yzhliu commented on a change in pull request #5618: URL: https://github.com/apache/incubator-tvm/pull/5618#discussion_r446359570
########## File path: tests/python/unittest/test_arith_solve_linear_inequality.py ########## @@ -0,0 +1,187 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +import random +import sys +import pytest +import tvm +from tvm import te, arith, ir, tir, testing + + +def test_solution_consistency(): + seed = random.randrange(sys.maxsize) + print("\nThis test is intentionally non-deterministic, " + "if it fails please report it in github issue together with this seed {}\n".format(seed)) + random.seed(seed) + + def _check(variables, formulas, coef=(-5, 5), bounds=(-20, 20)): + vs = [te.var("x" + str(i)) for i in range(variables)] + + fs = [] + for i in range(formulas): + s1 = sum([v*random.randint(coef[0], coef[1]) for v in vs]) + s1 += random.randint(coef[0], coef[1]) + s2 = sum([v*random.randint(coef[0], coef[1]) for v in vs]) + s2 += random.randint(coef[0], coef[1]) + op = random.choice([tir.expr.EQ, tir.expr.LE, tir.expr.LT, tir.expr.GE, tir.expr.GT]) + fs.append(op(s1, s2)) + + vranges = {v: tvm.ir.expr.Range(bounds[0], bounds[1] + 1) for v in vs} + before = te.all(tir.const(1, 'bool'), *fs) + after = arith._ffi_api.SolveInequalitiesAsCondition(vs, vranges, fs) + after = te.all(tir.const(1, 'bool'), *after) + testing.check_bool_expr_is_true(before == after, vranges) + + solution = arith.solve_linear_inequalities(fs, vs, vranges, deskew_range=True) + testing.check_int_constraints_trans_consistency(solution) + + for i in range(3): + _check(1, 1) + for i in range(3): + _check(1, 2) + + for i in range(3): + _check(2, 1) + for i in range(3): + _check(2, 2) + for i in range(3): + _check(2, 3) + + # Somewhere here coefficients in the results become too large, leading to overflow, + # so we use smaller initial coefficients + for i in range(5): + _check(3, 3, coef=(-2, 2)) + for i in range(5): + _check(3, 4, coef=(-2, 2)) + + for i in range(5): + _check(4, 3, coef=(-1, 1)) + + for i in range(5): + _check(10, 2, coef=(-1, 1), bounds=(0, 4)) + for i in range(5): + _check(10, 3, coef=(0, 1), bounds=(0, 4)) + + +def test_dual_variable(): + x, y = te.var("x"), te.var("y") + + variables = [x, y] + ranges = { + x: tvm.ir.Range(-100, 100), + y: tvm.ir.Range(0, 10), + } + problem = [ + tvm.tir.LE(x + y, 20), + tvm.tir.GE(x - y, 10), + ] + + # solution as conditions + solution = arith._ffi_api.SolveInequalitiesAsCondition(variables, ranges, problem) + assert ir.structural_equal(solution[0], x >= (y + 10)) + assert ir.structural_equal(solution[1], x <= (20 - y)) + assert ir.structural_equal(solution[2], y >= 0) + assert ir.structural_equal(solution[3], y <= 5) + + # solve and get the ranges + solution = arith.solve_linear_inequalities([ + tvm.tir.LE(x + y, 20), + tvm.tir.GE(x - y, 10), + ], [x, y], ranges) + # 0 <= y <=5 + assert solution.ranges[y].min == 0 + assert solution.ranges[y].extent == 6 + # y + 10 <= x <= 20 - y + assert ir.structural_equal(solution.ranges[x].min, y + 10) + assert solution.ranges[x].extent == 11 # max(10 - 2y) + + # deskew the solved ranges to be starting from zero + solution = arith.solve_linear_inequalities(problem, variables, ranges, deskew_range=True) + [x_new, y_new] = solution.dst.variables + [rel] = solution.dst.relations + assert ir.structural_equal(rel, (y_new*2) + x_new <= 10) + assert ir.structural_equal(solution.dst.ranges[x_new].min, 0) + assert ir.structural_equal(solution.dst.ranges[x_new].extent, 11) + assert ir.structural_equal(solution.dst.ranges[y_new].min, 0) + assert ir.structural_equal(solution.dst.ranges[y_new].extent, 6) + assert ir.structural_equal(solution.src_to_dst[x], x_new + (y_new + 10)) + assert ir.structural_equal(solution.src_to_dst[y], y_new) + assert ir.structural_equal(solution.dst_to_src[x_new], x - y - 10) + assert ir.structural_equal(solution.dst_to_src[y_new], y) + + +def test_equal(): + x, y = te.var("x"), te.var("y") + problem = [ + tvm.tir.GE(x + y, 10), + tvm.tir.GE(x - y, 2), + tvm.tir.LE(x, 6), + ] + + solution = arith.solve_linear_inequalities(problem, [x, y]) + assert solution.ranges[x].min == 6 + assert solution.ranges[x].extent == 1 + assert solution.ranges[y].min == 4 + assert solution.ranges[y].extent == 1 + + solution = arith.solve_linear_inequalities(problem, [x, y], deskew_range=True) + assert len(solution.dst.variables) == 0 + assert len(solution.dst.ranges) == 0 + assert len(solution.dst.relations) == 0 + assert solution.src_to_dst[x] == 6 + assert solution.src_to_dst[y] == 4 + + +def test_multi_equal(): + x, y, z = te.var("x"), te.var("y"), te.var("z") + problem = [ + tvm.tir.LE(x, 6), + tvm.tir.GE(x, 6), + tvm.tir.GE(x - z * y, 0), + tvm.tir.LE(x - z * y, 0), + ] + + solution = arith.solve_linear_inequalities(problem, [x, y, z]) + assert solution.ranges[x].min == 6 + assert solution.ranges[x].extent == 1 + + solution = arith.solve_linear_inequalities(problem, [x, y, z], deskew_range=True) + assert solution.src_to_dst[y] == y + assert solution.src_to_dst[z] == z + assert solution.src_to_dst[x] == 6 + + +def test_no_solution(): + x = te.var("x0") + vranges = {x: tvm.ir.Range.make_by_min_extent(-20, 41)} + problem = [-x - 4 <= -5*x + 2, x*4 + 5 <= x*5] Review comment: more cases are covered in `test_solution_consistency` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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