new API inner&outer

python/paddle/__init__.py

@@ -246,6 +246,8 @@
 from .tensor.math import angle  # noqa: F401
 from .tensor.math import fmax  # noqa: F401
 from .tensor.math import fmin  # noqa: F401
+from .tensor.math import inner  # noqa: F401
+from .tensor.math import outer  # noqa: F401
 
 from .tensor.random import multinomial  # noqa: F401
 from .tensor.random import standard_normal  # noqa: F401
@@ -495,6 +497,8 @@
            'lgamma',
            'lerp',
            'erfinv',
+           'inner',
+           'outer',
            'square',
            'divide',
            'ceil',

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

@@ -0,0 +1,166 @@
+# 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
+from paddle.static import Program, program_guard
+
+
+class TestMultiplyApi(unittest.TestCase):
+    def _run_static_graph_case(self, x_data, y_data):
+        with program_guard(Program(), Program()):
+            paddle.enable_static()
+            x = paddle.static.data(
+                name='x', shape=x_data.shape, dtype=x_data.dtype)
+            y = paddle.static.data(
+                name='y', shape=y_data.shape, dtype=y_data.dtype)
+            res = paddle.inner(x, y)
+
+            place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda(
+            ) else paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            outs = exe.run(paddle.static.default_main_program(),
+                           feed={'x': x_data,
+                                 'y': y_data},
+                           fetch_list=[res])
+            res = outs[0]
+            return res
+
+    def _run_dynamic_graph_case(self, x_data, y_data):
+        paddle.disable_static()
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        res = paddle.inner(x, y)
+        return res.numpy()
+
+    def test_multiply(self):
+        np.random.seed(7)
+
+        # test static computation graph: 3-d array
+        x_data = np.random.rand(2, 10, 10).astype(np.float64)
+        y_data = np.random.rand(2, 5, 10).astype(np.float64)
+        res = self._run_static_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test static computation graph: 2-d array
+        x_data = np.random.rand(200, 5).astype(np.float64)
+        y_data = np.random.rand(50, 5).astype(np.float64)
+        res = self._run_static_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test static computation graph: 1-d array
+        x_data = np.random.rand(50).astype(np.float64)
+        y_data = np.random.rand(50).astype(np.float64)
+        res = self._run_static_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test dynamic computation graph: 3-d array
+        x_data = np.random.rand(5, 10, 10).astype(np.float64)
+        y_data = np.random.rand(2, 10).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test dynamic computation graph: 2-d array
+        x_data = np.random.rand(20, 50).astype(np.float64)
+        y_data = np.random.rand(50).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test dynamic computation graph: Scalar
+        x_data = np.random.rand(20, 10).astype(np.float32)
+        y_data = np.random.rand(1).astype(np.float32).item()
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test dynamic computation graph: 2-d array Complex
+        x_data = np.random.rand(20,
+                                50).astype(np.float64) + 1J * np.random.rand(
+                                    20, 50).astype(np.float64)
+        y_data = np.random.rand(50).astype(np.float64) + 1J * np.random.rand(
+            50).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+        # test dynamic computation graph: 3-d array Complex
+        x_data = np.random.rand(5, 10,
+                                10).astype(np.float64) + 1J * np.random.rand(
+                                    5, 10, 10).astype(np.float64)
+        y_data = np.random.rand(2, 10).astype(np.float64) + 1J * np.random.rand(
+            2, 10).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.inner(x_data, y_data)))
+
+
+class TestMultiplyError(unittest.TestCase):
+    def test_errors(self):
+        # test static computation graph: dtype can not be int8
+        paddle.enable_static()
+        with program_guard(Program(), Program()):
+            x = paddle.static.data(name='x', shape=[100], dtype=np.int8)
+            y = paddle.static.data(name='y', shape=[100], dtype=np.int8)
+            self.assertRaises(TypeError, paddle.inner, x, y)
+
+        # test static computation graph: inputs must be broadcastable 
+        with program_guard(Program(), Program()):
+            x = paddle.static.data(name='x', shape=[20, 50], dtype=np.float64)
+            y = paddle.static.data(name='y', shape=[20], dtype=np.float64)
+            self.assertRaises(ValueError, paddle.inner, x, y)
+
+        np.random.seed(7)
+        # test dynamic computation graph: dtype can not be int8
+        paddle.disable_static()
+        x_data = np.random.randn(200).astype(np.int8)
+        y_data = np.random.randn(200).astype(np.int8)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(RuntimeError, paddle.inner, x, y)
+
+        # test dynamic computation graph: inputs must be broadcastable
+        x_data = np.random.rand(20, 5)
+        y_data = np.random.rand(10, 2)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(ValueError, paddle.inner, x, y)
+
+        # test dynamic computation graph: dtype must be same	
+        x_data = np.random.randn(200).astype(np.float32)
+        y_data = np.random.randn(200).astype(np.float64)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(ValueError, paddle.inner, x, y)
+
+        # test dynamic computation graph: dtype must be Tensor type
+        x_data = np.random.randn(200).astype(np.float64)
+        y_data = np.random.randn(200).astype(np.float64)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(ValueError, paddle.inner, x_data, y)
+
+        # test dynamic computation graph: dtype must be Tensor type
+        x_data = np.random.randn(200).astype(np.float64)
+        y_data = np.random.randn(200).astype(np.float64)
+        x = paddle.to_tensor(x_data)
+        self.assertRaises(ValueError, paddle.inner, x, y_data)
+
