wehub-resource-sync
432 lines
13 KiB
Python
432 lines
13 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import itertools
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import unittest
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import numpy as np
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import scipy
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import scipy.linalg
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from op_test import OpTest, get_device_place, get_places, is_custom_device
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import paddle
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from paddle import base
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from paddle.base import core
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def scipy_lu(A, pivot):
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shape = A.shape
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if len(shape) == 2:
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return scipy.linalg.lu(A, permute_l=not pivot)
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else:
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preshape = shape[:-2]
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batchsize = np.prod(shape) // (shape[-2] * shape[-1])
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PP = []
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PL = []
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PU = []
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NA = A.reshape((-1, shape[-2], shape[-1]))
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for b in range(batchsize):
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P, L, U = scipy.linalg.lu(NA[b], permute_l=not pivot)
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pshape = P.shape
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lshape = L.shape
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ushape = U.shape
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PP.append(P)
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PL.append(L)
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PU.append(U)
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return (
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np.array(PP).reshape(preshape + pshape),
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np.array(PL).reshape(preshape + lshape),
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np.array(PU).reshape(preshape + ushape),
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)
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def Pmat_to_perm(Pmat_org, cut):
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Pmat = copy.deepcopy(Pmat_org)
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shape = Pmat.shape
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rows = shape[-2]
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cols = shape[-1]
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batchsize = max(1, np.prod(shape[:-2]))
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P = Pmat.reshape(batchsize, rows, cols)
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permmat = []
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for b in range(batchsize):
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permlst = []
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sP = P[b]
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for c in range(min(rows, cols)):
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idx = np.argmax(sP[:, c])
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permlst.append(idx)
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tmp = copy.deepcopy(sP[c, :])
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sP[c, :] = sP[idx, :]
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sP[idx, :] = tmp
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permmat.append(permlst)
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Pivot = (
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np.array(permmat).reshape(
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[
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*shape[:-2],
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rows,
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]
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)
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+ 1
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)
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return Pivot[..., :cut]
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def perm_to_Pmat(perm, dim):
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pshape = perm.shape
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bs = int(np.prod(perm.shape[:-1]).item())
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perm = perm.reshape((bs, pshape[-1]))
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oneslst = []
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for i in range(bs):
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idlst = np.arange(dim)
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perm_item = perm[i, :]
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for idx, p in enumerate(perm_item - 1):
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temp = idlst[idx]
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idlst[idx] = idlst[p]
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idlst[p] = temp
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ones = paddle.eye(dim)
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nmat = paddle.scatter(ones, paddle.to_tensor(idlst), ones)
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oneslst.append(nmat)
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return np.array(oneslst).reshape([*pshape[:-1], dim, dim])
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# m < n
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class TestLUOp(OpTest):
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"""
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case 1
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"""
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def config(self):
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self.x_shape = [3, 10, 12]
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self.pivot = True
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self.get_infos = True
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self.dtype = "float64"
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def set_output(self):
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X = self.inputs['X']
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sP, sl, sU = scipy_lu(X, self.pivot)
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sL = np.tril(sl, -1)
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ashape = np.array(X.shape)
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lshape = np.array(sL.shape)
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ushape = np.array(sU.shape)
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lpad = (len(sL.shape) - 2) * [(0, 0)] + [
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(0, (ashape - lshape)[-2]),
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(0, (ashape - lshape)[-1]),
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]
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upad = (len(sU.shape) - 2) * [(0, 0)] + [
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(0, (ashape - ushape)[-2]),
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(0, (ashape - ushape)[-1]),
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]
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NsL = np.pad(sL, lpad)
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NsU = np.pad(sU, upad)
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NLU = NsL + NsU
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self.output = NLU
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self.Pivots = Pmat_to_perm(sP, min(ashape[-2], ashape[-1]))
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self.Infos = (
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np.zeros(self.x_shape[:-2]) if len(X.shape) > 2 else np.array(0)
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)
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def set_input(self):
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A = np.random.random(self.x_shape).astype(self.dtype)
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if 'complex' in self.dtype:
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A += 1j * np.random.random(self.x_shape).astype(self.dtype)
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self.inputs = {'X': A}
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def setUp(self):
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self.op_type = "lu"
