[Cherry-pick Release/2.0] Add addcmul, test=release/2.0 (#23894)

* add addcmul, test=release/2.0

python/paddle/__init__.py

@@ -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

python/paddle/fluid/tests/unittests/test_addcmul.py

+#  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()

python/paddle/tensor/__init__.py

@@ -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

python/paddle/tensor/math.py

@@ -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