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提交 e19d6ca3 编写于 作者: T tianshuo78520a
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notest;add kunlun_test

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paddle/scripts/paddle_build.sh
浏览文件 @ e19d6ca3
...@@ -1694,6 +1694,7 @@ function main() { ...@@ -1694,6 +1694,7 @@ function main() {
check_xpu) check_xpu)
cmake_gen_and_build ${PYTHON_ABI:-""} ${parallel_number} cmake_gen_and_build ${PYTHON_ABI:-""} ${parallel_number}
parallel_test parallel_test
;;
cmake_gen) cmake_gen)
cmake_gen ${PYTHON_ABI:-""} cmake_gen ${PYTHON_ABI:-""}
;; ;;
......
python/paddle/fluid/tests/unittests/xpu/test_activation_op.py 已删除 100755 → 0
浏览文件 @ 73da8c02
# Copyright (c) 2018 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
import sys
sys.path.append("..")
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from scipy.special import expit, erf
import paddle
import paddle.fluid as fluid
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.fluid import compiler, Program, program_guard
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUActivation(OpTest):
def setUp(self):
self.op_type = "exp"
self.init_dtype()
self.init_kernel_type()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.exp(x)
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_dtype(self):
self.dtype = np.float32
def test_check_output(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_output_with_place(place, atol=1e-3)
def init_kernel_type(self):
pass
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUSigmoid(TestXPUActivation):
def setUp(self):
self.op_type = "sigmoid"
self.init_dtype()
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = 1 / (1 + np.exp(-x))
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X'], 'Out', max_relative_error=0.01)
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUTanh(TestXPUActivation):
def setUp(self):
self.op_type = "tanh"
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.tanh(x)
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUSqrt(TestXPUActivation):
def setUp(self):
self.op_type = "sqrt"
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.sqrt(x)
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUAbs(TestXPUActivation):
def setUp(self):
self.op_type = "abs"
self.init_dtype()
x = np.random.uniform(-1, 1, [4, 25]).astype(self.dtype)
# Because we set delta = 0.005 in calculating numeric gradient,
# if x is too small, such as 0.002, x_neg will be -0.003
# x_pos will be 0.007, so the numeric gradient is inaccurate.
# we should avoid this
x[np.abs(x) < 0.005] = 0.02
out = np.abs(x)
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPURelu(TestXPUActivation):
def setUp(self):
self.op_type = "relu"
self.init_dtype()
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
out = np.maximum(x, 0)
self.attrs = {'use_xpu': True}
self.inputs = {'X': x}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUGelu(TestXPUActivation):
def setUp(self):
self.op_type = "gelu"
self.init_dtype()
approximate = False
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = gelu(x, approximate)
self.inputs = {'X': x}
self.outputs = {'Out': out}
self.attrs = {"approximate": approximate, 'use_xpu': True}
def gelu(x, approximate):
if approximate:
y_ref = 0.5 * x * (1.0 + np.tanh(
np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
else:
y_ref = 0.5 * x * (1 + erf(x / np.sqrt(2)))
return y_ref.astype(x.dtype)
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPULog(TestXPUActivation):
def setUp(self):
self.op_type = "log"
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.log(x)
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUSquare(TestXPUActivation):
def setUp(self):
self.op_type = "square"
self.init_dtype()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
out = np.square(x)
self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUPow(TestXPUActivation):
def setUp(self):
self.op_type = "pow"
self.init_dtype()
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
out = np.power(x, 3)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.attrs = {'factor': 3.0, 'use_xpu': True}
self.outputs = {'Out': out}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_elementwise_add_op.py 已删除 100644 → 0
浏览文件 @ 73da8c02
# Copyright (c) 2018 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
import sys
sys.path.append("..")
