未验证 提交 477cb1fd 编写于 作者: B Bai Yifan 提交者: GitHub

Add addcmul, test=develop (#23411)

* add addcmul, test=develop
上级 b7d185d6
......@@ -142,7 +142,7 @@ from .tensor.math import logsumexp #DEFINE_ALIAS
# from .tensor.math import inverse #DEFINE_ALIAS
from .tensor.math import log1p #DEFINE_ALIAS
# from .tensor.math import erf #DEFINE_ALIAS
# from .tensor.math import addcmul #DEFINE_ALIAS
from .tensor.math import addcmul #DEFINE_ALIAS
from .tensor.math import addmm #DEFINE_ALIAS
# from .tensor.attribute import rank #DEFINE_ALIAS
# from .tensor.attribute import shape #DEFINE_ALIAS
......
# 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from paddle.fluid import compiler, Program, program_guard
from op_test import OpTest, skip_check_grad_ci
class TestAddcmulLayer(unittest.TestCase):
def setUp(self):
self._dtype = "float64"
self.input = np.random.uniform(0.1, 1, [3, 100]).astype(self._dtype)
self.tensor1 = np.random.uniform(0.1, 1, [100]).astype(self._dtype)
self.tensor2 = np.random.uniform(0.1, 1, [3, 100]).astype(self._dtype)
def static(self, value=1.0):
prog = fluid.Program()
with fluid.program_guard(prog):
input = fluid.data(name="input", dtype=self._dtype, shape=[3, 100])
tensor1 = fluid.data(name="tensor1", dtype=self._dtype, shape=[100])
tensor2 = fluid.data(
name="tensor2", dtype=self._dtype, shape=[3, 100])
out = paddle.addcmul(input, tensor1, tensor2, value)
exe = fluid.Executor(self._place)
return exe.run(feed={
"input": self.input,
"tensor1": self.tensor1,
"tensor2": self.tensor2
},
program=prog,
fetch_list=[out])[0]
def dynamic(self, value=1.0):
with fluid.dygraph.guard(self._place):
input = fluid.dygraph.to_variable(self.input)
tensor1 = fluid.dygraph.to_variable(self.tensor1)
tensor2 = fluid.dygraph.to_variable(self.tensor2)
out = paddle.addcmul(input, tensor1, tensor2, value)
return out.numpy()
def numpy(self, value=1.0):
self.out = np.add(self.input,
np.multiply(self.tensor1, self.tensor2) * value)
return self.out
def test_equal(self):
places = []
if fluid.core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for place in places:
self._place = place
self.assertTrue(np.allclose(self.numpy(), self.static()))
self.assertTrue(
np.allclose(
self.numpy(value=0.9), self.dynamic(value=0.9)))
self.assertTrue(
np.allclose(
self.numpy(value=0), self.dynamic(value=0)))
class TestAddcmul(unittest.TestCase):
def test_addcmul(self):
program = Program()
with program_guard(program):
data_shape = [3, 64, 64]
input = fluid.data(name='in', shape=data_shape, dtype='float32')
tensor1 = fluid.data(name='t1', shape=data_shape, dtype='float32')
tensor2 = fluid.data(name='t2', shape=data_shape, dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2)
self.assertEqual(out.shape, input.shape)
def test_addcmul_with_broadcast0(self):
program = Program()
with program_guard(program):
input = fluid.data(name='in', shape=[3, 100], dtype='float32')
tensor1 = fluid.data(name='t1', shape=[3, 100], dtype='float32')
tensor2 = fluid.data(name='t2', shape=[100], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2)
self.assertEqual(out.shape, input.shape)
def test_addcmul_with_broadcast1(self):
program = Program()
with program_guard(program):
input = fluid.data(name='in', shape=[4, 100], dtype='float32')
tensor1 = fluid.data(name='t1', shape=[100], dtype='float32')
tensor2 = fluid.data(name='t2', shape=[4, 100], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2)
self.assertEqual(out.shape, input.shape)
def test_addcmul_with_broadcast2(self):
program = Program()
with program_guard(program):
input = fluid.data(name='in', shape=[4, 100], dtype='float32')
tensor1 = fluid.data(name='t1', shape=[100], dtype='float32')
tensor2 = fluid.data(name='t2', shape=[100], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2)
self.assertEqual(out.shape, input.shape)
def test_addcmul_has_out(self):
program = Program()
with program_guard(program):
input = fluid.data(name='in', shape=[4, 100], dtype='float32')
tensor1 = fluid.data(name='t1', shape=[100], dtype='float32')
tensor2 = fluid.data(name='t2', shape=[100], dtype='float32')
out = fluid.data(name='out', shape=[4, 100], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2, out=out)
self.assertEqual(out.shape, input.shape)
class InvalidInputTest(unittest.TestCase):
def test_error(self):
def test_invalid_input():
program = Program()
with program_guard(program):
input = [20, 20]
tensor1 = fluid.data(
name='tensor1', shape=[20, 20], dtype='float32')
tensor2 = fluid.data(
