test_rewriting.py 15 KB

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  1. import tempfile
  2. from sympy.core.numbers import pi, Rational
  3. from sympy.core.power import Pow
  4. from sympy.core.singleton import S
  5. from sympy.core.symbol import Symbol
  6. from sympy.functions.elementary.complexes import Abs
  7. from sympy.functions.elementary.exponential import (exp, log)
  8. from sympy.functions.elementary.trigonometric import (cos, sin, sinc)
  9. from sympy.matrices.expressions.matexpr import MatrixSymbol
  10. from sympy.assumptions import assuming, Q
  11. from sympy.external import import_module
  12. from sympy.printing.codeprinter import ccode
  13. from sympy.codegen.matrix_nodes import MatrixSolve
  14. from sympy.codegen.cfunctions import log2, exp2, expm1, log1p
  15. from sympy.codegen.numpy_nodes import logaddexp, logaddexp2
  16. from sympy.codegen.scipy_nodes import cosm1, powm1
  17. from sympy.codegen.rewriting import (
  18. optimize, cosm1_opt, log2_opt, exp2_opt, expm1_opt, log1p_opt, powm1_opt, optims_c99,
  19. create_expand_pow_optimization, matinv_opt, logaddexp_opt, logaddexp2_opt,
  20. optims_numpy, optims_scipy, sinc_opts, FuncMinusOneOptim
  21. )
  22. from sympy.testing.pytest import XFAIL, skip
  23. from sympy.utilities import lambdify
  24. from sympy.utilities._compilation import compile_link_import_strings, has_c
  25. from sympy.utilities._compilation.util import may_xfail
  26. cython = import_module('cython')
  27. numpy = import_module('numpy')
  28. scipy = import_module('scipy')
  29. def test_log2_opt():
  30. x = Symbol('x')
  31. expr1 = 7*log(3*x + 5)/(log(2))
  32. opt1 = optimize(expr1, [log2_opt])
  33. assert opt1 == 7*log2(3*x + 5)
  34. assert opt1.rewrite(log) == expr1
  35. expr2 = 3*log(5*x + 7)/(13*log(2))
  36. opt2 = optimize(expr2, [log2_opt])
  37. assert opt2 == 3*log2(5*x + 7)/13
  38. assert opt2.rewrite(log) == expr2
  39. expr3 = log(x)/log(2)
  40. opt3 = optimize(expr3, [log2_opt])
  41. assert opt3 == log2(x)
  42. assert opt3.rewrite(log) == expr3
  43. expr4 = log(x)/log(2) + log(x+1)
  44. opt4 = optimize(expr4, [log2_opt])
  45. assert opt4 == log2(x) + log(2)*log2(x+1)
  46. assert opt4.rewrite(log) == expr4
  47. expr5 = log(17)
  48. opt5 = optimize(expr5, [log2_opt])
  49. assert opt5 == expr5
  50. expr6 = log(x + 3)/log(2)
  51. opt6 = optimize(expr6, [log2_opt])
  52. assert str(opt6) == 'log2(x + 3)'
  53. assert opt6.rewrite(log) == expr6
  54. def test_exp2_opt():
  55. x = Symbol('x')
  56. expr1 = 1 + 2**x
  57. opt1 = optimize(expr1, [exp2_opt])
  58. assert opt1 == 1 + exp2(x)
  59. assert opt1.rewrite(Pow) == expr1
  60. expr2 = 1 + 3**x
  61. assert expr2 == optimize(expr2, [exp2_opt])
  62. def test_expm1_opt():
  63. x = Symbol('x')
  64. expr1 = exp(x) - 1
  65. opt1 = optimize(expr1, [expm1_opt])
