【PaddlePaddle Hackathon 第四期】No.6：为 Paddle 新增 ldexp API

python/paddle/__init__.py

@@ -301,6 +301,7 @@
 from .tensor.math import sgn  # noqa: F401
 from .tensor.math import take  # noqa: F401
 from .tensor.math import frexp  # noqa: F401
+from .tensor.math import ldexp  # noqa: F401
 from .tensor.math import trapezoid  # noqa: F401
 from .tensor.math import cumulative_trapezoid  # noqa: F401
 from .tensor.math import vander  # noqa: F401
@@ -699,6 +700,7 @@
     'triu_indices',
     'take',
     'frexp',
+    'ldexp',
     'trapezoid',
     'cumulative_trapezoid',
     'polar',

python/paddle/tensor/__init__.py

@@ -251,6 +251,7 @@
 from .math import sgn  # noqa: F401
 from .math import take  # noqa: F401
 from .math import frexp  # noqa: F401
+from .math import ldexp  # noqa: F401
 from .math import trapezoid  # noqa: F401
 from .math import cumulative_trapezoid  # noqa: F401
 from .math import sigmoid  # noqa: F401
@@ -549,6 +550,7 @@
     'bucketize',
     'sgn',
     'frexp',
+    'ldexp',
     'trapezoid',
     'cumulative_trapezoid',
     'polar',

python/paddle/tensor/math.py

@@ -5785,3 +5785,55 @@ def polygamma(x, n, name=None):
                 attrs={'n': n},
             )
         return out
+
+
+def ldexp(x, y, name=None):
+    """
+    Compute the result of multiplying x by 2 to the power of y. The equation is:
+
+    .. math::
+        out = x * 2^{y}
+
+    Args:
+        x (Tensor): The input Tensor, the data type is float32, float64, int32 or int64.
+        y (Tensor):  A Tensor of exponents, typically integers.
+        name (str, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        out (Tensor): An N-D Tensor. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. And the data type is float32 or float64.
+
+    Examples:
+
+        ..  code-block:: python
+
+            import paddle
+
+            #example1
+            x = paddle.to_tensor([1, 2, 3], dtype='float32')
+            y = paddle.to_tensor([2, 3, 4], dtype='int32')
+            res = paddle.ldexp(x, y)
+            print(res)
+            # Tensor(shape=[3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #        [4., 16., 48.])
+
+            #example2
+            x = paddle.to_tensor([1, 2, 3], dtype='float32')
+            y = paddle.to_tensor([2], dtype='int32')
+            res = paddle.ldexp(x, y)
+            print(res)
+            # Tensor(shape=[3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+            #        [4., 8., 12.])
+
+    """
+    if not isinstance(x, (paddle.Tensor, Variable)):
+        raise TypeError(f"x must be tensor type, but got {type(x)}")
+    if not isinstance(y, (paddle.Tensor, Variable)):
+        raise TypeError(f"y must be tensor type, but got {type(y)}")
+    if x.dtype == paddle.float64 or y.dtype == paddle.float64:
+        out_dtype = paddle.float64
+    else:
+        out_dtype = paddle.get_default_dtype()
+    x = paddle.cast(x, dtype=out_dtype)
+    y = paddle.cast(y, dtype=out_dtype)
+    two = paddle.to_tensor(2, dtype=out_dtype)
+    return paddle.multiply(x, paddle.pow(two, y))