name='tensor2', shape=[20, 20], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2)
self.assertRaises(TypeError, test_invalid_input)
def test_invalid_tensor1():
program = Program()
with program_guard(program):
input = fluid.data(
name='input', shape=[20, 20], dtype='float32')
tensor1 = [20, 20]
tensor2 = fluid.data(
name='tensor2', shape=[20, 20], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2)
self.assertRaises(TypeError, test_invalid_tensor1)
def test_invalid_tensor2():
program = Program()
with program_guard(program):
input = fluid.data(
name='input', shape=[20, 20], dtype='float32')
tensor1 = fluid.data(
name='tensor1', shape=[20, 20], dtype='float32')
tensor2 = [20, 20]
out = paddle.addcmul(input, tensor1, tensor2)
self.assertRaises(TypeError, test_invalid_tensor2)
def test_invalid_value_int():
program = Program()
with program_guard(program):
input = fluid.data(
name='input', shape=[20, 20], dtype='float32')
tensor1 = fluid.data(
name='tensor1', shape=[20, 20], dtype='float32')
tensor2 = fluid.data(
name='tensor2', shape=[20, 20], dtype='float32')
out = paddle.addcmul(input, tensor1, tensor2, value=1)
self.assertRaises(TypeError, test_invalid_value_int)
def test_invalid_value_float():
program = Program()
with program_guard(program):
input = fluid.data(name='input', shape=[20, 20], dtype='int32')
tensor1 = fluid.data(
name='tensor1', shape=[20, 20], dtype='int32')
tensor2 = fluid.data(
name='tensor2', shape=[20, 20], dtype='int32')
out = paddle.addcmul(input, tensor1, tensor2, value=1.0)
self.assertRaises(TypeError, test_invalid_value_float)
if __name__ == '__main__':
unittest.main()
......@@ -121,7 +121,7 @@ from .math import logsumexp #DEFINE_ALIAS
# from .math import inverse #DEFINE_ALIAS
from .math import log1p #DEFINE_ALIAS
# from .math import erf #DEFINE_ALIAS
# from .math import addcmul #DEFINE_ALIAS
from .math import addcmul #DEFINE_ALIAS
from .math import addmm #DEFINE_ALIAS
# from .attribute import rank #DEFINE_ALIAS
# from .attribute import shape #DEFINE_ALIAS
......
......@@ -75,7 +75,7 @@ __all__ = [
# 'inverse',
'log1p',
# 'erf',
# 'addcmul',
'addcmul',
'addmm'
]
# yapf: enable.
......@@ -1255,3 +1255,54 @@ def log1p(x, out=None, name=None):
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type="log1p", inputs={"X": x}, outputs={"Out": out})
return out
def addcmul(input, tensor1, tensor2, value=1.0, out=None, name=None):
"""
Calculate the element-wise multiplication of tensor1 and tensor2,
then multiply the result by value, and add it to input. The shape of input,
tensor1, tensor2 should be broadcastable.
The equation is:
.. math::
out = input + value * tensor1 * tensor2
Args:
input(Variable): The input to be added. A Tensor with type float32, float64, int32, int64.
tensor1(Variable): The tensor to be multiplied. A Tensor with type float32, float64, int32, int64.
tensor2(Variable): The tensor to be multiplied. A Tensor with type float32, float64, int32, int64.
value(int|float): The multiplier for tensor1*tensor2. For float32 and float64 type input, value must be float, otherwise an integer.
out(Variable, Optional): The variable that specifies the output of the
operator, which can be Variable that has been created in the
program. The default value is None, and a new Variable will be
created to save the output. Default: None.
name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
Generally, no setting is required. Default: None.
Returns:
out(Variable): The output result. A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
input = fluid.data(name='input', dtype='float32', shape=[3, 4])
tensor1 = fluid.data(name='tenosr1', dtype='float32', shape=[1, 4])
tensor2 = fluid.data(name='tensor2', dtype='float32', shape=[3, 4])
data = paddle.addcmul(input, tensor1, tensor2, value=1.0)
"""
check_variable_and_dtype(input, 'input', ['float32', 'float64', 'int32', 'int64'], 'addcmul')
check_variable_and_dtype(tensor1, 'tensor1', ['float32', 'float64', 'int32', 'int64'], 'addcmul')
check_variable_and_dtype(tensor2, 'tensor2', ['float32', 'float64', 'int32', 'int64'], 'addcmul')
if convert_dtype(input.dtype) in ['float32', 'float64']:
check_type(value, 'value', float, 'addcmul')
if convert_dtype(input.dtype) in ['int32', 'int64']:
check_type(value, 'value', int, 'addcmul')
if out is not None:
layers.assign(layers.elementwise_add(input, layers.elementwise_mul(tensor1, tensor2) * value), out)
else:
out = layers.elementwise_add(input, layers.elementwise_mul(tensor1, tensor2) * value)
return out
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