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from op_test import OpTest, skip_check_grad_ci
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
class TestElementwiseAddOp(OpTest):
def init_kernel_type(self):
self.use_mkldnn = False
def setUp(self):
self.op_type = "elementwise_add"
self.init_dtype()
self.init_input_output()
self.init_kernel_type()
self.init_axis()
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
}
self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': self.out}
def test_check_output(self):
# TODO(wangzhongpu): support mkldnn op in dygraph mode
self.check_output(check_dygraph=(self.use_mkldnn == False))
def test_check_grad_normal(self):
# TODO(wangzhongpu): support mkldnn op in dygraph mode
if self.dtype == np.float16:
return
self.check_grad(
['X', 'Y'], 'Out', check_dygraph=(self.use_mkldnn == False))
def test_check_grad_ingore_x(self):
# TODO(wangzhongpu): support mkldnn op in dygraph mode
if self.dtype == np.float16:
return
self.check_grad(
['Y'],
'Out',
no_grad_set=set("X"),
check_dygraph=(self.use_mkldnn == False))
def test_check_grad_ingore_y(self):
# TODO(wangzhongpu): support mkldnn op in dygraph mode
if self.dtype == np.float16:
return
self.check_grad(
['X'],
'Out',
no_grad_set=set('Y'),
check_dygraph=(self.use_mkldnn == False))
def init_input_output(self):
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
self.out = np.add(self.x, self.y)
def init_dtype(self):
self.dtype = np.float64
def init_axis(self):
self.axis = -1
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUElementwiseAddOp(OpTest):
def setUp(self):
self.op_type = "elementwise_add"
self.init_dtype()
self.init_input_output()
self.init_axis()
self.inputs = {'X': self.x, 'Y': self.y}
self.attrs = {'axis': self.axis, 'use_mkldnn': False, 'use_xpu': True}
self.outputs = {'Out': self.out}
def test_check_output(self):
if self.dtype == np.float32 and paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad_normal(self):
if self.dtype == np.float32 and paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X', 'Y'], 'Out')
def test_check_grad_ingore_x(self):
if self.dtype == np.float32 and paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['Y'], 'Out')
def test_check_grad_ingore_y(self):
if self.dtype == np.float32 and paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
def init_input_output(self):
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
self.out = np.add(self.x, self.y)
def init_dtype(self):
self.dtype = np.float32
def init_axis(self):
self.axis = -1
@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y
@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1,1) to test broadcast.")
class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1, 1).astype(self.dtype)
self.out = self.x + self.y
class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.random((100, )).astype(self.dtype)
self.y = np.random.random((100, )).astype(self.dtype)
self.out = np.add(self.x, self.y)
class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(100, 1, 1)
def init_axis(self):
self.axis = 0
class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 100, 3).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 100, 1)
def init_axis(self):
self.axis = 1
class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 100)
class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
self.y = np.random.rand(10, 12).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 10, 12, 1)
def init_axis(self):
self.axis = 1
class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(100, 2, 3, 4).astype(self.dtype)
self.y = np.random.rand(100, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(100, 1, 1, 1)
def init_axis(self):
self.axis = 0
class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(10, 3, 12).astype(self.dtype)
self.y = np.random.rand(10, 1, 12).astype(self.dtype)
self.out = self.x + self.y
class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
self.out = self.x + self.y
class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
self.out = self.x + self.y
class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 10, 12).astype(self.dtype)
self.y = np.random.rand(10, 12).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 10, 12)
def init_axis(self):
self.axis = 1
@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(100, 1).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1)
def init_axis(self):
self.axis = 1
class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
self.y = np.random.rand(100, 1, 1).astype(self.dtype)
self.out = self.x + self.y
def init_axis(self):
self.axis = -1
class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
self.y = np.random.rand(1, 1, 100).astype(self.dtype)
self.out = self.x + self.y
def init_axis(self):
self.axis = -1
class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
self.out = self.x + self.y
def init_axis(self):
self.axis = -1
class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(10, 12).astype(self.dtype)
self.y = np.random.rand(2, 3, 10, 12).astype(self.dtype)
self.out = self.x + self.y
def init_axis(self):
self.axis = 2
class TestElementwiseAddOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# the input of elementwise_add must be Variable.
x1 = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
y1 = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)
# the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64
# float16 only can be set on GPU place
x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
class TestAddOp(unittest.TestCase):
def test_name(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data(name="x", shape=[2, 3], dtype="float32")
y = fluid.data(name='y', shape=[2, 3], dtype='float32')
y_1 = paddle.add(x, y, name='add_res')
self.assertEqual(('add_res' in y_1.name), True)
def test_declarative(self):
with fluid.program_guard(fluid.Program()):
def gen_data():
return {
"x": np.array([2, 3, 4]).astype('float32'),
"y": np.array([1, 5, 2]).astype('float32')
}
x = fluid.data(name="x", shape=[3], dtype='float32')
y = fluid.data(name="y", shape=[3], dtype='float32')
z = paddle.add(x, y)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
z_expected = np.array([3., 8., 6.])