  66. assert expm1(x) - opt1 == 0
  67. assert opt1.rewrite(exp) == expr1
  68. expr2 = 3*exp(x) - 3
  69. opt2 = optimize(expr2, [expm1_opt])
  70. assert 3*expm1(x) == opt2
  71. assert opt2.rewrite(exp) == expr2
  72. expr3 = 3*exp(x) - 5
  73. opt3 = optimize(expr3, [expm1_opt])
  74. assert 3*expm1(x) - 2 == opt3
  75. assert opt3.rewrite(exp) == expr3
  76. expm1_opt_non_opportunistic = FuncMinusOneOptim(exp, expm1, opportunistic=False)
  77. assert expr3 == optimize(expr3, [expm1_opt_non_opportunistic])
  78. assert opt1 == optimize(expr1, [expm1_opt_non_opportunistic])
  79. assert opt2 == optimize(expr2, [expm1_opt_non_opportunistic])
  80. expr4 = 3*exp(x) + log(x) - 3
  81. opt4 = optimize(expr4, [expm1_opt])
  82. assert 3*expm1(x) + log(x) == opt4
  83. assert opt4.rewrite(exp) == expr4
  84. expr5 = 3*exp(2*x) - 3
  85. opt5 = optimize(expr5, [expm1_opt])
  86. assert 3*expm1(2*x) == opt5
  87. assert opt5.rewrite(exp) == expr5
  88. expr6 = (2*exp(x) + 1)/(exp(x) + 1) + 1
  89. opt6 = optimize(expr6, [expm1_opt])
  90. assert opt6.count_ops() <= expr6.count_ops()
  91. def ev(e):
  92. return e.subs(x, 3).evalf()
  93. assert abs(ev(expr6) - ev(opt6)) < 1e-15
  94. y = Symbol('y')
  95. expr7 = (2*exp(x) - 1)/(1 - exp(y)) - 1/(1-exp(y))
  96. opt7 = optimize(expr7, [expm1_opt])
  97. assert -2*expm1(x)/expm1(y) == opt7
  98. assert (opt7.rewrite(exp) - expr7).factor() == 0
  99. expr8 = (1+exp(x))**2 - 4
  100. opt8 = optimize(expr8, [expm1_opt])
  101. tgt8a = (exp(x) + 3)*expm1(x)
  102. tgt8b = 2*expm1(x) + expm1(2*x)
  103. # Both tgt8a & tgt8b seem to give full precision (~16 digits for double)
  104. # for x=1e-7 (compare with expr8 which only achieves ~8 significant digits).
  105. # If we can show that either tgt8a or tgt8b is preferable, we can
  106. # change this test to ensure the preferable version is returned.
  107. assert (tgt8a - tgt8b).rewrite(exp).factor() == 0
  108. assert opt8 in (tgt8a, tgt8b)
  109. assert (opt8.rewrite(exp) - expr8).factor() == 0
  110. expr9 = sin(expr8)
  111. opt9 = optimize(expr9, [expm1_opt])
  112. tgt9a = sin(tgt8a)
  113. tgt9b = sin(tgt8b)
  114. assert opt9 in (tgt9a, tgt9b)
  115. assert (opt9.rewrite(exp) - expr9.rewrite(exp)).factor().is_zero
  116. def test_expm1_two_exp_terms():
  117. x, y = map(Symbol, 'x y'.split())
  118. expr1 = exp(x) + exp(y) - 2
  119. opt1 = optimize(expr1, [expm1_opt])
  120. assert opt1 == expm1(x) + expm1(y)
  121. def test_cosm1_opt():
  122. x = Symbol('x')
  123. expr1 = cos(x) - 1
  124. opt1 = optimize(expr1, [cosm1_opt])
  125. assert cosm1(x) - opt1 == 0
  126. assert opt1.rewrite(cos) == expr1
  127. expr2 = 3*cos(x) - 3
  128. opt2 = optimize(expr2, [cosm1_opt])
  129. assert 3*cosm1(x) == opt2
  130. assert opt2.rewrite(cos) == expr2
  131. expr3 = 3*cos(x) - 5
  132. opt3 = optimize(expr3, [cosm1_opt])
