mirror of
https://github.com/ml-explore/mlx.git
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6b0d30bb85
* implemented vector_norm in cpp added linalg to mlx * implemented vector_norm python binding * renamed vector_norm to norm, implemented norm without provided ord * completed the implementation of the norm * added tests * removed unused import in linalg.cpp * updated python bindings * added some tests for python bindings * handling inf, -inf as numpy does, more extensive tests of compatibility with numpy * added better docs and examples * refactored mlx.linalg.norm bindings * reused existing util for implementation of linalg.norm * more tests * fixed a bug with no ord and axis provided * removed unused imports * some style and API consistency updates to linalg norm * remove unused includes * fix python tests * fixed a bug with frobenius norm of a complex-valued matrix * complex for vector too --------- Co-authored-by: Awni Hannun <awni@apple.com>
76 lines
1.8 KiB
ReStructuredText
76 lines
1.8 KiB
ReStructuredText
MLX
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===
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MLX is a NumPy-like array framework designed for efficient and flexible machine
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learning on Apple silicon, brought to you by Apple machine learning research.
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The Python API closely follows NumPy with a few exceptions. MLX also has a
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fully featured C++ API which closely follows the Python API.
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The main differences between MLX and NumPy are:
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- **Composable function transformations**: MLX has composable function
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transformations for automatic differentiation, automatic vectorization,
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and computation graph optimization.
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- **Lazy computation**: Computations in MLX are lazy. Arrays are only
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materialized when needed.
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- **Multi-device**: Operations can run on any of the supported devices (CPU,
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GPU, ...)
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The design of MLX is inspired by frameworks like `PyTorch
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<https://pytorch.org/>`_, `Jax <https://github.com/google/jax>`_, and
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`ArrayFire <https://arrayfire.org/>`_. A noteable difference from these
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frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared
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memory. Operations on MLX arrays can be performed on any of the supported
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device types without performing data copies. Currently supported device types
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are the CPU and GPU.
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.. toctree::
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:caption: Install
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:maxdepth: 1
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install
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.. toctree::
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:caption: Usage
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:maxdepth: 1
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quick_start
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unified_memory
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using_streams
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.. toctree::
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:caption: Examples
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:maxdepth: 1
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examples/linear_regression
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examples/mlp
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examples/llama-inference
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.. toctree::
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:caption: Python API Reference
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:maxdepth: 1
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python/array
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python/devices_and_streams
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python/ops
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python/random
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python/transforms
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python/fft
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python/linalg
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python/nn
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python/optimizers
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python/tree_utils
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.. toctree::
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:caption: C++ API Reference
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:maxdepth: 1
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cpp/ops
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.. toctree::
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:caption: Further Reading
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:maxdepth: 1
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dev/extensions
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