wehub-resource-sync
310 lines
9.8 KiB
Python
310 lines
9.8 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 itertools
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import OpTest, get_device, get_device_place, is_custom_device
|
|
from utils import dygraph_guard, static_guard
|
|
|
|
import paddle
|
|
from paddle import base, static
|
|
from paddle.base import core
|
|
|
|
|
|
class TestQrOp(OpTest):
|
|
def setUp(self):
|
|
with static_guard():
|
|
self.python_api = paddle.linalg.qr
|
|
np.random.seed(7)
|
|
self.op_type = "qr"
|
|
a, q, r = self.get_input_and_output()
|
|
self.inputs = {"X": a}
|
|
self.attrs = {"mode": self.get_mode()}
|
|
self.outputs = {"Q": q, "R": r}
|
|
|
|
def get_dtype(self):
|
|
return "float64"
|
|
|
|
def get_mode(self):
|
|
return "reduced"
|
|
|
|
def get_shape(self):
|
|
return (11, 11)
|
|
|
|
def _get_places(self):
|
|
places = []
|
|
places.append(base.CPUPlace())
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device_place())
|
|
return places
|
|
|
|
def get_input_and_output(self):
|
|
dtype = self.get_dtype()
|
|
shape = self.get_shape()
|
|
mode = self.get_mode()
|
|
assert mode != "r", "Cannot be backward in r mode."
|
|
a = np.random.rand(*shape).astype(dtype)
|
|
q, r = np.linalg.qr(a, mode=mode)
|
|
return a, q, r
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(
|
|
['X'],
|
|
['Q', 'R'],
|
|
numeric_grad_delta=1e-5,
|
|
max_relative_error=1e-6,
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
class TestQrOpCase1(TestQrOp):
|
|
def get_shape(self):
|
|
return (10, 12)
|
|
|
|
|
|
class TestQrOpCase2(TestQrOp):
|
|
def get_shape(self):
|
|
return (16, 15)
|
|
|
|
|
|
class TestQrOpCase3(TestQrOp):
|
|
def get_shape(self):
|
|
return (2, 12, 16)
|
|
|
|
|
|
class TestQrOpCase4(TestQrOp):
|
|
def get_shape(self):
|
|
return (3, 16, 15)
|
|
|
|
|
|
class TestQrOpCase5(TestQrOp):
|
|
def get_mode(self):
|
|
return "complete"
|
|
|
|
def get_shape(self):
|
|
return (10, 12)
|
|
|
|
|
|
class TestQrOpCase6(TestQrOp):
|
|
def get_mode(self):
|
|
return "complete"
|
|
|
|
def get_shape(self):
|
|
return (2, 10, 12)
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for complex dtype is not fully supported",
|
|
)
|
|
class TestQrOpcomplex(TestQrOp):
|
|
def get_input_and_output(self):
|
|
dtype = self.get_dtype()
|
|
shape = self.get_shape()
|
|
mode = self.get_mode()
|
|
assert mode != "r", "Cannot be backward in r mode."
|
|
a_real = np.random.rand(*shape).astype(dtype)
|
|
a_imag = np.random.rand(*shape).astype(dtype)
|
|
a = a_real + 1j * a_imag
|
|
q, r = np.linalg.qr(a, mode=mode)
|
|
return a, q, r
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for complex dtype is not fully supported",
|
|
)
|
|
class TestQrOpcomplexCase1(TestQrOpcomplex):
|
|
def get_shape(self):
|
|
return (16, 15)
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for complex dtype is not fully supported",
|
|
)
|
|
class TestQrOpcomplexCase2(TestQrOpcomplex):
|
|
def get_shape(self):
|
|
return (3, 16, 15)
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for complex dtype is not fully supported",
|
|
)
|
|
class TestQrOpcomplexCase3(TestQrOpcomplex):
|
|
def get_shape(self):
|
|
return (12, 15)
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for complex dtype is not fully supported",
|
|
)
|
|
class TestQrOpcomplexCase4(TestQrOpcomplex):
|
|
def get_shape(self):
|
|
return (3, 12, 15)
|
|
|
|
|
|
class TestQrAPI(unittest.TestCase):
|
|
def test_dygraph(self):
|
|
def run_qr_dygraph(shape, mode, 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
|
|
