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| from sympy.concrete.summations import Sum | |
| from sympy.core.mod import Mod | |
| from sympy.core.relational import (Equality, Unequality) | |
| from sympy.functions.elementary.miscellaneous import sqrt | |
| from sympy.functions.elementary.piecewise import Piecewise | |
| from sympy.matrices.expressions.blockmatrix import BlockMatrix | |
| from sympy.matrices.expressions.matexpr import MatrixSymbol | |
| from sympy.matrices.expressions.special import Identity | |
| from sympy.utilities.lambdify import lambdify | |
| from sympy.abc import x, i, j, a, b, c, d | |
| from sympy.core import Function, Pow, Symbol | |
| from sympy.codegen.matrix_nodes import MatrixSolve | |
| from sympy.codegen.numpy_nodes import logaddexp, logaddexp2 | |
| from sympy.codegen.cfunctions import log1p, expm1, hypot, log10, exp2, log2, Sqrt | |
| from sympy.tensor.array import Array | |
| from sympy.tensor.array.expressions.array_expressions import ArrayTensorProduct, ArrayAdd, \ | |
| PermuteDims, ArrayDiagonal | |
| from sympy.printing.numpy import JaxPrinter, _jax_known_constants, _jax_known_functions | |
| from sympy.tensor.array.expressions.from_matrix_to_array import convert_matrix_to_array | |
| from sympy.testing.pytest import skip, raises | |
| from sympy.external import import_module | |
| # Unlike NumPy which will aggressively promote operands to double precision, | |
| # jax always uses single precision. Double precision in jax can be | |
| # configured before the call to `import jax`, however this must be explicitly | |
| # configured and is not fully supported. Thus, the tests here have been modified | |
| # from the tests in test_numpy.py, only in the fact that they assert lambdify | |
| # function accuracy to only single precision accuracy. | |
| # https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision | |
| jax = import_module('jax') | |
| if jax: | |
| deafult_float_info = jax.numpy.finfo(jax.numpy.array([]).dtype) | |
| JAX_DEFAULT_EPSILON = deafult_float_info.eps | |
| def test_jax_piecewise_regression(): | |
| """ | |
| NumPyPrinter needs to print Piecewise()'s choicelist as a list to avoid | |
| breaking compatibility with numpy 1.8. This is not necessary in numpy 1.9+. | |
| See gh-9747 and gh-9749 for details. | |
| """ | |
| printer = JaxPrinter() | |
| p = Piecewise((1, x < 0), (0, True)) | |
| assert printer.doprint(p) == \ | |
| 'jax.numpy.select([jax.numpy.less(x, 0),True], [1,0], default=jax.numpy.nan)' | |
| assert printer.module_imports == {'jax.numpy': {'select', 'less', 'nan'}} | |
| def test_jax_logaddexp(): | |
| lae = logaddexp(a, b) | |
| assert JaxPrinter().doprint(lae) == 'jax.numpy.logaddexp(a, b)' | |
| lae2 = logaddexp2(a, b) | |
| assert JaxPrinter().doprint(lae2) == 'jax.numpy.logaddexp2(a, b)' | |
| def test_jax_sum(): | |
| if not jax: | |
| skip("JAX not installed") | |
| s = Sum(x ** i, (i, a, b)) | |
| f = lambdify((a, b, x), s, 'jax') | |
| a_, b_ = 0, 10 | |
| x_ = jax.numpy.linspace(-1, +1, 10) | |
| assert jax.numpy.allclose(f(a_, b_, x_), sum(x_ ** i_ for i_ in range(a_, b_ + 1))) | |
| s = Sum(i * x, (i, a, b)) | |
| f = lambdify((a, b, x), s, 'jax') | |
| a_, b_ = 0, 10 | |
| x_ = jax.numpy.linspace(-1, +1, 10) | |
| assert jax.numpy.allclose(f(a_, b_, x_), sum(i_ * x_ for i_ in range(a_, b_ + 1))) | |
| def test_jax_multiple_sums(): | |
| if not jax: | |
| skip("JAX not installed") | |
| s = Sum((x + j) * i, (i, a, b), (j, c, d)) | |
| f = lambdify((a, b, c, d, x), s, 'jax') | |
| a_, b_ = 0, 10 | |
| c_, d_ = 11, 21 | |
| x_ = jax.numpy.linspace(-1, +1, 10) | |
| assert jax.numpy.allclose(f(a_, b_, c_, d_, x_), | |
