensor(other._tensor)) |
|||
|
|||
def __rmul__(self, other: int | float | Tensor) -> Tensor: |
|||
return self * other |
|||
|
|||
def __truediv__(self, other: int | float | Tensor) -> Tensor: |
|||
if isinstance(other, (int, float)): |
|||
return self._from_tensor(self._tensor.div_scalar(float(other))) |
|||
return self._from_tensor(self._tensor.div_tensor(other._tensor)) |
|||
|
|||
def __rtruediv__(self, other: int | float | Tensor) -> Tensor: |
|||
raise NotImplementedError("Tensor.__rtruediv__ is not implemented") |
|||
|
|||
def __matmul__(self, other: Tensor) -> Tensor: |
|||
return self._from_tensor(self._tensor.matmul(other._tensor)) |
|||
|
|||
def sum(self) -> Tensor: |
|||
return self._from_tensor(self._tensor.sum()) |
|||
|
|||
def __getitem__(self, index: Sequence[int]) -> Tensor: |
|||
return self._from_tensor(self._tensor.get(index)) |
|||
|
|||
@classmethod |
|||
def zeros(cls, shape: Sequence[int]) -> Tensor: |
|||
return cls._from_tensor(TensorF32.zeros(shape)) |
|||
|
|||
@classmethod |
|||
def ones(cls, shape: Sequence[int]) -> Tensor: |
|||
return cls._from_tensor(TensorF32.ones(shape)) |
|||
|
|||
@classmethod |
|||
def eye(cls, n: int) -> Tensor: |
|||
return cls._from_tensor(TensorF32.eye(n)) |
|||
@ -0,0 +1 @@ |
|||
|
|||
@ -0,0 +1,35 @@ |
|||
from __future__ import annotations |
|||
|
|||
from typing import Sequence |
|||
|
|||
class TensorF32: |
|||
def __init__(self, data: Sequence[float], shape: Sequence[int]) -> None: ... |
|||
def neg(self) -> TensorF32: ... |
|||
def add_tensor(self, other: TensorF32) -> TensorF32: ... |
|||
def sub_tensor(self, other: TensorF32) -> TensorF32: ... |
|||
def mul_tensor(self, other: TensorF32) -> TensorF32: ... |
|||
def div_tensor(self, other: TensorF32) -> TensorF32: ... |
|||
def add_scalar(self, other: float) -> TensorF32: ... |
|||
def sub_scalar(self, other: float) -> TensorF32: ... |
|||
def mul_scalar(self, other: float) -> TensorF32: ... |
|||
def div_scalar(self, other: float) -> TensorF32: ... |
|||
def matmul(self, other: TensorF32) -> TensorF32: ... |
|||
def sum(self) -> TensorF32: ... |
|||
def reshape(self, shape: Sequence[int]) -> TensorF32: ... |
|||
def get(self, index: Sequence[int]) -> TensorF32: ... |
|||
@property |
|||
def data(self) -> Sequence[float]: ... |
|||
@property |
|||
def size(self) -> int: ... |
|||
@property |
|||
def shape(self) -> list[int]: ... |
|||
@property |
|||
def ndim(self) -> int: ... |
|||
@property |
|||
def value(self) -> float: ... |
|||
@staticmethod |
|||
def zeros(shape: Sequence[int]) -> TensorF32: ... |
|||
@staticmethod |
|||
def ones(shape: Sequence[int]) -> TensorF32: ... |
|||
@staticmethod |
|||
def eye(size: int) -> TensorF32: ... |
|||
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