+        # test dynamic computation graph: dtype must be Tensor type
+        x_data = np.random.randn(200).astype(np.float32)
+        y_data = np.random.randn(200).astype(np.float32)
+        self.assertRaises(ValueError, paddle.inner, x_data, y_data)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -0,0 +1,153 @@
+# 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
+from paddle.static import Program, program_guard
+
+
+class TestMultiplyApi(unittest.TestCase):
+    def _run_static_graph_case(self, x_data, y_data):
+        with program_guard(Program(), Program()):
+            paddle.enable_static()
+            x = paddle.static.data(
+                name='x', shape=x_data.shape, dtype=x_data.dtype)
+            y = paddle.static.data(
+                name='y', shape=y_data.shape, dtype=y_data.dtype)
+            res = paddle.outer(x, y)
+
+            place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda(
+            ) else paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            outs = exe.run(paddle.static.default_main_program(),
+                           feed={'x': x_data,
+                                 'y': y_data},
+                           fetch_list=[res])
+            res = outs[0]
+            return res
+
+    def _run_dynamic_graph_case(self, x_data, y_data):
+        paddle.disable_static()
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        res = paddle.outer(x, y)
+        return res.numpy()
+
+    def test_multiply(self):
+        np.random.seed(7)
+
+        # test static computation graph: 3-d array
+        x_data = np.random.rand(2, 10, 10).astype(np.float64)
+        y_data = np.random.rand(2, 5, 10).astype(np.float64)
+        res = self._run_static_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+        # test static computation graph: 2-d array
+        x_data = np.random.rand(200, 5).astype(np.float64)
+        y_data = np.random.rand(50, 5).astype(np.float64)
+        res = self._run_static_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+        # test static computation graph: 1-d array
+        x_data = np.random.rand(50).astype(np.float64)
+        y_data = np.random.rand(50).astype(np.float64)
+        res = self._run_static_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+        # test dynamic computation graph: 3-d array
+        x_data = np.random.rand(5, 10, 10).astype(np.float64)
+        y_data = np.random.rand(2, 10).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+        # test dynamic computation graph: 2-d array
+        x_data = np.random.rand(20, 50).astype(np.float64)
+        y_data = np.random.rand(50).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+        # test dynamic computation graph: Scalar
+        x_data = np.random.rand(20, 10).astype(np.float32)
+        y_data = np.random.rand(1).astype(np.float32).item()
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data), rtol=1e4))
+
+        # test dynamic computation graph: 2-d array Complex
+        x_data = np.random.rand(20,
+                                50).astype(np.float64) + 1J * np.random.rand(
+                                    20, 50).astype(np.float64)
+        y_data = np.random.rand(50).astype(np.float64) + 1J * np.random.rand(
+            50).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+        # test dynamic computation graph: 3-d array Complex
+        x_data = np.random.rand(5, 10,
+                                10).astype(np.float64) + 1J * np.random.rand(
+                                    5, 10, 10).astype(np.float64)
+        y_data = np.random.rand(2, 10).astype(np.float64) + 1J * np.random.rand(
+            2, 10).astype(np.float64)
+        res = self._run_dynamic_graph_case(x_data, y_data)
+        self.assertTrue(np.allclose(res, np.outer(x_data, y_data)))
+
+
+class TestMultiplyError(unittest.TestCase):
+    def test_errors(self):
+        # test static computation graph: dtype can not be int8
+        paddle.enable_static()
+        with program_guard(Program(), Program()):
+            x = paddle.static.data(name='x', shape=[100], dtype=np.int8)
+            y = paddle.static.data(name='y', shape=[100], dtype=np.int8)
+            self.assertRaises(TypeError, paddle.outer, x, y)
+
+        np.random.seed(7)
+        # test dynamic computation graph: dtype can not be int8
+        paddle.disable_static()
+        x_data = np.random.randn(200).astype(np.int8)
+        y_data = np.random.randn(200).astype(np.int8)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(RuntimeError, paddle.outer, x, y)
+
+        # test dynamic computation graph: dtype must be same	
+        x_data = np.random.randn(200).astype(np.float32)
+        y_data = np.random.randn(200).astype(np.float64)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(ValueError, paddle.outer, x, y)
+
+        # test dynamic computation graph: dtype must be Tensor type
+        x_data = np.random.randn(200).astype(np.float64)
+        y_data = np.random.randn(200).astype(np.float64)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(ValueError, paddle.outer, x_data, y)
+
+        # test dynamic computation graph: dtype must be Tensor type
+        x_data = np.random.randn(200).astype(np.float32)
+        y_data = np.random.randn(200).astype(np.float32)
+        x = paddle.to_tensor(x_data)
+        self.assertRaises(ValueError, paddle.outer, x, y_data)
+
+        # test dynamic computation graph: dtype must be Tensor type
+        x_data = np.random.randn(200).astype(np.float32)
+        y_data = np.random.randn(200).astype(np.float32)
+        self.assertRaises(ValueError, paddle.outer, x_data, y_data)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -213,6 +213,8 @@
 from .math import angle  # noqa: F401
 from .math import fmax  # noqa: F401
 from .math import fmin  # noqa: F401
+from .math import inner  # noqa: F401
+from .math import outer  # noqa: F401
 
 from .random import multinomial  # noqa: F401
 from .random import standard_normal  # noqa: F401
@@ -319,6 +321,8 @@
            'fmax',
            'fmin',
            'mm',
+           'inner',
+           'outer',
            'divide',
            'floor_divide',
            'remainder',