wehub-resource-sync
445 lines
13 KiB
Python
445 lines
13 KiB
Python
# 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.
|
|
|
|
import copy
|
|
import itertools
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import scipy
|
|
import scipy.linalg
|
|
from op_test import OpTest, get_device_place, get_places, is_custom_device
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core
|
|
|
|
|
|
def scipy_lu_unpack(A):
|
|
shape = A.shape
|
|
if len(shape) == 2:
|
|
return scipy.linalg.lu(A)
|
|
else:
|
|
preshape = shape[:-2]
|
|
batchsize = np.prod(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.prod(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(
|
|
[
|
|
*shape[:-2],
|
|
rows,
|
|
]
|
|
)
|
|
+ 1
|
|
)
|
|
|
|
return Pivot[..., :cut]
|
|
|
|
|
|
def perm_to_Pmat(perm, dim):
|
|
pshape = perm.shape
|
|
bs = int(np.prod(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([*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.python_api = paddle.tensor.linalg.lu_unpack
|
|
self.python_out_sig = ["Pmat", "L", "U"]
|
|
self.config()
|
|
x = np.random.random(self.x_shape).astype(self.dtype)
|
|
if 'complex' in self.dtype:
|
|
x += 1j * 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 paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
place = base.CPUPlace()
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
xv = paddle.static.data(
|
|
name="input", shape=self.x_shape, dtype=self.dtype
|
|
)
|
|
lu, p = paddle.linalg.lu(xv)
|
|
exe = base.Executor(place)
|
|
fetches = exe.run(
|
|
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(check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], ['L', 'U'], check_pir=True)
|
|
|
|
|
|
# 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"
|
|
|
|
|
|
# batchsize = 0
|
|
class TestLU_UnpackOp4(TestLU_UnpackOp):
|
|
"""
|
|
case 4
|
|
"""
|
|
|
|
def config(self):
|
|
self.x_shape = [10, 12]
|
|
self.unpack_ludata = True
|
|
self.unpack_pivots = True
|
|
self.dtype = "float64"
|
|
|
|
|
|
# complex64
|
|
class TestLU_UnpackOp5(TestLU_UnpackOp):
|
|
"""
|
|
case 5
|
|
"""
|
|
|
|
def config(self):
|
|
self.x_shape = [10, 12]
|
|
self.unpack_ludata = True
|
|
self.unpack_pivots = True
|
|
self.dtype = "complex64"
|
|
|
|
|
|
# complex128
|
|
class TestLU_UnpackOp6(TestLU_UnpackOp):
|
|
"""
|
|
case 6
|
|
"""
|
|
|
|
def config(self):
|
|
self.x_shape = [10, 12]
|
|
self.unpack_ludata = True
|
|
self.unpack_pivots = True
|
|
self.dtype = "complex128"
|
|
|
|
|
|
class TestLU_UnpackAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2022)
|
|
|
|
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
|
|
elif dtype == "complex64":
|
|
np_dtype = np.complex64
|
|
elif dtype == "complex128":
|
|
np_dtype = np.complex128
|
|
a = np.random.rand(*shape).astype(np_dtype)
|
|
if dtype in {"complex64", "complex128"}:
|
|
a += 1j * np.random.rand(*shape).astype(np_dtype)
|
|
m = a.shape[-2]
|
|
n = a.shape[-1]
|
|
min_mn = min(m, n)
|
|
|
|
for place in get_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)
|
|
|
|
np.testing.assert_allclose(sU, pU, rtol=1e-05, atol=1e-05)
|
|
np.testing.assert_allclose(sL, pL, rtol=1e-05, atol=1e-05)
|
|
np.testing.assert_allclose(sP, pP, rtol=1e-05, atol=1e-05)
|
|
|
|
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", "complex64", "complex128"]
|
|
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
|
|
elif dtype == "complex64":
|
|
np_dtype = np.complex64
|
|
elif dtype == "complex128":
|
|
np_dtype = np.complex128
|
|
a = np.random.rand(*shape).astype(np_dtype)
|
|
if dtype in {"complex64", "complex128"}:
|
|
a += 1j * np.random.rand(*shape).astype(np_dtype)
|
|
m = a.shape[-2]
|
|
n = a.shape[-1]
|
|
min_mn = min(m, n)
|
|
|
|
for place in get_places():
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
sP, sL, sU = scipy_lu_unpack(a)
|
|
|
|
x = paddle.static.data(
|
|
name="input", shape=shape, dtype=dtype
|
|
)
|
|
lu, p = paddle.linalg.lu(x)
|
|
pP, pL, pU = paddle.linalg.lu_unpack(lu, p)
|
|
exe = base.Executor(place)
|
|
fetches = exe.run(
|
|
feed={"input": a},
|
|
fetch_list=[pP, pL, pU],
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[0], sP, rtol=1e-05, atol=1e-05
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[1], sL, rtol=1e-05, atol=1e-05
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[2], sU, rtol=1e-05, atol=1e-05
|
|
)
|
|
|
|
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", "complex64", "complex128"]
|
|
for tensor_shape, dtype in itertools.product(tensor_shapes, dtypes):
|
|
run_lu_static(tensor_shape, dtype)
|
|
|
|
|
|
class TestLU_UnpackAPIError(unittest.TestCase):
|
|
def test_errors_1(self):
|
|
with paddle.base.dygraph.guard():