  133. assert 3*cosm1(x) - 2 == opt3
  134. assert opt3.rewrite(cos) == expr3
  135. cosm1_opt_non_opportunistic = FuncMinusOneOptim(cos, cosm1, opportunistic=False)
  136. assert expr3 == optimize(expr3, [cosm1_opt_non_opportunistic])
  137. assert opt1 == optimize(expr1, [cosm1_opt_non_opportunistic])
  138. assert opt2 == optimize(expr2, [cosm1_opt_non_opportunistic])
  139. expr4 = 3*cos(x) + log(x) - 3
  140. opt4 = optimize(expr4, [cosm1_opt])
  141. assert 3*cosm1(x) + log(x) == opt4
  142. assert opt4.rewrite(cos) == expr4
  143. expr5 = 3*cos(2*x) - 3
  144. opt5 = optimize(expr5, [cosm1_opt])
  145. assert 3*cosm1(2*x) == opt5
  146. assert opt5.rewrite(cos) == expr5
  147. expr6 = 2 - 2*cos(x)
  148. opt6 = optimize(expr6, [cosm1_opt])
  149. assert -2*cosm1(x) == opt6
  150. assert opt6.rewrite(cos) == expr6
  151. def test_cosm1_two_cos_terms():
  152. x, y = map(Symbol, 'x y'.split())
  153. expr1 = cos(x) + cos(y) - 2
  154. opt1 = optimize(expr1, [cosm1_opt])
  155. assert opt1 == cosm1(x) + cosm1(y)
  156. def test_expm1_cosm1_mixed():
  157. x = Symbol('x')
  158. expr1 = exp(x) + cos(x) - 2
  159. opt1 = optimize(expr1, [expm1_opt, cosm1_opt])
  160. assert opt1 == cosm1(x) + expm1(x)
  161. def _check_num_lambdify(expr, opt, val_subs, approx_ref, lambdify_kw=None, poorness=1e10):
  162. """ poorness=1e10 signifies that `expr` loses precision of at least ten decimal digits. """
  163. num_ref = expr.subs(val_subs).evalf()
  164. eps = numpy.finfo(numpy.float64).eps
  165. assert abs(num_ref - approx_ref) < approx_ref*eps
  166. f1 = lambdify(list(val_subs.keys()), opt, **(lambdify_kw or {}))
  167. args_float = tuple(map(float, val_subs.values()))
  168. num_err1 = abs(f1(*args_float) - approx_ref)
  169. assert num_err1 < abs(num_ref*eps)
  170. f2 = lambdify(list(val_subs.keys()), expr, **(lambdify_kw or {}))
  171. num_err2 = abs(f2(*args_float) - approx_ref)
  172. assert num_err2 > abs(num_ref*eps*poorness) # this only ensures that the *test* works as intended
  173. def test_cosm1_apart():
  174. x = Symbol('x')
  175. expr1 = 1/cos(x) - 1
  176. opt1 = optimize(expr1, [cosm1_opt])
  177. assert opt1 == -cosm1(x)/cos(x)
  178. if scipy:
  179. _check_num_lambdify(expr1, opt1, {x: S(10)**-30}, 5e-61, lambdify_kw={"modules": 'scipy'})
  180. expr2 = 2/cos(x) - 2
  181. opt2 = optimize(expr2, optims_scipy)
  182. assert opt2 == -2*cosm1(x)/cos(x)
  183. if scipy:
  184. _check_num_lambdify(expr2, opt2, {x: S(10)**-30}, 1e-60, lambdify_kw={"modules": 'scipy'})
  185. expr3 = pi/cos(3*x) - pi
  186. opt3 = optimize(expr3, [cosm1_opt])
  187. assert opt3 == -pi*cosm1(3*x)/cos(3*x)
  188. if scipy:
  189. _check_num_lambdify(expr3, opt3, {x: S(10)**-30/3}, float(5e-61*pi), lambdify_kw={"modules": 'scipy'})
  190. def test_powm1():
  191. args = x, y = map(Symbol, "xy")
