wehub-resource-sync
342 lines
12 KiB
Python
342 lines
12 KiB
Python
# Copyright (c) 2020 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 unittest
|
|
|
|
import numpy as np
|
|
from op_test import (
|
|
OpTest,
|
|
convert_float_to_uint16,
|
|
get_device_place,
|
|
is_custom_device,
|
|
)
|
|
|
|
import paddle
|
|
from paddle.base import core
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestDiagonalOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "diagonal"
|
|
self.python_api = paddle.diagonal
|
|
self.init_dtype()
|
|
self.init_config()
|
|
self.outputs = {'Out': self.target}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['Input'], 'Out', check_pir=True)
|
|
|
|
def init_dtype(self):
|
|
self.dtype = 'float64'
|
|
|
|
def init_config(self):
|
|
self.case = np.random.randn(10, 5, 2).astype(self.dtype)
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
|
|
|
|
class TestDiagonalOpCase1(TestDiagonalOp):
|
|
def init_config(self):
|
|
self.case = np.random.randn(4, 2, 4, 4).astype('float32')
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': -2, 'axis1': 3, 'axis2': 0}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
|
|
|
|
class TestDiagonalOpCase2(TestDiagonalOp):
|
|
def init_config(self):
|
|
self.case = np.random.randn(100, 100).astype('int64')
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
self.grad_x = np.eye(100).astype('int64')
|
|
self.grad_out = np.ones(100).astype('int64')
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(
|
|
['Input'],
|
|
'Out',
|
|
user_defined_grads=[self.grad_x],
|
|
user_defined_grad_outputs=[self.grad_out],
|
|
check_pir=True,
|
|
)
|
|
|
|
|
|
class TestDiagonalOpCase3(TestDiagonalOp):
|
|
def init_config(self):
|
|
self.case = np.random.randint(0, 2, (4, 2, 4, 4)).astype('bool')
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': -2, 'axis1': 3, 'axis2': 0}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
pass
|
|
|
|
|
|
class TestDiagonalOpCase4(TestDiagonalOp):
|
|
def init_config(self):
|
|
self.case = np.random.randn(100, 100).astype('int64')
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': 1, 'axis1': 1, 'axis2': 0}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
pass
|
|
|
|
|
|
class TestDiagonalOpCase5(TestDiagonalOp):
|
|
def init_config(self):
|
|
self.case = np.random.randn(4, 2, 4, 4).astype('float32')
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': -2, 'axis1': 0, 'axis2': 3}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
|
|
|
|
class TestDiagonalOp_ZeroSize(TestDiagonalOp):
|
|
def init_config(self):
|
|
self.case = np.random.randn(0, 2, 4, 4).astype(self.dtype)
|
|
self.inputs = {'Input': self.case}
|
|
self.attrs = {'offset': -2, 'axis1': 0, 'axis2': 3}
|
|
self.target = np.diagonal(
|
|
self.inputs['Input'],
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
)
|
|
|
|
|
|
class TestDiagonalAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.shape = [10, 3, 4]
|
|
self.x = np.random.random((10, 3, 4)).astype(np.float32)
|
|
self.place = paddle.CPUPlace()
|
|
|
|
def test_api_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('X', self.shape)
|
|
out = paddle.diagonal(x)
|
|
exe = paddle.static.Executor(self.place)
|
|
res = exe.run(feed={'X': self.x}, fetch_list=[out])
|
|
out_ref = np.diagonal(self.x)
|
|
for out in res:
|
|
np.testing.assert_allclose(out, out_ref, rtol=1e-08)
|
|
|
|
def test_api_dygraph(self):
|
|
paddle.disable_static(self.place)
|
|
x_tensor = paddle.to_tensor(self.x)
|
|
out = paddle.diagonal(x_tensor)
|
|
out_ref = np.diagonal(self.x)
|
|
np.testing.assert_allclose(out.numpy(), out_ref, rtol=1e-08)
|
|
paddle.enable_static()
|
|
|
|
def test_api_eager(self):
|
|
paddle.disable_static(self.place)
|
|
x_tensor = paddle.to_tensor(self.x)
|
|
out = paddle.diagonal(x_tensor)
|
|
out2 = paddle.diagonal(x_tensor, offset=0, axis1=2, axis2=1)
|
|
out3 = paddle.diagonal(x_tensor, offset=1, axis1=0, axis2=1)
|
|
out4 = paddle.diagonal(x_tensor, offset=0, axis1=1, axis2=2)
|
|
out_ref = np.diagonal(self.x)
|
|
np.testing.assert_allclose(out.numpy(), out_ref, rtol=1e-08)
|
|
out2_ref = np.diagonal(self.x, offset=0, axis1=2, axis2=1)
|
|
np.testing.assert_allclose(out2.numpy(), out2_ref, rtol=1e-08)
|
|
out3_ref = np.diagonal(self.x, offset=1, axis1=0, axis2=1)
|
|
np.testing.assert_allclose(out3.numpy(), out3_ref, rtol=1e-08)
|
|
out4_ref = np.diagonal(self.x, offset=0, axis1=1, axis2=2)
|
|
np.testing.assert_allclose(out4.numpy(), out4_ref, rtol=1e-08)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestDiagonalFP16OP(TestDiagonalOp):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