|
|
# The size of input in lu should not be 0.
|
|
def test_x_size():
|
|
x = paddle.to_tensor(
|
|
np.random.uniform(-6666666, 100000000, [2]).astype(
|
|
np.float32
|
|
)
|
|
)
|
|
y = paddle.to_tensor(
|
|
np.random.uniform(-2147483648, 2147483647, [2]).astype(
|
|
np.int32
|
|
)
|
|
)
|
|
unpack_ludata = True
|
|
unpack_pivots = True
|
|
paddle.linalg.lu_unpack(x, y, unpack_ludata, unpack_pivots)
|
|
|
|
self.assertRaises(ValueError, test_x_size)
|
|
|
|
def test_errors_2(self):
|
|
with paddle.base.dygraph.guard():
|
|
# The size of input in lu should not be 0.
|
|
def test_y_size():
|
|
x = paddle.to_tensor(
|
|
np.random.uniform(-6666666, 100000000, [8, 4, 2]).astype(
|
|
np.float32
|
|
)
|
|
)
|
|
y = paddle.to_tensor(
|
|
np.random.uniform(-2147483648, 2147483647, []).astype(
|
|
np.int32
|
|
)
|
|
)
|
|
unpack_ludata = True
|
|
unpack_pivots = True
|
|
paddle.linalg.lu_unpack(x, y, unpack_ludata, unpack_pivots)
|
|
|
|
self.assertRaises(ValueError, test_y_size)
|
|
|
|
def test_errors_3(self):
|
|
with paddle.base.dygraph.guard():
|
|
# The size of input in lu should not be 0.
|
|
def test_y_data():
|
|
x = paddle.to_tensor(
|
|
np.random.uniform(-6666666, 100000000, [8, 4, 2]).astype(
|
|
np.float32
|
|
)
|
|
)
|
|
y = paddle.to_tensor(
|
|
np.random.uniform(-2147483648, 2147483647, [8, 2]).astype(
|
|
np.int32
|
|
)
|
|
)
|
|
unpack_ludata = True
|
|
unpack_pivots = True
|
|
paddle.linalg.lu_unpack(x, y, unpack_ludata, unpack_pivots)
|
|
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
"The data in Pivot must be between",
|
|
test_y_data,
|
|
)
|
|
|
|
|
|
class TestLuUnpackAPI_ZeroSize(unittest.TestCase):
|
|
def test_dygraph_api(self):
|
|
for place in get_places():
|
|
paddle.disable_static(place)
|
|
x_np = np.random.random([2, 3, 0])
|
|
y_np = np.random.random([2, 3])
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
y = paddle.to_tensor(y_np)
|
|
out = paddle.linalg.lu_unpack(x, y)
|
|
np_out0 = np.array([np.eye(3) for _ in range(2)])
|
|
np_out1 = np.random.random([2, 3, 0])
|
|
np_out2 = np.random.random([2, 0, 0])
|
|
np.testing.assert_allclose(out[0].numpy(), np_out0)
|
|
np.testing.assert_allclose(out[1].numpy(), np_out1)
|
|
np.testing.assert_allclose(out[2].numpy(), np_out2)
|
|
|
|
paddle.sum(out[0]).backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|