  192. expr1 = x**y - 1
  193. opt1 = optimize(expr1, [powm1_opt])
  194. assert opt1 == powm1(x, y)
  195. for arg in args:
  196. assert expr1.diff(arg) == opt1.diff(arg)
  197. if scipy and tuple(map(int, scipy.version.version.split('.')[:3])) >= (1, 10, 0):
  198. subs1_a = {x: Rational(*(1.0+1e-13).as_integer_ratio()), y: pi}
  199. ref1_f64_a = 3.139081648208105e-13
  200. _check_num_lambdify(expr1, opt1, subs1_a, ref1_f64_a, lambdify_kw={"modules": 'scipy'}, poorness=10**11)
  201. subs1_b = {x: pi, y: Rational(*(1e-10).as_integer_ratio())}
  202. ref1_f64_b = 1.1447298859149205e-10
  203. _check_num_lambdify(expr1, opt1, subs1_b, ref1_f64_b, lambdify_kw={"modules": 'scipy'}, poorness=10**9)
  204. def test_log1p_opt():
  205. x = Symbol('x')
  206. expr1 = log(x + 1)
  207. opt1 = optimize(expr1, [log1p_opt])
  208. assert log1p(x) - opt1 == 0
  209. assert opt1.rewrite(log) == expr1
  210. expr2 = log(3*x + 3)
  211. opt2 = optimize(expr2, [log1p_opt])
  212. assert log1p(x) + log(3) == opt2
  213. assert (opt2.rewrite(log) - expr2).simplify() == 0
  214. expr3 = log(2*x + 1)
  215. opt3 = optimize(expr3, [log1p_opt])
  216. assert log1p(2*x) - opt3 == 0
  217. assert opt3.rewrite(log) == expr3
  218. expr4 = log(x+3)
  219. opt4 = optimize(expr4, [log1p_opt])
  220. assert str(opt4) == 'log(x + 3)'
  221. def test_optims_c99():
  222. x = Symbol('x')
  223. expr1 = 2**x + log(x)/log(2) + log(x + 1) + exp(x) - 1
  224. opt1 = optimize(expr1, optims_c99).simplify()
  225. assert opt1 == exp2(x) + log2(x) + log1p(x) + expm1(x)
  226. assert opt1.rewrite(exp).rewrite(log).rewrite(Pow) == expr1
  227. expr2 = log(x)/log(2) + log(x + 1)
  228. opt2 = optimize(expr2, optims_c99)
  229. assert opt2 == log2(x) + log1p(x)
  230. assert opt2.rewrite(log) == expr2
  231. expr3 = log(x)/log(2) + log(17*x + 17)
  232. opt3 = optimize(expr3, optims_c99)
  233. delta3 = opt3 - (log2(x) + log(17) + log1p(x))
  234. assert delta3 == 0
  235. assert (opt3.rewrite(log) - expr3).simplify() == 0
  236. expr4 = 2**x + 3*log(5*x + 7)/(13*log(2)) + 11*exp(x) - 11 + log(17*x + 17)
  237. opt4 = optimize(expr4, optims_c99).simplify()
  238. delta4 = opt4 - (exp2(x) + 3*log2(5*x + 7)/13 + 11*expm1(x) + log(17) + log1p(x))
  239. assert delta4 == 0
  240. assert (opt4.rewrite(exp).rewrite(log).rewrite(Pow) - expr4).simplify() == 0
  241. expr5 = 3*exp(2*x) - 3
  242. opt5 = optimize(expr5, optims_c99)
  243. delta5 = opt5 - 3*expm1(2*x)
  244. assert delta5 == 0
  245. assert opt5.rewrite(exp) == expr5
  246. expr6 = exp(2*x) - 3
  247. opt6 = optimize(expr6, optims_c99)
  248. assert opt6 in (expm1(2*x) - 2, expr6) # expm1(2*x) - 2 is not better or worse
  249. expr7 = log(3*x + 3)
  250. opt7 = optimize(expr7, optims_c99)
  251. delta7 = opt7 - (log(3) + log1p(x))
  252. assert delta7 == 0
  253. assert (opt7.rewrite(log) - expr7).simplify() == 0