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self.python_api = paddle.tensor.linalg.lu
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self.python_out_sig = ["Out", "Pivots"]
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self.config()
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self.set_input()
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self.attrs = {'pivots': self.pivot}
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self.set_output()
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self.outputs = {
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'Out': self.output,
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'Pivots': self.Pivots,
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'Infos': self.Infos,
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}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], ['Out'], check_pir=True)
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# m = n 2D
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class TestLUOp2(TestLUOp):
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"""
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case 2
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"""
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def config(self):
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self.x_shape = [10, 10]
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self.pivot = True
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self.get_infos = True
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self.dtype = "float64"
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# m > n
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class TestLUOp3(TestLUOp):
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"""
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case 3
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"""
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def config(self):
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self.x_shape = [2, 12, 10]
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self.pivot = True
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self.get_infos = True
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self.dtype = "float64"
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# complex64
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class TestLUOp4(TestLUOp):
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def config(self):
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self.x_shape = [10, 10]
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self.pivot = True
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self.get_infos = True
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self.dtype = "complex64"
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# complex128
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class TestLUOp5(TestLUOp):
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def config(self):
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self.x_shape = [10, 10]
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self.pivot = True
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self.get_infos = True
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self.dtype = "complex128"
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class TestLUAPI(unittest.TestCase):
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def test_dygraph(self):
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def run_lu_dygraph(shape, dtype):
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if dtype == "float32":
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np_dtype = np.float32
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elif dtype == "float64":
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np_dtype = np.float64
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elif dtype == "complex64":
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np_dtype = np.complex64
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elif dtype == "complex128":
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np_dtype = np.complex128
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np.random.seed(1024)
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a = np.random.rand(*shape).astype(np_dtype)
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if dtype in {"complex64", "complex128"}:
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a = a + 1j * np.random.rand(*shape).astype(np_dtype)
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m = a.shape[-2]
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n = a.shape[-1]
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min_mn = min(m, n)
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pivot = True
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for place in get_places():
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paddle.disable_static(place)
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batch_size = a.size // (a.shape[-1] * a.shape[-2])
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x = paddle.to_tensor(a, dtype=dtype)
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sP, sl, sU = scipy_lu(a, pivot)
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sL = np.tril(sl, -1)
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LU, P, Info = paddle.linalg.lu(x, pivot=pivot, get_infos=True)
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m, n = LU.shape[-2], LU.shape[-1]
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tril = np.tril(LU, -1)[..., :m, :m]
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triu = np.triu(LU)[..., :n, :n]
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mtp = Pmat_to_perm(sP, min(m, n))
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nP = perm_to_Pmat(P, sP.shape[-1])
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np.testing.assert_allclose(sU, triu, rtol=1e-05, atol=1e-05)
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np.testing.assert_allclose(sL, tril, rtol=1e-05, atol=1e-05)
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np.testing.assert_allclose(P, mtp, rtol=1e-05, atol=1e-05)
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np.testing.assert_allclose(nP, sP, rtol=1e-05, atol=1e-05)
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tensor_shapes = [
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(3, 5),
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(5, 5),
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(5, 3), # 2-dim Tensors
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(2, 3, 5),
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(3, 5, 5),
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(4, 5, 3), # 3-dim Tensors
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(2, 5, 3, 5),
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(3, 5, 5, 5),
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(4, 5, 5, 3), # 4-dim Tensors
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]
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dtypes = ["float32", "float64", "complex64", "complex128"]
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for tensor_shape, dtype in itertools.product(tensor_shapes, dtypes):
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run_lu_dygraph(tensor_shape, dtype)
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def test_static(self):
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paddle.enable_static()
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def run_lu_static(shape, dtype):
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if dtype == "float32":
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np_dtype = np.float32
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elif dtype == "float64":
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np_dtype = np.float64
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elif dtype == "complex64":
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np_dtype = np.complex64
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elif dtype == "complex128":
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np_dtype = np.complex128
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a = np.random.rand(*shape).astype(np_dtype)
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if dtype in {"complex64", "complex128"}:
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a = a + 1j * np.random.rand(*shape).astype(np_dtype)
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m = a.shape[-2]
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n = a.shape[-1]
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min_mn = min(m, n)
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pivot = True
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for place in get_places():