self.assertEqual((z_value == z_expected).all(), True)
def test_dygraph(self):
with fluid.dygraph.guard():
np_x = np.array([2, 3, 4]).astype('float64')
np_y = np.array([1, 5, 2]).astype('float64')
x = fluid.dygraph.to_variable(np_x)
y = fluid.dygraph.to_variable(np_y)
z = paddle.add(x, y)
np_z = z.numpy()
z_expected = np.array([3., 8., 6.])
self.assertEqual((np_z == z_expected).all(), True)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_matmul_op.py 已删除 100644 → 0
浏览文件 @ 73da8c02
# Copyright (c) 2018 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
import sys
sys.path.append("..")
import paddle.fluid.core as core
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y):
BATCH_SIZE = 2
M = 3
N = 4
K = 5
if (dim_X == 1 and transpose_X) or (dim_Y == 1 and transpose_Y):
K = 1
if dim_X == 1:
if transpose_X:
shape_X = [M]
else:
shape_X = [K]
if dim_Y == 1:
if transpose_Y:
shape_Y = [N]
else:
shape_Y = [K]
if dim_X >= 2:
if transpose_X:
shape_X = [K, M]
else:
shape_X = [M, K]
if dim_X == 3:
shape_X = [BATCH_SIZE] + shape_X
if dim_Y >= 2:
if transpose_Y:
shape_Y = [N, K]
else:
shape_Y = [K, N]
if dim_Y == 3:
shape_Y = [BATCH_SIZE] + shape_Y
return shape_X, shape_Y
def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
"""Reference forward implementation using np.matmul."""
# np.matmul does not support the transpose flags, so we manually
# transpose X and Y appropriately.
if transpose_X:
if X.ndim == 1:
X = X.reshape((X.size, 1))
elif X.ndim == 2:
X = X.T
else:
dim = [i for i in range(len(X.shape))]
dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
X = np.transpose(X, tuple(dim))
if transpose_Y:
if Y.ndim == 1:
Y = Y.reshape((1, Y.size))
else:
dim = [i for i in range(len(Y.shape))]
dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
Y = np.transpose(Y, tuple(dim))
Out = np.matmul(X, Y)
if not Out.shape:
# We do not support 0-dimensional Tensors (scalars). So where
# np.matmul outputs a scalar, we must convert to a Tensor of
# shape (1, ) instead.
# Everywhere else, we are compatible with np.matmul.
Out = np.array([Out], dtype="float32")
return Out
class Generator(object):
def setUp(self):
self.op_type = "matmul"
X = np.random.random(self.shape_X).astype("float32")
Y = np.random.random(self.shape_Y).astype("float32")
Out = reference_matmul(X, Y, self.transpose_X, self.transpose_Y)
self.inputs = {'X': X, 'Y': Y}
self.attrs = {
'transpose_X': self.transpose_X,
'transpose_Y': self.transpose_Y
}
self.outputs = {'Out': Out}
def test_check_output(self):
self.check_output()
if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
self.inputs['Y'].shape) and self.inputs['X'].shape[
0] == self.inputs['Y'].shape[0]:
place = paddle.XPUPlace(0)
self.check_output_with_place(place, atol=1e-3)
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=1e-3)
if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
self.inputs['Y'].shape) and self.inputs['X'].shape[
0] == self.inputs['Y'].shape[0]:
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X', 'Y'], 'Out', max_relative_error=5e-2)
def test_check_grad_ignore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=1e-3, no_grad_set=set("X"))
if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
self.inputs['Y'].shape) and self.inputs['X'].shape[
0] == self.inputs['Y'].shape[0]:
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['Y'],
'Out',
max_relative_error=5e-2,
no_grad_set=set("X"))
def test_check_grad_ignore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=1e-3, no_grad_set=set('Y'))
if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
self.inputs['Y'].shape) and self.inputs['X'].shape[
0] == self.inputs['Y'].shape[0]:
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X'],
'Out',
max_relative_error=5e-2,
no_grad_set=set('Y'))
class TestMatmulOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# The inputs type of matmul_op must be Variable.