| sum((x_ + j_) * i_ for i_ in range(a_, b_ + 1) for j_ in range(c_, d_ + 1))) | |
| def test_jax_codegen_einsum(): | |
| if not jax: | |
| skip("JAX not installed") | |
| M = MatrixSymbol("M", 2, 2) | |
| N = MatrixSymbol("N", 2, 2) | |
| cg = convert_matrix_to_array(M * N) | |
| f = lambdify((M, N), cg, 'jax') | |
| ma = jax.numpy.array([[1, 2], [3, 4]]) | |
| mb = jax.numpy.array([[1,-2], [-1, 3]]) | |
| assert (f(ma, mb) == jax.numpy.matmul(ma, mb)).all() | |
| def test_jax_codegen_extra(): | |
| if not jax: | |
| skip("JAX not installed") | |
| M = MatrixSymbol("M", 2, 2) | |
| N = MatrixSymbol("N", 2, 2) | |
| P = MatrixSymbol("P", 2, 2) | |
| Q = MatrixSymbol("Q", 2, 2) | |
| ma = jax.numpy.array([[1, 2], [3, 4]]) | |
| mb = jax.numpy.array([[1,-2], [-1, 3]]) | |
| mc = jax.numpy.array([[2, 0], [1, 2]]) | |
| md = jax.numpy.array([[1,-1], [4, 7]]) | |
| cg = ArrayTensorProduct(M, N) | |
| f = lambdify((M, N), cg, 'jax') | |
| assert (f(ma, mb) == jax.numpy.einsum(ma, [0, 1], mb, [2, 3])).all() | |
| cg = ArrayAdd(M, N) | |
| f = lambdify((M, N), cg, 'jax') | |
| assert (f(ma, mb) == ma+mb).all() | |
| cg = ArrayAdd(M, N, P) | |
| f = lambdify((M, N, P), cg, 'jax') | |
| assert (f(ma, mb, mc) == ma+mb+mc).all() | |
| cg = ArrayAdd(M, N, P, Q) | |
| f = lambdify((M, N, P, Q), cg, 'jax') | |
| assert (f(ma, mb, mc, md) == ma+mb+mc+md).all() | |
| cg = PermuteDims(M, [1, 0]) | |
| f = lambdify((M,), cg, 'jax') | |
| assert (f(ma) == ma.T).all() | |
| cg = PermuteDims(ArrayTensorProduct(M, N), [1, 2, 3, 0]) | |
| f = lambdify((M, N), cg, 'jax') | |
| assert (f(ma, mb) == jax.numpy.transpose(jax.numpy.einsum(ma, [0, 1], mb, [2, 3]), (1, 2, 3, 0))).all() | |
| cg = ArrayDiagonal(ArrayTensorProduct(M, N), (1, 2)) | |
| f = lambdify((M, N), cg, 'jax') | |
| assert (f(ma, mb) == jax.numpy.diagonal(jax.numpy.einsum(ma, [0, 1], mb, [2, 3]), axis1=1, axis2=2)).all() | |
| def test_jax_relational(): | |
| if not jax: | |
| skip("JAX not installed") | |
| e = Equality(x, 1) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [False, True, False]) | |
| e = Unequality(x, 1) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [True, False, True]) | |
| e = (x < 1) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [True, False, False]) | |
| e = (x <= 1) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [True, True, False]) | |
| e = (x > 1) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [False, False, True]) | |
| e = (x >= 1) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [False, True, True]) | |
| # Multi-condition expressions | |
| e = (x >= 1) & (x < 2) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [False, True, False]) | |
| e = (x >= 1) | (x < 2) | |
| f = lambdify((x,), e, 'jax') | |
| x_ = jax.numpy.array([0, 1, 2]) | |
| assert jax.numpy.array_equal(f(x_), [True, True, True]) | |
| def test_jax_mod(): | |
| if not jax: | |
| skip("JAX not installed") | |
| e = Mod(a, b) | |
| f = lambdify((a, b), e, 'jax') | |
| a_ = jax.numpy.array([0, 1, 2, 3]) | |
| b_ = 2 | |
| assert jax.numpy.array_equal(f(a_, b_), [0, 1, 0, 1]) | |
| a_ = jax.numpy.array([0, 1, 2, 3]) | |
| b_ = jax.numpy.array([2, 2, 2, 2]) | |
| assert jax.numpy.array_equal(f(a_, b_), [0, 1, 0, 1]) | |
| a_ = jax.numpy.array([2, 3, 4, 5]) | |
| b_ = jax.numpy.array([2, 3, 4, 5]) | |
| assert jax.numpy.array_equal(f(a_, b_), [0, 0, 0, 0]) | |
| def test_jax_pow(): | |
| if not jax: | |
| skip('JAX not installed') | |
| expr = Pow(2, -1, evaluate=False) | |
| f = lambdify([], expr, 'jax') | |
| assert f() == 0.5 | |
| def test_jax_expm1(): | |
| if not jax: | |
| skip("JAX not installed") | |
| f = lambdify((a,), expm1(a), 'jax') | |
| assert abs(f(1e-10) - 1e-10 - 5e-21) <= 1e-10 * JAX_DEFAULT_EPSILON | |