  254. expr8 = log(2*x + 3)
  255. opt8 = optimize(expr8, optims_c99)
  256. assert opt8 == expr8
  257. def test_create_expand_pow_optimization():
  258. cc = lambda x: ccode(
  259. optimize(x, [create_expand_pow_optimization(4)]))
  260. x = Symbol('x')
  261. assert cc(x**4) == 'x*x*x*x'
  262. assert cc(x**4 + x**2) == 'x*x + x*x*x*x'
  263. assert cc(x**5 + x**4) == 'pow(x, 5) + x*x*x*x'
  264. assert cc(sin(x)**4) == 'pow(sin(x), 4)'
  265. # gh issue 15335
  266. assert cc(x**(-4)) == '1.0/(x*x*x*x)'
  267. assert cc(x**(-5)) == 'pow(x, -5)'
  268. assert cc(-x**4) == '-(x*x*x*x)'
  269. assert cc(x**4 - x**2) == '-(x*x) + x*x*x*x'
  270. i = Symbol('i', integer=True)
  271. assert cc(x**i - x**2) == 'pow(x, i) - (x*x)'
  272. y = Symbol('y', real=True)
  273. assert cc(Abs(exp(y**4))) == "exp(y*y*y*y)"
  274. # gh issue 20753
  275. cc2 = lambda x: ccode(optimize(x, [create_expand_pow_optimization(
  276. 4, base_req=lambda b: b.is_Function)]))
  277. assert cc2(x**3 + sin(x)**3) == "pow(x, 3) + sin(x)*sin(x)*sin(x)"
  278. def test_matsolve():
  279. n = Symbol('n', integer=True)
  280. A = MatrixSymbol('A', n, n)
  281. x = MatrixSymbol('x', n, 1)
  282. with assuming(Q.fullrank(A)):
  283. assert optimize(A**(-1) * x, [matinv_opt]) == MatrixSolve(A, x)
  284. assert optimize(A**(-1) * x + x, [matinv_opt]) == MatrixSolve(A, x) + x
  285. def test_logaddexp_opt():
  286. x, y = map(Symbol, 'x y'.split())
  287. expr1 = log(exp(x) + exp(y))
  288. opt1 = optimize(expr1, [logaddexp_opt])
  289. assert logaddexp(x, y) - opt1 == 0
  290. assert logaddexp(y, x) - opt1 == 0
  291. assert opt1.rewrite(log) == expr1
  292. def test_logaddexp2_opt():
  293. x, y = map(Symbol, 'x y'.split())
  294. expr1 = log(2**x + 2**y)/log(2)
  295. opt1 = optimize(expr1, [logaddexp2_opt])
  296. assert logaddexp2(x, y) - opt1 == 0
  297. assert logaddexp2(y, x) - opt1 == 0
  298. assert opt1.rewrite(log) == expr1
  299. def test_sinc_opts():
  300. def check(d):
  301. for k, v in d.items():
  302. assert optimize(k, sinc_opts) == v
  303. x = Symbol('x')
  304. check({
  305. sin(x)/x : sinc(x),
  306. sin(2*x)/(2*x) : sinc(2*x),
  307. sin(3*x)/x : 3*sinc(3*x),
  308. x*sin(x) : x*sin(x)
  309. })
  310. y = Symbol('y')
  311. check({
  312. sin(x*y)/(x*y) : sinc(x*y),
  313. y*sin(x/y)/x : sinc(x/y),
  314. sin(sin(x))/sin(x) : sinc(sin(x)),
  315. sin(3*sin(x))/sin(x) : 3*sinc(3*sin(x)),
  316. sin(x)/y : sin(x)/y
  317. })
  318. def test_optims_numpy():
  319. def check(d):
  320. for k, v in d.items():
  321. assert optimize(k, optims_numpy) == v
  322. x = Symbol('x')
  323. check({
  324. sin(2*x)/(2*x) + exp(2*x) - 1: sinc(2*x) + expm1(2*x),
  325. log(x+3)/log(2) + log(x**2 + 1): log1p(x**2) + log2(x+3)
  326. })
  327. @XFAIL # room for improvement, ideally this test case should pass.
