Add the max, min, maximum, minimum api for the API 2.0

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -82,7 +82,13 @@ class ElementwiseOp : public framework::OperatorWithKernel {
       auto y_dims = ctx->GetInputDim("Y");
       int max_dim = std::max(x_dims.size(), y_dims.size());
       int axis = ctx->Attrs().Get<int>("axis");
-      axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
+      PADDLE_ENFORCE_EQ((axis >= (-1 * max_dim)) && (axis < max_dim), true,
+                        platform::errors::InvalidArgument(
+                            "The axis range must be [%s, %s), but axis is %s. "
+                            "Please set the axis again.",
+                            -1 * max_dim, max_dim, axis));
+      axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
+                       : axis);
       std::vector<int> x_dims_array(max_dim);
       std::vector<int> y_dims_array(max_dim);
       std::vector<int> out_dims_array(max_dim);
@@ -132,8 +138,7 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
                  "Y.dimension must be a subsequence of x.dimension. And axis "
                  "is the start dimension index "
                  "for broadcasting Y onto X. ")
-        .SetDefault(-1)
-        .EqualGreaterThan(-1);
+        .SetDefault(-1);
     AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
         .SetDefault(false);
     AddAttr<std::string>("x_data_format", "This parameter is no longer used.")

python/paddle/__init__.py

@@ -134,8 +134,6 @@
 from .tensor.math import elementwise_add  #DEFINE_ALIAS
 from .tensor.math import elementwise_div  #DEFINE_ALIAS
 from .tensor.math import elementwise_floordiv  #DEFINE_ALIAS
-from .tensor.math import elementwise_max  #DEFINE_ALIAS
-from .tensor.math import elementwise_min  #DEFINE_ALIAS
 from .tensor.math import elementwise_mod  #DEFINE_ALIAS
 from .tensor.math import elementwise_pow  #DEFINE_ALIAS
 from .tensor.math import elementwise_sub  #DEFINE_ALIAS
@@ -164,7 +162,9 @@
 from .tensor.math import tanh  #DEFINE_ALIAS
 from .tensor.math import elementwise_sum  #DEFINE_ALIAS
 from .tensor.math import max  #DEFINE_ALIAS
+from .tensor.math import maximum  #DEFINE_ALIAS
 from .tensor.math import min  #DEFINE_ALIAS
+from .tensor.math import minimum  #DEFINE_ALIAS
 from .tensor.math import mm  #DEFINE_ALIAS
 from .tensor.math import div  #DEFINE_ALIAS
 from .tensor.math import multiply  #DEFINE_ALIAS

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

@@ -0,0 +1,69 @@
+# 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
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+
+
+class ApiMaxTest(unittest.TestCase):
+    def setUp(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+    def test_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data = paddle.nn.data("data", shape=[10, 10], dtype="float32")
+            result_max = paddle.max(x=data, axis=1)
+            exe = paddle.static.Executor(self.place)
+            input_data = np.random.rand(10, 10).astype(np.float32)
+            res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
+        self.assertEqual((res == np.max(input_data, axis=1)).all(), True)
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
+            result_max = paddle.max(x=data, axis=0)
+            exe = paddle.static.Executor(self.place)
+            input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
+            res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
+        self.assertEqual((res == np.max(input_data, axis=0)).all(), True)
+
+    def test_errors(self):
+        paddle.enable_static()
+
+        def test_input_type():
+            with paddle.static.program_guard(paddle.static.Program(),
+                                             paddle.static.Program()):
+                data = np.random.rand(10, 10)
+                result_max = paddle.max(x=data, axis=0)
+
+        self.assertRaises(TypeError, test_input_type)
+
+    def test_imperative_api(self):
+        paddle.disable_static()
+        np_x = np.array([10, 10]).astype('float64')
+        x = paddle.to_variable(np_x)
+        z = paddle.max(x, axis=0)
+        np_z = z.numpy()
+        z_expected = np.array(np.max(np_x, axis=0))
+        self.assertEqual((np_z == z_expected).all(), True)

