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未验证 提交 dc8bb76c 编写于 作者: W Wei Shengyu 提交者： GitHub
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remove addcmul (#28937) 

* remove addcmul

* remove unittest and other related code of addcmul

* fix bug

* fix merge conflict
上级 f459dd96
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python/paddle/__init__.py
浏览文件 @ dc8bb76c
......@@ -187,7 +187,6 @@ 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 addmm #DEFINE_ALIAS
from .tensor.math import clip #DEFINE_ALIAS
from .tensor.math import trace #DEFINE_ALIAS
......
python/paddle/fluid/tests/unittests/test_addcmul.py 已删除 100644 → 0
浏览文件 @ f459dd96
# 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.tensor.math.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.tensor.math.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.tensor.math.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.tensor.math.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.tensor.math.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.tensor.math.addcmul(input, tensor1, tensor2)
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.tensor.math.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.tensor.math.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.tensor.math.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.tensor.math.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.tensor.math.addcmul(input, tensor1, tensor2, value=1.0)
self.assertRaises(TypeError, test_invalid_value_float)
if __name__ == '__main__':
unittest.main()
python/paddle/tensor/__init__.py
浏览文件 @ dc8bb76c
......@@ -156,7 +156,6 @@ from .math import log2 #DEFINE_ALIAS
from .math import log10 #DEFINE_ALIAS
from .math import log1p #DEFINE_ALIAS
from .math import erf #DEFINE_ALIAS
# from .math import addcmul #DEFINE_ALIAS
from .math import addmm #DEFINE_ALIAS
from .math import clip #DEFINE_ALIAS
from .math import trace #DEFINE_ALIAS
......
python/paddle/tensor/math.py
浏览文件 @ dc8bb76c
......@@ -117,7 +117,6 @@ __all__ = [
'inverse',
'log1p',
'erf',
'addcmul',
'addmm',
'clip',
'trace',
......@@ -283,7 +282,7 @@ def add(x, y, name=None):
def subtract(x, y, name=None):
"""
Substract two tensors element-wise. The equation is:
Substract two tensors element-wise. The equation is:
.. math::
out = x - y
......@@ -302,7 +301,7 @@ def subtract(x, y, name=None):
Examples:
.. code-block:: python
import numpy as np
import paddle
......@@ -517,7 +516,7 @@ def multiply(x, y, name=None):
def maximum(x, y, name=None):
"""
Compare two tensors and returns a new tensor containing the element-wise maxima. The equation is:
Compare two tensors and returns a new tensor containing the element-wise maxima. The equation is:
.. math::
out = max(x, y)
......@@ -576,7 +575,7 @@ def maximum(x, y, name=None):
def minimum(x, y, name=None):
"""
Compare two tensors and returns a new tensor containing the element-wise minima. The equation is:
Compare two tensors and returns a new tensor containing the element-wise minima. The equation is:
.. math::
out = min(x, y)
......@@ -1174,7 +1173,7 @@ def max(x, axis=None, keepdim=False, name=None):
print(result1)
#[0.9]
result2 = paddle.max(x, axis=0)
print(result2)
print(result2)
#[0.2 0.3 0.6 0.9]
result3 = paddle.max(x, axis=-1)
print(result3)
......@@ -1268,7 +1267,7 @@ def min(x, axis=None, keepdim=False, name=None):
print(result2)
#[0.1 0.2 0.5 0.7]
result3 = paddle.min(x, axis=-1)
print(result3)
print(result3)
#[0.2 0.1]
result4 = paddle.min(x, axis=1, keepdim=True)
print(result4)
......@@ -1280,7 +1279,7 @@ def min(x, axis=None, keepdim=False, name=None):
y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]],
[[5.0, 6.0], [7.0, 8.0]]])
result5 = paddle.min(y, axis=[1, 2])
print(result5)
print(result5)
#[1. 5.]
result6 = paddle.min(y, axis=[0, 1])
print(result6)
......@@ -1454,50 +1453,6 @@ def log10(x, name=None):
return out
def addcmul(input, tensor1, tensor2, value=1.0, 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(Tensor): The input to be added. A Tensor with type float32, float64, int32, int64.
tensor1(Tensor): The tensor to be multiplied. A Tensor with type float32, float64, int32, int64.
tensor2(Tensor): 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.
name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
Generally, no setting is required. Default: None.
Returns:
out(Tensor): The output result. A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle
input = paddle.ones([2,2])
tensor1 = paddle.ones([2,2])
tensor2 = paddle.ones([2,2])
out = paddle.tensor.math.addcmul(input, tensor1, tensor2, value=0.5)
print(out)
# [[1.5 1.5]
# [1.5 1.5]]
"""
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')
out = layers.elementwise_add(input, layers.elementwise_mul(tensor1, tensor2) * value)
return out
def clip(x, min=None, max=None, name=None):
"""
This operator clip all elements in input into the range [ min, max ] and return
......
tools/static_mode_white_list.py
浏览文件 @ dc8bb76c
......@@ -48,7 +48,6 @@ STATIC_MODE_TESTING_LIST = [
'test_adaptive_max_pool1d',
'test_add_position_encoding_op',
'test_add_reader_dependency',
'test_addcmul',
'test_addmm_op',
'test_affine_grid_op',
'test_allclose_layer',
......
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