or not core.is_bfloat16_supported(get_device_place()),
|
|
"core is not compiled with CUDA and not support the bfloat16",
|
|
)
|
|
class TestDiagonalBF16OP(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "diagonal"
|
|
self.python_api = paddle.diagonal
|
|
self.dtype = np.uint16
|
|
self.init_config()
|
|
self.outputs = {'Out': convert_float_to_uint16(self.target)}
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_output_with_place(place, check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
place = get_device_place()
|
|
self.check_grad_with_place(place, ['Input'], 'Out', check_pir=True)
|
|
|
|
def init_config(self):
|
|
self.case = np.random.randn(10, 5, 2).astype(np.float32)
|
|
self.inputs = {'Input': convert_float_to_uint16(self.case)}
|
|
self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
|
|
self.target = np.diagonal(
|
|
self.case,
|
|
offset=self.attrs['offset'],
|
|
axis1=self.attrs['axis1'],
|
|
axis2=self.attrs['axis2'],
|
|
).copy()
|
|
|
|
|
|
class TestDiagonalAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
paddle.enable_static()
|
|
self.shape = [5, 6, 7]
|
|
self.dtype = 'float32'
|
|
self.init_data()
|
|
|
|
def init_data(self):
|
|
self.np_input = np.random.rand(*self.shape).astype(self.dtype)
|
|
|
|
def test_dygraph_Compatibility(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.np_input)
|
|
paddle_dygraph_out = []
|
|
# Position args (args)
|
|
out1 = paddle.diagonal(x)
|
|
paddle_dygraph_out.append(out1)
|
|
# Keywords args for paddle
|
|
out2 = paddle.diagonal(x=x, offset=1, axis1=0, axis2=2)
|
|
paddle_dygraph_out.append(out2)
|
|
# Keywords args for torch
|
|
out3 = paddle.diagonal(input=x, offset=-1, dim1=1, dim2=2)
|
|
paddle_dygraph_out.append(out3)
|
|
# Mixed args - paddle parameters prioritized
|
|
out4 = paddle.diagonal(x, offset=0, axis1=1, axis2=2)
|
|
paddle_dygraph_out.append(out4)
|
|
# Mixed args - torch parameters prioritized
|
|
out5 = paddle.diagonal(input=x, offset=0, dim1=1, dim2=2)
|
|
paddle_dygraph_out.append(out5)
|
|
# Tensor method args
|
|
out6 = x.diagonal()
|
|
paddle_dygraph_out.append(out6)
|
|
# Tensor method kwargs
|
|
out7 = x.diagonal(offset=2, dim1=0, dim2=1)
|
|
paddle_dygraph_out.append(out7)
|
|
|
|
ref_out1 = np.diagonal(self.np_input)
|
|
ref_out2 = np.diagonal(self.np_input, offset=1, axis1=0, axis2=2)
|
|
ref_out3 = np.diagonal(self.np_input, offset=-1, axis1=1, axis2=2)
|
|
ref_out4 = np.diagonal(self.np_input, offset=0, axis1=1, axis2=2)
|
|
ref_out5 = np.diagonal(self.np_input, offset=0, axis1=1, axis2=2)
|
|
ref_out6 = np.diagonal(self.np_input)
|
|
ref_out7 = np.diagonal(self.np_input, offset=2, axis1=0, axis2=1)
|
|
ref_outs = [
|
|
ref_out1,
|
|
ref_out2,
|
|
ref_out3,
|
|
ref_out4,
|
|
ref_out5,
|
|
ref_out6,
|
|
ref_out7,
|
|
]
|
|
for out, ref_out in zip(paddle_dygraph_out, ref_outs):
|
|
np.testing.assert_allclose(ref_out, out.numpy(), rtol=1e-6)
|
|
paddle.enable_static()
|
|
|
|
def test_static_Compatibility(self):
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with paddle.base.program_guard(main, startup):
|
|
x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
|
|
# Position args (args)
|
|
out1 = paddle.diagonal(x)
|
|
# Keywords args for paddle
|
|
out2 = paddle.diagonal(x=x, offset=1, axis1=0, axis2=2)
|
|
# Keywords args for torch
|
|
out3 = paddle.diagonal(input=x, offset=-1, dim1=1, dim2=2)
|
|
# Mixed args - paddle parameters prioritized
|
|
out4 = paddle.diagonal(x, offset=0, axis1=1, axis2=2)
|
|
# Mixed args - torch parameters prioritized
|
|
out5 = paddle.diagonal(input=x, offset=0, dim1=1, dim2=2)
|
|
# Tensor method args
|
|
out6 = x.diagonal()
|
|
# Tensor method kwargs
|
|
out7 = x.diagonal(offset=2, dim1=0, dim2=1)
|
|
|
|
exe = paddle.base.Executor(paddle.CPUPlace())
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_input},
|
|
fetch_list=[out1, out2, out3, out4, out5, out6, out7],
|
|
)
|
|
ref_out1 = np.diagonal(self.np_input)
|
|
ref_out2 = np.diagonal(self.np_input, offset=1, axis1=0, axis2=2)
|
|
ref_out3 = np.diagonal(self.np_input, offset=-1, axis1=1, axis2=2)
|
|
ref_out4 = np.diagonal(self.np_input, offset=0, axis1=1, axis2=2)
|
|
ref_out5 = np.diagonal(self.np_input, offset=0, axis1=1, axis2=2)
|
|
ref_out6 = np.diagonal(self.np_input)
|
|
ref_out7 = np.diagonal(self.np_input, offset=2, axis1=0, axis2=1)
|
|
ref_outs = [
|
|
ref_out1,
|
|
ref_out2,
|
|
ref_out3,
|
|
ref_out4,
|
|
ref_out5,
|
|
ref_out6,
|
|
ref_out7,
|
|
]
|
|
for out, ref_out in zip(fetches, ref_outs):
|
|
np.testing.assert_allclose(out, ref_out, rtol=1e-6)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|