if np.issubdtype(np_dtype, np.complexfloating):
|
|
a_dtype = np.float32 if np_dtype == np.complex64 else np.float64
|
|
a_real = np.random.rand(*shape).astype(a_dtype)
|
|
a_imag = np.random.rand(*shape).astype(a_dtype)
|
|
a = a_real + 1j * a_imag
|
|
else:
|
|
a = np.random.rand(*shape).astype(np_dtype)
|
|
places = []
|
|
places.append('cpu')
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device())
|
|
for place in places:
|
|
if mode == "r":
|
|
np_r = np.linalg.qr(a, mode=mode)
|
|
else:
|
|
np_q, np_r = np.linalg.qr(a, mode=mode)
|
|
|
|
x = paddle.to_tensor(a, dtype=dtype, place=place)
|
|
if mode == "r":
|
|
r = paddle.linalg.qr(x, mode=mode)
|
|
np.testing.assert_allclose(r, np_r, rtol=1e-05, atol=1e-05)
|
|
else:
|
|
q, r = paddle.linalg.qr(x, mode=mode)
|
|
np.testing.assert_allclose(q, np_q, rtol=1e-05, atol=1e-05)
|
|
np.testing.assert_allclose(r, np_r, rtol=1e-05, atol=1e-05)
|
|
|
|
with dygraph_guard():
|
|
np.random.seed(7)
|
|
tensor_shapes = [
|
|
(0, 3),
|
|
(3, 5),
|
|
(5, 5),
|
|
(5, 3), # 2-dim Tensors
|
|
(0, 3, 5),
|
|
(4, 0, 5),
|
|
(5, 4, 0),
|
|
(2, 3, 5),
|
|
(3, 5, 5),
|
|
(4, 5, 3), # 3-dim Tensors
|
|
(0, 5, 3, 5),
|
|
(2, 5, 3, 5),
|
|
(3, 5, 5, 5),
|
|
(4, 5, 5, 3), # 4-dim Tensors
|
|
]
|
|
modes = ["reduced", "complete", "r"]
|
|
dtypes = ["float32", "float64", 'complex64', 'complex128']
|
|
for tensor_shape, mode, dtype in itertools.product(
|
|
tensor_shapes, modes, dtypes
|
|
):
|
|
run_qr_dygraph(tensor_shape, mode, dtype)
|
|
|
|
def test_static(self):
|
|
def run_qr_static(shape, mode, 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
|
|
if np.issubdtype(np_dtype, np.complexfloating):
|
|
a_dtype = np.float32 if np_dtype == np.complex64 else np.float64
|
|
a_real = np.random.rand(*shape).astype(a_dtype)
|
|
a_imag = np.random.rand(*shape).astype(a_dtype)
|
|
a = a_real + 1j * a_imag
|
|
else:
|
|
a = np.random.rand(*shape).astype(np_dtype)
|
|
places = []
|
|
places.append(paddle.CPUPlace())
|
|
if (
|
|
core.is_compiled_with_cuda() or is_custom_device()
|
|
) or is_custom_device():
|
|
places.append(get_device_place())
|
|
|
|
for place in places:
|
|
with static.program_guard(static.Program(), static.Program()):
|
|
if mode == "r":
|
|
np_r = np.linalg.qr(a, mode=mode)
|
|
else:
|
|
np_q, np_r = np.linalg.qr(a, mode=mode)
|
|
x = paddle.static.data(
|
|
name="input", shape=shape, dtype=dtype
|
|
)
|
|
if mode == "r":
|
|
r = paddle.linalg.qr(x, mode=mode)
|
|
exe = base.Executor(place=place)
|
|
fetches = exe.run(
|
|
feed={"input": a},
|
|
fetch_list=[r],
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[0], np_r, rtol=1e-05, atol=1e-05
|
|
)
|
|
else:
|
|
q, r = paddle.linalg.qr(x, mode=mode)
|
|
exe = base.Executor(place=place)
|
|
fetches = exe.run(
|
|
feed={"input": a},
|
|
fetch_list=[q, r],
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[0], np_q, rtol=1e-05, atol=1e-05
|
|
)
|
|
np.testing.assert_allclose(
|
|
fetches[1], np_r, rtol=1e-05, atol=1e-05
|
|
)
|
|
|
|
with static_guard():
|
|
np.random.seed(7)
|
|
tensor_shapes = [
|
|
(0, 3),
|
|
(3, 5),
|
|
(5, 5),
|
|
(5, 3), # 2-dim Tensors
|
|
(0, 3, 5),
|
|
(4, 0, 5),
|
|
(5, 4, 0),
|
|
(4, 5, 3), # 3-dim Tensors
|
|
(0, 5, 3, 5),
|
|
(2, 5, 3, 5),
|
|
(3, 5, 5, 5),
|
|
(4, 5, 5, 3), # 4-dim Tensors
|
|
]
|
|
modes = ["reduced", "complete", "r"]
|
|
dtypes = ["float32", "float64", 'complex64', 'complex128']
|
|
for tensor_shape, mode, dtype in itertools.product(
|
|
tensor_shapes, modes, dtypes
|
|
):
|
|
run_qr_static(tensor_shape, mode, dtype)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|