input1 = 12
self.assertRaises(TypeError, fluid.layers.matmul, input1, input1)
# The inputs dtype of matmul_op must be float32, float64.
input2 = fluid.layers.data(
name='input2', shape=[10, 10], dtype="int32")
self.assertRaises(TypeError, fluid.layers.matmul, input2, input2)
input3 = fluid.layers.data(
name='input3', shape=[2, 2], dtype="float16")
fluid.layers.matmul(input3, input3)
# Negative dimension generation
def generate_negative_dims(in_shape):
from itertools import combinations
size = len(in_shape)
indexs = list()
shapes = list()
for i in range(size):
indexs.extend(list(combinations([j for j in range(size)], i + 1)))
for idx in indexs:
shapes.append(
[in_shape[i] if i not in idx else -1 for i in range(size)])
return shapes
# Build program with inputs sizes that contain negative numbers
def test_negative_dims_program(obj):
for shape_x in generate_negative_dims(obj.shape_X):
for shape_y in generate_negative_dims(obj.shape_Y):
X = np.random.random(obj.shape_X).astype("float32")
Y = np.random.random(obj.shape_Y).astype("float32")
Ref = reference_matmul(X, Y, obj.transpose_X, obj.transpose_Y)
with program_guard(Program(), Program()):
x = fluid.data(name='x', shape=shape_x, dtype='float32')
y = fluid.data(name='y', shape=shape_y, dtype='float32')
output = fluid.layers.matmul(x, y, obj.transpose_X,
obj.transpose_Y)
obj.assertEqual(len(Ref.shape), len(output.shape))
for idx in range(len(Ref.shape)):
if output.shape[idx] != -1:
obj.assertEqual(Ref.shape[idx], output.shape[idx])
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(fluid.default_main_program(),
feed={'x': X,
'y': Y},
fetch_list=[output])
np.allclose(res, Ref, atol=1e-5)
# Generate program api cases for all negative possibilities
def api_test(dim_x, dim_y, trans_x, trans_y):
test_name = ('TestMatMulAPI_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
dim_x, dim_y, trans_x, trans_y))
shape_x, shape_y = generate_compatible_shapes(dim_x, dim_y, trans_x,
trans_y)
globals()[test_name] = type(test_name, (unittest.TestCase, ), {
'shape_X': shape_x,
'shape_Y': shape_y,
'transpose_X': trans_x,
'transpose_Y': trans_y,
'test_propram': test_negative_dims_program,
})
# Generate operators cases for all possibilities
def inject_test(dim_x, dim_y, trans_x, trans_y):
test_name = ('TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
dim_x, dim_y, trans_x, trans_y))
shape_x, shape_y = generate_compatible_shapes(dim_x, dim_y, trans_x,
trans_y)
globals()[test_name] = type(test_name, (Generator, OpTest), {
'shape_X': shape_x,
'shape_Y': shape_y,
'transpose_X': trans_x,
'transpose_Y': trans_y,
})
for dim_X in (1, 2, 3):
for dim_Y in (1, 2, 3):
for transose_x in (False, True):
for transose_y in (False, True):
inject_test(dim_X, dim_Y, transose_x, transose_y)
api_test(dim_X, dim_Y, transose_x, transose_y)
# Test case n-dim
def generate_compatible_shapes(dim, transpose_X, transpose_Y):
M = 2
N = 4
K = 3
shape_X = [2 for _ in range(dim - 2)]
shape_Y = [2 for _ in range(dim - 2)]
if transpose_X:
shape_X += [K, M]
else:
shape_X += [M, K]
if transpose_Y:
shape_Y += [N, K]
else:
shape_Y += [K, N]
return shape_X, shape_Y
# # Test case n-dim
for dim in [4]:
for transpose_X in [False, True]:
for transpose_Y in [False, True]:
test_name = (
'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
dim, dim, transpose_X, transpose_Y))
shape_X, shape_Y = generate_compatible_shapes(dim, transpose_X,
transpose_Y)
globals()[test_name] = type(test_name, (Generator, OpTest), {
'shape_X': shape_X,
'shape_Y': shape_Y,
'transpose_X': transpose_X,
'transpose_Y': transpose_Y,
})
class API_TestMm(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data(name="x", shape=[2], dtype="float64")
y = fluid.data(name='y', shape=[2], dtype='float64')
res = fluid.data(name="output", shape=[1], dtype="float64")
result = paddle.mm(x, y)
exe = fluid.Executor(fluid.CPUPlace())
data1 = np.random.rand(2)
data2 = np.random.rand(2)
np_res = exe.run(feed={'x': data1, 'y': data2}, fetch_list=[result])
expected_result = np.matmul(
data1.reshape(1, 2), data2.reshape(2, 1))
self.assertTrue(
np.allclose(
np_res, expected_result, atol=1e-5),
"two value is\
{}\n{}, check diff!".format(np_res, expected_result))
def test_dygraph_without_out(self):
device = fluid.CPUPlace()
with fluid.dygraph.guard(device):
input_array1 = np.random.rand(3, 4).astype("float64")
input_array2 = np.random.rand(4, 3).astype("float64")