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

@@ -0,0 +1,80 @@
+# 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
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+
+
+class ApiMaximumTest(unittest.TestCase):
+    def setUp(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+        self.input_x = np.random.rand(10, 15).astype("float32")
+        self.input_y = np.random.rand(10, 15).astype("float32")
+        self.input_z = np.random.rand(15).astype("float32")
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
+            data_y = paddle.nn.data("y", shape=[10, 15], dtype="float32")
+            result_max = paddle.maximum(data_x, data_y)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"x": self.input_x,
+                                 "y": self.input_y},
+                           fetch_list=[result_max])
+        self.assertEqual((res == np.maximum(self.input_x, self.input_y)).all(),
+                         True)
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
+            data_z = paddle.nn.data("z", shape=[15], dtype="float32")
+            result_max = paddle.maximum(data_x, data_z, axis=1)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"x": self.input_x,
+                                 "z": self.input_z},
+                           fetch_list=[result_max])
+        self.assertEqual((res == np.maximum(self.input_x, self.input_z)).all(),
+                         True)
+
+    def test_dynamic_api(self):
+        paddle.disable_static()
+        np_x = np.array([10, 10]).astype('float64')
+        x = paddle.to_variable(self.input_x)
+        y = paddle.to_variable(self.input_y)
+        z = paddle.maximum(x, y)
+        np_z = z.numpy()
+        z_expected = np.array(np.maximum(self.input_x, self.input_y))
+        self.assertEqual((np_z == z_expected).all(), True)
+
+    def test_broadcast_axis(self):
+        paddle.disable_static()
+        np_x = np.random.rand(5, 4, 3, 2).astype("float64")
+        np_y = np.random.rand(4, 3).astype("float64")
+
+        x = paddle.to_variable(self.input_x)
+        y = paddle.to_variable(self.input_y)
+        result_1 = paddle.maximum(x, y, axis=1)
+        result_2 = paddle.maximum(x, y, axis=-2)
+        self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)

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

@@ -0,0 +1,69 @@
+# 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
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+
+
+class ApiMinTest(unittest.TestCase):
+    def setUp(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+    def test_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data = paddle.nn.data("data", shape=[10, 10], dtype="float32")
+            result_min = paddle.min(x=data, axis=1)
+            exe = paddle.static.Executor(self.place)
+            input_data = np.random.rand(10, 10).astype(np.float32)
+            res, = exe.run(feed={"data": input_data}, fetch_list=[result_min])
+        self.assertEqual((res == np.min(input_data, axis=1)).all(), True)
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data = paddle.nn.data("data", shape=[10, 10], dtype="int64")
+            result_min = paddle.min(x=data, axis=0)
+            exe = paddle.static.Executor(self.place)
+            input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
+            res, = exe.run(feed={"data": input_data}, fetch_list=[result_min])
+        self.assertEqual((res == np.min(input_data, axis=0)).all(), True)
+
+    def test_errors(self):
+        paddle.enable_static()
+
+        def test_input_type():
+            with paddle.static.program_guard(paddle.static.Program(),
+                                             paddle.static.Program()):
+                data = np.random.rand(10, 10)
+                result_min = paddle.min(x=data, axis=0)
+
+        self.assertRaises(TypeError, test_input_type)
+
+    def test_imperative_api(self):
+        paddle.disable_static()
+        np_x = np.array([10, 10]).astype('float64')
+        x = paddle.to_variable(np_x)
+        z = paddle.min(x, axis=0)
+        np_z = z.numpy()
+        z_expected = np.array(np.min(np_x, axis=0))
+        self.assertEqual((np_z == z_expected).all(), True)

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

@@ -0,0 +1,80 @@
+# 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
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+
+
+class ApiMinimumTest(unittest.TestCase):
+    def setUp(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+        self.input_x = np.random.rand(10, 15).astype("float32")
+        self.input_y = np.random.rand(10, 15).astype("float32")
+        self.input_z = np.random.rand(15).astype("float32")
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
+            data_y = paddle.nn.data("y", shape=[10, 15], dtype="float32")
+            result_min = paddle.minimum(data_x, data_y)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"x": self.input_x,
+                                 "y": self.input_y},
+                           fetch_list=[result_min])
+        self.assertEqual((res == np.minimum(self.input_x, self.input_y)).all(),
+                         True)
+
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            data_x = paddle.nn.data("x", shape=[10, 15], dtype="float32")
+            data_z = paddle.nn.data("z", shape=[15], dtype="float32")
+            result_min = paddle.minimum(data_x, data_z, axis=1)
+            exe = paddle.static.Executor(self.place)
+            res, = exe.run(feed={"x": self.input_x,
+                                 "z": self.input_z},
+                           fetch_list=[result_min])
+        self.assertEqual((res == np.minimum(self.input_x, self.input_z)).all(),
+                         True)
+
+    def test_dynamic_api(self):
+        paddle.disable_static()
+        np_x = np.array([10, 10]).astype('float64')
+        x = paddle.to_variable(self.input_x)
+        y = paddle.to_variable(self.input_y)
+        z = paddle.minimum(x, y)
+        np_z = z.numpy()
+        z_expected = np.array(np.minimum(self.input_x, self.input_y))
+        self.assertEqual((np_z == z_expected).all(), True)
+
+    def test_broadcast_axis(self):
+        paddle.disable_static()
+        np_x = np.random.rand(5, 4, 3, 2).astype("float64")
+        np_y = np.random.rand(4, 3).astype("float64")
+
+        x = paddle.to_variable(self.input_x)
+        y = paddle.to_variable(self.input_y)
+        result_1 = paddle.minimum(x, y, axis=1)
+        result_2 = paddle.minimum(x, y, axis=-2)
+        self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)