test/legacy_test/test_ldexp.py

@@ -0,0 +1,172 @@
+# 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.
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.fluid import core
+from paddle.static import Program, program_guard
+
+DYNAMIC = 1
+STATIC = 2
+
+
+def _run_ldexp(mode, x, y, device='cpu'):
+    # dynamic mode
+    if mode == DYNAMIC:
+        paddle.disable_static()
+        # Set device
+        paddle.set_device(device)
+        x_ = paddle.to_tensor(x)
+        # y is scalar
+        if isinstance(y, (int)):
+            y_ = y
+        # y is tensor
+        else:
+            y_ = paddle.to_tensor(y)
+        res = paddle.ldexp(x_, y_)
+        return res.numpy()
+    # static graph mode
+    elif mode == STATIC:
+        paddle.enable_static()
+        # y is scalar
+        if isinstance(y, (int)):
+            with program_guard(Program(), Program()):
+                x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
+                y_ = y
+                res = paddle.ldexp(x_, y_)
+                place = (
+                    paddle.CPUPlace()
+                    if device == 'cpu'
+                    else paddle.CUDAPlace(0)
+                )
+                exe = paddle.static.Executor(place)
+                outs = exe.run(feed={'x': x, 'y': y}, fetch_list=[res])
+                return outs[0]
+        # y is tensor
+        else:
+            with program_guard(Program(), Program()):
+                x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
+                y_ = paddle.static.data(name="y", shape=y.shape, dtype=y.dtype)
+                res = paddle.ldexp(x_, y_)
+                place = (
+                    paddle.CPUPlace()
+                    if device == 'cpu'
+                    else paddle.CUDAPlace(0)
+                )
+                exe = paddle.static.Executor(place)
+                outs = exe.run(feed={'x': x, 'y': y}, fetch_list=[res])
+                return outs[0]
+
+
+def check_dtype(input, desired_dtype):
+    if input.dtype != desired_dtype:
+        raise ValueError(
+            "The expected data type to be obtained is {}, but got {}".format(
+                desired_dtype, input.dtype
+            )
+        )
+
+
+class TestLdexpAPI(unittest.TestCase):
+    def setUp(self):
+        self.places = ['cpu']
+        if core.is_compiled_with_cuda():
+            self.places.append('gpu')
+
+    def test_ldexp(self):
+        np.random.seed(7)
+        for place in self.places:
+            # test 1-d float tensor and 1-d int tensor
+            dims = (np.random.randint(200, 300),)
+            x = (np.random.rand(*dims) * 10).astype(np.float64)
+            y = (np.random.randint(-10, 10, dims)).astype(np.int32)
+            res = _run_ldexp(DYNAMIC, x, y, place)
+            check_dtype(res, np.float64)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+            res = _run_ldexp(STATIC, x, y, place)
+            check_dtype(res, np.float64)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+
+            dims = (np.random.randint(200, 300),)
+            x = (np.random.rand(*dims) * 10).astype(np.float32)
+            y = (np.random.randint(-10, 10, dims)).astype(np.int32)
+            res = _run_ldexp(DYNAMIC, x, y, place)
+            check_dtype(res, np.float32)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+            res = _run_ldexp(STATIC, x, y, place)
+            check_dtype(res, np.float32)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+
+            # test 1-d int tensor and 1-d int tensor
+            dims = (np.random.randint(200, 300),)
+            x = (np.random.randint(-10, 10, dims)).astype(np.int64)
+            y = (np.random.randint(-10, 10, dims)).astype(np.int32)
+            res = _run_ldexp(DYNAMIC, x, y, place)
+            check_dtype(res, np.float32)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+            res = _run_ldexp(STATIC, x, y, place)
+            check_dtype(res, np.float32)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+
+            dims = (np.random.randint(200, 300),)
+            x = (np.random.randint(-10, 10, dims)).astype(np.int32)
+            y = (np.random.randint(-10, 10, dims)).astype(np.int32)
+            res = _run_ldexp(DYNAMIC, x, y, place)
+            check_dtype(res, np.float32)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+            res = _run_ldexp(STATIC, x, y, place)
+            check_dtype(res, np.float32)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+
+            # test broadcast
+            dims = (
+                np.random.randint(1, 10),
+                np.random.randint(5, 10),
+                np.random.randint(5, 10),
+            )
+            x = (np.random.rand(*dims) * 10).astype(np.float64)
+            y = (np.random.randint(-10, 10, dims[-1])).astype(np.int32)
+            res = _run_ldexp(DYNAMIC, x, y)
+            check_dtype(res, np.float64)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+            res = _run_ldexp(STATIC, x, y)
+            check_dtype(res, np.float64)
+            np.testing.assert_allclose(res, np.ldexp(x, y))
+
+
+class TestLdexpError(unittest.TestCase):
+    """TestLdexpError."""
+
+    def test_errors(self):
+        """test_errors."""
+        np.random.seed(7)
+
+        # test 1-d float and int tensor
+        dims = (np.random.randint(200, 300),)
+        x = (np.random.rand(*dims) * 10).astype(np.float64)
+        y = (np.random.randint(-10, 10, dims)).astype(np.int32)
+        self.assertRaises(TypeError, paddle.ldexp, x, paddle.to_tensor(y))
+
+        # test 1-d float tensor and int
+        dims = (np.random.randint(200, 300),)
+        x = (np.random.rand(*dims) * 10).astype(np.float64)
+        y = (np.random.randint(-10, 10, dims)).astype(np.int32)
+        self.assertRaises(TypeError, paddle.ldexp, paddle.to_tensor(x), y)
+
+
+if __name__ == '__main__':
+    unittest.main()