data1 = fluid.dygraph.to_variable(input_array1)
data2 = fluid.dygraph.to_variable(input_array2)
out = paddle.mm(data1, data2)
expected_result = np.matmul(input_array1, input_array2)
self.assertTrue(np.allclose(expected_result, out.numpy()))
class Test_API_Matmul(unittest.TestCase):
def test_dygraph_without_out(self):
device = fluid.CPUPlace()
with fluid.dygraph.guard(device):
input_array1 = np.random.rand(3, 4).astype("float64")
input_array2 = np.random.rand(4, 3).astype("float64")
data1 = fluid.dygraph.to_variable(input_array1)
data2 = fluid.dygraph.to_variable(input_array2)
out = paddle.matmul(data1, data2)
expected_result = np.matmul(input_array1, input_array2)
self.assertTrue(np.allclose(expected_result, out.numpy()))
class API_TestMmError(unittest.TestCase):
def test_errors(self):
def test_error1():
with fluid.program_guard(fluid.Program(), fluid.Program()):
data1 = fluid.data(name="data1", shape=[10, 2], dtype="float32")
data2 = fluid.data(name="data2", shape=[3, 10], dtype="float32")
paddle.mm(data1, data2)
self.assertRaises(ValueError, test_error1)
def test_error2():
with fluid.program_guard(fluid.Program(), fluid.Program()):
data1 = fluid.data(
name="data1", shape=[-1, 10, 2], dtype="float32")
data2 = fluid.data(
name="data2", shape=[-1, 2, 10], dtype="float32")
paddle.mm(data1, data2)
test_error2()
def test_error3():
with fluid.program_guard(fluid.Program(), fluid.Program()):
data1 = fluid.data(
name="data1", shape=[10, 10, 2], dtype="float32")
data2 = fluid.data(
name="data2", shape=[3, 2, 10], dtype="float32")
paddle.mm(data1, data2)
self.assertRaises(ValueError, test_error3)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_mul_op.py 已删除 100644 → 0
浏览文件 @ 73da8c02
# Copyright (c) 2018 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
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
import sys
sys.path.append("..")
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
class TestMulOp(OpTest):
def setUp(self):
self.op_type = "mul"
self.dtype = np.float64
self.init_dtype_type()
self.inputs = {
'X': np.random.random((20, 5)).astype(self.dtype),
'Y': np.random.random((5, 21)).astype(self.dtype)
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
def init_dtype_type(self):
pass
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out')
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
class TestMulOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# The input type of mul_op must be Variable.
x1 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace())
x2 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace())
self.assertRaises(TypeError, fluid.layers.mul, x1, x2)
# The input dtype of mul_op must be float32 or float64.
x3 = fluid.layers.data(name='x3', shape=[4], dtype="int32")
x4 = fluid.layers.data(name='x4', shape=[4], dtype="int32")
self.assertRaises(TypeError, fluid.layers.mul, x3, x4)
class TestMulOp2(OpTest):
def setUp(self):
self.op_type = "mul"
self.dtype = np.float64
self.init_dtype_type()
self.inputs = {
'X': np.random.random((3, 4, 2, 9)).astype(self.dtype),
'Y': np.random.random((3, 6, 1, 2, 3)).astype(self.dtype)
}
self.attrs = {
'x_num_col_dims': 2,
'y_num_col_dims': 2,
}
result = np.dot(self.inputs['X'].reshape(3 * 4, 2 * 9),
self.inputs['Y'].reshape(3 * 6, 1 * 2 * 3))
result = result.reshape(3, 4, 1, 2, 3)
self.outputs = {'Out': result}
def init_dtype_type(self):
pass
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out')
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set('X'))
def test_check_grad_ignore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUMulOp1(TestMulOp):
def init_dtype_type(self):
self.dtype = np.float32
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place, atol=1e-1)
def test_check_grad_normal(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X', 'Y'], 'Out', max_relative_error=0.5)
def test_check_grad_ingore_x(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPUMulOp2(TestMulOp2):
def init_dtype_type(self):
self.dtype = np.float32
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place, atol=2e-1)
def test_check_grad_normal(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X', 'Y'], 'Out', max_relative_error=0.9)
def test_check_grad_ingore_x(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X'], 'Out', max_relative_error=0.9, no_grad_set=set('Y'))
if __name__ == "__main__":
unittest.main()
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