  328. def test_optims_numpy_TODO():
  329. def check(d):
  330. for k, v in d.items():
  331. assert optimize(k, optims_numpy) == v
  332. x, y = map(Symbol, 'x y'.split())
  333. check({
  334. log(x*y)*sin(x*y)*log(x*y+1)/(log(2)*x*y): log2(x*y)*sinc(x*y)*log1p(x*y),
  335. exp(x*sin(y)/y) - 1: expm1(x*sinc(y))
  336. })
  337. @may_xfail
  338. def test_compiled_ccode_with_rewriting():
  339. if not cython:
  340. skip("cython not installed.")
  341. if not has_c():
  342. skip("No C compiler found.")
  343. x = Symbol('x')
  344. about_two = 2**(58/S(117))*3**(97/S(117))*5**(4/S(39))*7**(92/S(117))/S(30)*pi
  345. # about_two: 1.999999999999581826
  346. unchanged = 2*exp(x) - about_two
  347. xval = S(10)**-11
  348. ref = unchanged.subs(x, xval).n(19) # 2.0418173913673213e-11
  349. rewritten = optimize(2*exp(x) - about_two, [expm1_opt])
  350. # Unfortunately, we need to call ``.n()`` on our expressions before we hand them
  351. # to ``ccode``, and we need to request a large number of significant digits.
  352. # In this test, results converged for double precision when the following number
  353. # of significant digits were chosen:
  354. NUMBER_OF_DIGITS = 25 # TODO: this should ideally be automatically handled.
  355. func_c = '''
  356. #include <math.h>
  357. double func_unchanged(double x) {
  358. return %(unchanged)s;
  359. }
  360. double func_rewritten(double x) {
  361. return %(rewritten)s;
  362. }
  363. ''' % {"unchanged": ccode(unchanged.n(NUMBER_OF_DIGITS)),
  364. "rewritten": ccode(rewritten.n(NUMBER_OF_DIGITS))}
  365. func_pyx = '''
  366. #cython: language_level=3
  367. cdef extern double func_unchanged(double)
  368. cdef extern double func_rewritten(double)
  369. def py_unchanged(x):
  370. return func_unchanged(x)
  371. def py_rewritten(x):
  372. return func_rewritten(x)
  373. '''
  374. with tempfile.TemporaryDirectory() as folder:
  375. mod, info = compile_link_import_strings(
  376. [('func.c', func_c), ('_func.pyx', func_pyx)],
  377. build_dir=folder, compile_kwargs={"std": 'c99'}
  378. )
  379. err_rewritten = abs(mod.py_rewritten(1e-11) - ref)
  380. err_unchanged = abs(mod.py_unchanged(1e-11) - ref)
  381. assert 1e-27 < err_rewritten < 1e-25 # highly accurate.
  382. assert 1e-19 < err_unchanged < 1e-16 # quite poor.
  383. # Tolerances used above were determined as follows:
  384. # >>> no_opt = unchanged.subs(x, xval.evalf()).evalf()
  385. # >>> with_opt = rewritten.n(25).subs(x, 1e-11).evalf()
  386. # >>> with_opt - ref, no_opt - ref
  387. # (1.1536301877952077e-26, 1.6547074214222335e-18)