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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batch_size = a.size // (a.shape[-1] * a.shape[-2])
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sP, sl, sU = scipy_lu(a, pivot)
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sL = np.tril(sl, -1)
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ashape = np.array(a.shape)
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lshape = np.array(sL.shape)
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ushape = np.array(sU.shape)
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lpad = (len(sL.shape) - 2) * [(0, 0)] + [
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(0, (ashape - lshape)[-2]),
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(0, (ashape - lshape)[-1]),
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]
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upad = (len(sU.shape) - 2) * [(0, 0)] + [
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(0, (ashape - ushape)[-2]),
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(0, (ashape - ushape)[-1]),
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]
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NsL = np.pad(sL, lpad)
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NsU = np.pad(sU, upad)
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NLU = NsL + NsU
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x = paddle.static.data(
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name="input", shape=shape, dtype=dtype
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)
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lu, p = paddle.linalg.lu(x, pivot=pivot)
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exe = base.Executor(place)
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fetches = exe.run(
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feed={"input": a},
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fetch_list=[lu, p],
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)
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np.testing.assert_allclose(
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fetches[0], NLU, rtol=1e-05, atol=1e-05
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)
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tensor_shapes = [
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(3, 5),
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(5, 5),
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(5, 3), # 2-dim Tensors
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(2, 3, 5),
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(3, 5, 5),
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(4, 5, 3), # 3-dim Tensors
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(2, 5, 3, 5),
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(3, 5, 5, 5),
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(4, 5, 5, 3), # 4-dim Tensors
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]
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dtypes = ["float32", "float64", "complex64", "complex128"]
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for tensor_shape, dtype in itertools.product(tensor_shapes, dtypes):
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run_lu_static(tensor_shape, dtype)
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# class TestLUAPIError(unittest.TestCase):
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# def test_errors(self):
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# with paddle.base.dygraph.guard():
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# # The size of input in lu should not be 0.
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# def test_0_size():
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# array = np.array([], dtype=np.float32)
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# x = paddle.to_tensor(
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# np.reshape(array, [0, 0, 0]), dtype='float32'
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# )
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# paddle.linalg.lu(x, get_infos=True)
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# self.assertRaises(ValueError, test_0_size)
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class TestLUAPIZeroSize(unittest.TestCase):
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def init_test_case(self):
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self.x_shape = [1, 0, 10]
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self.dtype = "float32"
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def _test_dygraph(self):
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paddle.disable_static()
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array = np.ones(self.x_shape).astype(self.dtype)
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x = paddle.to_tensor(array, stop_gradient=False)
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lu, p, info = paddle.linalg.lu(x, get_infos=True)
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loss = lu.sum()
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loss.backward()
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self.assertEqual(x.grad.shape, x.shape)
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def test_zero_size(self):
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self.init_test_case()
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self._test_dygraph()
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class TestLUAPI_ZeroSize(unittest.TestCase):
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def test_zero_size1(self):
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self.x_shape = (2, 0, 12)
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self.dtype = "float32"
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paddle.disable_static()
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a = np.random.randn(*self.x_shape)
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x = paddle.to_tensor(a, dtype=self.dtype, stop_gradient=False)
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lu, p, info = paddle.linalg.lu(x, get_infos=True)
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loss = lu.sum()
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loss.backward()
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self.assertEqual(x.grad.shape, x.shape)
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class TestLUOp(OpTest):
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def config(self):
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self.x_shape = [2, 0, 12]
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self.pivot = True
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self.get_infos = True
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self.dtype = "float64"
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def setUp(self):
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self.op_type = "lu"
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self.python_api = paddle.tensor.linalg.lu
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self.python_out_sig = ["Out", "Pivots"]
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self.config()
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A = np.random.random([2, 0, 12]).astype(self.dtype)
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self.inputs = {'X': A}
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self.attrs = {'pivots': self.pivot}
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self.output = np.zeros([2, 0, 12]).astype(self.dtype)
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self.Pivots = np.zeros([2, 0]).astype(self.dtype)
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self.Infos = np.zeros([2]).astype(self.dtype)
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self.outputs = {
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'Out': self.output,
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'Pivots': self.Pivots,
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'Infos': self.Infos,
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}
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def test_check_output(self):
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self.check_output_with_place(paddle.CPUPlace(), check_pir=True)
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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paddle.CPUPlace(), ['X'], ['Out'], check_pir=True
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)
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(
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get_device_place(), ['X'], ['Out'], check_pir=True
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)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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