| def test_jax_log1p(): | |
| if not jax: | |
| skip("JAX not installed") | |
| f = lambdify((a,), log1p(a), 'jax') | |
| assert abs(f(1e-99) - 1e-99) <= 1e-99 * JAX_DEFAULT_EPSILON | |
| def test_jax_hypot(): | |
| if not jax: | |
| skip("JAX not installed") | |
| assert abs(lambdify((a, b), hypot(a, b), 'jax')(3, 4) - 5) <= JAX_DEFAULT_EPSILON | |
| def test_jax_log10(): | |
| if not jax: | |
| skip("JAX not installed") | |
| assert abs(lambdify((a,), log10(a), 'jax')(100) - 2) <= JAX_DEFAULT_EPSILON | |
| def test_jax_exp2(): | |
| if not jax: | |
| skip("JAX not installed") | |
| assert abs(lambdify((a,), exp2(a), 'jax')(5) - 32) <= JAX_DEFAULT_EPSILON | |
| def test_jax_log2(): | |
| if not jax: | |
| skip("JAX not installed") | |
| assert abs(lambdify((a,), log2(a), 'jax')(256) - 8) <= JAX_DEFAULT_EPSILON | |
| def test_jax_Sqrt(): | |
| if not jax: | |
| skip("JAX not installed") | |
| assert abs(lambdify((a,), Sqrt(a), 'jax')(4) - 2) <= JAX_DEFAULT_EPSILON | |
| def test_jax_sqrt(): | |
| if not jax: | |
| skip("JAX not installed") | |
| assert abs(lambdify((a,), sqrt(a), 'jax')(4) - 2) <= JAX_DEFAULT_EPSILON | |
| def test_jax_matsolve(): | |
| if not jax: | |
| skip("JAX not installed") | |
| M = MatrixSymbol("M", 3, 3) | |
| x = MatrixSymbol("x", 3, 1) | |
| expr = M**(-1) * x + x | |
| matsolve_expr = MatrixSolve(M, x) + x | |
| f = lambdify((M, x), expr, 'jax') | |
| f_matsolve = lambdify((M, x), matsolve_expr, 'jax') | |
| m0 = jax.numpy.array([[1, 2, 3], [3, 2, 5], [5, 6, 7]]) | |
| assert jax.numpy.linalg.matrix_rank(m0) == 3 | |
| x0 = jax.numpy.array([3, 4, 5]) | |
| assert jax.numpy.allclose(f_matsolve(m0, x0), f(m0, x0)) | |
| def test_16857(): | |
| if not jax: | |
| skip("JAX not installed") | |
| a_1 = MatrixSymbol('a_1', 10, 3) | |
| a_2 = MatrixSymbol('a_2', 10, 3) | |
| a_3 = MatrixSymbol('a_3', 10, 3) | |
| a_4 = MatrixSymbol('a_4', 10, 3) | |
| A = BlockMatrix([[a_1, a_2], [a_3, a_4]]) | |
| assert A.shape == (20, 6) | |
| printer = JaxPrinter() | |
| assert printer.doprint(A) == 'jax.numpy.block([[a_1, a_2], [a_3, a_4]])' | |
| def test_issue_17006(): | |
| if not jax: | |
| skip("JAX not installed") | |
| M = MatrixSymbol("M", 2, 2) | |
| f = lambdify(M, M + Identity(2), 'jax') | |
| ma = jax.numpy.array([[1, 2], [3, 4]]) | |
| mr = jax.numpy.array([[2, 2], [3, 5]]) | |
| assert (f(ma) == mr).all() | |
| from sympy.core.symbol import symbols | |
| n = symbols('n', integer=True) | |
| N = MatrixSymbol("M", n, n) | |
| raises(NotImplementedError, lambda: lambdify(N, N + Identity(n), 'jax')) | |
| def test_jax_array(): | |
| assert JaxPrinter().doprint(Array(((1, 2), (3, 5)))) == 'jax.numpy.array([[1, 2], [3, 5]])' | |
| assert JaxPrinter().doprint(Array((1, 2))) == 'jax.numpy.array((1, 2))' | |
| def test_jax_known_funcs_consts(): | |
| assert _jax_known_constants['NaN'] == 'jax.numpy.nan' | |
| assert _jax_known_constants['EulerGamma'] == 'jax.numpy.euler_gamma' | |
| assert _jax_known_functions['acos'] == 'jax.numpy.arccos' | |
| assert _jax_known_functions['log'] == 'jax.numpy.log' | |
| def test_jax_print_methods(): | |
| prntr = JaxPrinter() | |
| assert hasattr(prntr, '_print_acos') | |
| assert hasattr(prntr, '_print_log') | |
| def test_jax_printmethod(): | |
| printer = JaxPrinter() | |
| assert hasattr(printer, 'printmethod') | |
| assert printer.printmethod == '_jaxcode' | |
| def test_jax_custom_print_method(): | |
| class expm1(Function): | |
| def _jaxcode(self, printer): | |
| x, = self.args | |
| function = f'expm1({printer._print(x)})' | |
| return printer._module_format(printer._module + '.' + function) | |
| printer = JaxPrinter() | |
| assert printer.doprint(expm1(Symbol('x'))) == 'jax.numpy.expm1(x)' | |