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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>
95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
# Copyright © 2023 Apple Inc.
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import itertools
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import math
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import unittest
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import mlx.core as mx
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import mlx_tests
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import numpy as np
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class TestLinalg(mlx_tests.MLXTestCase):
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def test_norm(self):
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vector_ords = [None, 0.5, 0, 1, 2, 3, -1, float("inf"), -float("inf")]
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matrix_ords = [None, "fro", -1, 1, float("inf"), -float("inf")]
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for shape in [(3,), (2, 3), (2, 3, 3)]:
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x_mx = mx.arange(1, math.prod(shape) + 1).reshape(shape)
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x_np = np.arange(1, math.prod(shape) + 1).reshape(shape)
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# Test when at least one axis is provided
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for num_axes in range(1, len(shape)):
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if num_axes == 1:
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ords = vector_ords
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else:
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ords = matrix_ords
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for axis in itertools.combinations(range(len(shape)), num_axes):
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for keepdims in [True, False]:
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for o in ords:
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out_np = np.linalg.norm(
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x_np, ord=o, axis=axis, keepdims=keepdims
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)
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out_mx = mx.linalg.norm(
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x_mx, ord=o, axis=axis, keepdims=keepdims
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)
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with self.subTest(
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shape=shape, ord=o, axis=axis, keepdims=keepdims
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):
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self.assertTrue(
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np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6)
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)
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# Test only ord provided
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for shape in [(3,), (2, 3)]:
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x_mx = mx.arange(1, math.prod(shape) + 1).reshape(shape)
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x_np = np.arange(1, math.prod(shape) + 1).reshape(shape)
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for o in [None, 1, -1, float("inf"), -float("inf")]:
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for keepdims in [True, False]:
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out_np = np.linalg.norm(x_np, ord=o, keepdims=keepdims)
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out_mx = mx.linalg.norm(x_mx, ord=o, keepdims=keepdims)
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with self.subTest(shape=shape, ord=o, keepdims=keepdims):
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self.assertTrue(
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np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6)
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)
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# Test no ord and no axis provided
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for shape in [(3,), (2, 3), (2, 3, 3)]:
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x_mx = mx.arange(1, math.prod(shape) + 1).reshape(shape)
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x_np = np.arange(1, math.prod(shape) + 1).reshape(shape)
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for keepdims in [True, False]:
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out_np = np.linalg.norm(x_np, keepdims=keepdims)
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out_mx = mx.linalg.norm(x_mx, keepdims=keepdims)
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with self.subTest(shape=shape, keepdims=keepdims):
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self.assertTrue(np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6))
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def test_complex_norm(self):
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for shape in [(3,), (2, 3), (2, 3, 3)]:
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x_np = np.random.uniform(size=shape).astype(
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np.float32
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) + 1j * np.random.uniform(size=shape).astype(np.float32)
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x_mx = mx.array(x_np)
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out_np = np.linalg.norm(x_np)
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out_mx = mx.linalg.norm(x_mx)
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with self.subTest(shape=shape):
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self.assertTrue(np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6))
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for num_axes in range(1, len(shape)):
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for axis in itertools.combinations(range(len(shape)), num_axes):
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out_np = np.linalg.norm(x_np, axis=axis)
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out_mx = mx.linalg.norm(x_mx, axis=axis)
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with self.subTest(shape=shape, axis=axis):
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self.assertTrue(
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np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6)
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)
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x_np = np.random.uniform(size=(4, 4)).astype(
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np.float32
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) + 1j * np.random.uniform(size=(4, 4)).astype(np.float32)
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x_mx = mx.array(x_np)
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out_np = np.linalg.norm(x_np, ord="fro")
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out_mx = mx.linalg.norm(x_mx, ord="fro")
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self.assertTrue(np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6))
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if __name__ == "__main__":
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unittest.main()
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