# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed 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. from __future__ import print_function from op_test import OpTest import unittest import itertools import numpy as np import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core import scipy import scipy.linalg import copy def scipy_lu_unpack(A): shape = A.shape if len(shape) == 2: return scipy.linalg.lu(A) else: preshape = shape[:-2] batchsize = np.product(shape) // (shape[-2] * shape[-1]) Plst = [] Llst = [] Ulst = [] NA = A.reshape((-1, shape[-2], shape[-1])) for b in range(batchsize): As = NA[b] P, L, U = scipy.linalg.lu(As) pshape = P.shape lshape = L.shape ushape = U.shape Plst.append(P) Llst.append(L) Ulst.append(U) return np.array(Plst).reshape(preshape + pshape), np.array( Llst).reshape(preshape + lshape), np.array(Ulst).reshape(preshape + ushape) def Pmat_to_perm(Pmat_org, cut): Pmat = copy.deepcopy(Pmat_org) shape = Pmat.shape rows = shape[-2] cols = shape[-1] batchsize = max(1, np.product(shape[:-2])) P = Pmat.reshape(batchsize, rows, cols) permmat = [] for b in range(batchsize): permlst = [] sP = P[b] for c in range(min(rows, cols)): idx = np.argmax(sP[:, c]) permlst.append(idx) tmp = copy.deepcopy(sP[c, :]) sP[c, :] = sP[idx, :] sP[idx, :] = tmp permmat.append(permlst) Pivot = np.array(permmat).reshape(list(shape[:-2]) + [rows, ]) + 1 return Pivot[..., :cut] def perm_to_Pmat(perm, dim): pshape = perm.shape bs = int(np.product(perm.shape[:-1]).item()) perm = perm.reshape((bs, pshape[-1])) oneslst = [] for i in range(bs): idlst = np.arange(dim) perm_item = perm[i, :] for idx, p in enumerate(perm_item - 1): temp = idlst[idx] idlst[idx] = idlst[p] idlst[p] = temp ones = paddle.eye(dim) nmat = paddle.scatter(ones, paddle.to_tensor(idlst), ones) oneslst.append(nmat) return np.array(oneslst).reshape(list(pshape[:-1]) + [dim, dim]) # m > n class TestLU_UnpackOp(OpTest): """ case 1 """ def config(self): self.x_shape = [2, 12, 10] self.unpack_ludata = True self.unpack_pivots = True self.dtype = "float64" def set_output(self, A): sP, sL, sU = scipy_lu_unpack(A) self.L = sL self.U = sU self.P = sP def setUp(self): self.op_type = "lu_unpack" self.config() x = np.random.random(self.x_shape).astype(self.dtype) if paddle.in_dynamic_mode(): xt = paddle.to_tensor(x) lu, pivots = paddle.linalg.lu(xt) lu = lu.numpy() pivots = pivots.numpy() else: with fluid.program_guard(fluid.Program(), fluid.Program()): place = fluid.CPUPlace() if core.is_compiled_with_cuda(): place = fluid.CUDAPlace(0) xv = paddle.fluid.data( name="input", shape=self.x_shape, dtype=self.dtype) lu, p = paddle.linalg.lu(xv) exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={"input": x}, fetch_list=[lu, p]) lu, pivots = fetches[0], fetches[1] self.inputs = {'X': lu, 'Pivots': pivots} self.attrs = { 'unpack_ludata': self.unpack_ludata, 'unpack_pivots': self.unpack_pivots } self.set_output(x) self.outputs = { 'Pmat': self.P, 'L': self.L, 'U': self.U, } def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], ['L', 'U']) # m = n class TestLU_UnpackOp2(TestLU_UnpackOp): """ case 2 """ def config(self): self.x_shape = [2, 10, 10] self.unpack_ludata = True self.unpack_pivots = True self.dtype = "float64" # m < n class TestLU_UnpackOp3(TestLU_UnpackOp): """ case 3 """ def config(self): self.x_shape = [2, 10, 12] self.unpack_ludata = True self.unpack_pivots = True self.dtype = "float64" class TestLU_UnpackAPI(unittest.TestCase): def test_dygraph(self): def run_lu_unpack_dygraph(shape, dtype): if dtype == "float32": np_dtype = np.float32 elif dtype == "float64": np_dtype = np.float64 a = np.random.rand(*shape).astype(np_dtype) m = a.shape[-2] n = a.shape[-1] min_mn = min(m, n) places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: paddle.disable_static(place) x = paddle.to_tensor(a, dtype=dtype) sP, sL, sU = scipy_lu_unpack(a) LU, P = paddle.linalg.lu(x) pP, pL, pU = paddle.linalg.lu_unpack(LU, P) self.assertTrue(np.allclose(sU, pU, atol=1e-5)) self.assertTrue(np.allclose(sL, pL, atol=1e-5)) self.assertTrue(np.allclose(sP, pP, atol=1e-5)) tensor_shapes = [ (3, 5), (5, 5), (5, 3), # 2-dim Tensors (2, 3, 5), (3, 5, 5), (4, 5, 3), # 3-dim Tensors (2, 5, 3, 5), (3, 5, 5, 5), (4, 5, 5, 3) # 4-dim Tensors ] dtypes = ["float32", "float64"] for tensor_shape, dtype in itertools.product(tensor_shapes, dtypes): run_lu_unpack_dygraph(tensor_shape, dtype) def test_static(self): paddle.enable_static() def run_lu_static(shape, dtype): if dtype == "float32": np_dtype = np.float32 elif dtype == "float64": np_dtype = np.float64 a = np.random.rand(*shape).astype(np_dtype) m = a.shape[-2] n = a.shape[-1] min_mn = min(m, n) places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: with fluid.program_guard(fluid.Program(), fluid.Program()): sP, sL, sU = scipy_lu_unpack(a) x = paddle.fluid.data( name="input", shape=shape, dtype=dtype) lu, p = paddle.linalg.lu(x) pP, pL, pU = paddle.linalg.lu_unpack(lu, p) exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={"input": a}, fetch_list=[pP, pL, pU]) self.assertTrue(np.allclose(fetches[0], sP, atol=1e-5)) self.assertTrue(np.allclose(fetches[1], sL, atol=1e-5)) self.assertTrue(np.allclose(fetches[2], sU, atol=1e-5)) tensor_shapes = [ (3, 5), (5, 5), (5, 3), # 2-dim Tensors (2, 3, 5), (3, 5, 5), (4, 5, 3), # 3-dim Tensors (2, 5, 3, 5), (3, 5, 5, 5), (4, 5, 5, 3) # 4-dim Tensors ] dtypes = ["float32", "float64"] for tensor_shape, dtype in itertools.product(tensor_shapes, dtypes): run_lu_static(tensor_shape, dtype) if __name__ == "__main__": paddle.enable_static() unittest.main()