Update `paddle.clamp` (#25906)

* Update `paddle.clamp`

rename to `paddle.clip`
add fast path for dygraph mode
remove `out`
rename `input` -> `x`
update doc sample

* Fix leftover `Variable` wording

* Indent doc with spaces

* Remove `:alias` in docs

* Update `enable_imperative` -> `disable_static`

* Remove `imperative`

also trigger CI

* Update tests for better coverage

* Rebase to fix `cosine_similarity`

* Fix `cosine_similarity` some more

python/paddle/__init__.py

@@ -181,7 +181,7 @@ 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 clamp  #DEFINE_ALIAS
+from .tensor.math import clip  #DEFINE_ALIAS
 from .tensor.math import trace  #DEFINE_ALIAS
 from .tensor.math import kron  #DEFINE_ALIAS
 from .tensor.math import prod  #DEFINE_ALIAS

python/paddle/fluid/layers/nn.py

@@ -12205,8 +12205,6 @@ def logical_not(x, out=None, name=None):
 @templatedoc()
 def clip(x, min, max, name=None):
     """
-    :alias_main: paddle.nn.clip
-	:alias: paddle.nn.clip,paddle.nn.clip.clip
 	:old_api: paddle.fluid.layers.clip
 
     ${comment}

python/paddle/fluid/tests/unittests/test_clamp.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 paddle.tensor as tensor
-import paddle.fluid as fluid
-import numpy as np
-import unittest
-
-
-class TestClampAPI(unittest.TestCase):
-    def test_dygraph_clamp(self):
-        in1 = np.array([[1.2, 3.5], [4.5, 6.4]]).astype('float32')
-        with fluid.dygraph.guard():
-            x1 = fluid.dygraph.to_variable(in1)
-            out1 = tensor.clamp(x1, min=3.5, max=5.0)
-            out2 = tensor.clamp(x1, min=2.5)
-            self.assertTrue(
-                np.allclose(
-                    out1.numpy(), in1.clip(
-                        min=3.5, max=5.0)))
-            self.assertTrue(np.allclose(out2.numpy(), in1.clip(min=2.5)))
-
-    def test_clamp(self):
-        data_shape = [1, 9, 9, 4]
-        data = np.random.random(data_shape).astype('float32')
-        images = fluid.data(name='image', shape=data_shape, dtype='float32')
-        min = fluid.data(name='min', shape=[1], dtype='float32')
-        max = fluid.data(name='max', shape=[1], dtype='float32')
-
-        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
-        exe = fluid.Executor(place)
-
-        out_1 = tensor.clamp(images, min=min, max=max)
-        out_2 = tensor.clamp(images, min=0.2, max=0.9)
-        out_3 = tensor.clamp(images, min=0.3)
-        out_4 = tensor.clamp(images, max=0.7)
-        out_5 = tensor.clamp(images, min=min)
-        out_6 = tensor.clamp(images, max=max)
-
-        res1, res2, res3, res4, res5, res6 = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "image": data,
-                "min": np.array([0.2]).astype('float32'),
-                "max": np.array([0.8]).astype('float32')
-            },
-            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6])
-
-        self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
-        self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
-        self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
-        self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
-        self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
-        self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
-
-
-class TestClampError(unittest.TestCase):
-    def test_errors(self):
-        x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16")
-        x2 = fluid.layers.data(name='x2', shape=[1], dtype="int8")
-        self.assertRaises(TypeError, tensor.clamp, x=x1, min=0.2, max=0.8)
-        self.assertRaises(TypeError, tensor.clamp, x=x2, min=0.2, max=0.8)
-
-
-if __name__ == '__main__':
-    unittest.main()

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

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import unittest
 import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 from op_test import OpTest
@@ -109,5 +110,64 @@ class TestClipOpError(unittest.TestCase):
             self.assertRaises(TypeError, test_dtype)
 
 
+class TestClipAPI(unittest.TestCase):
+    def test_clip(self):
+        data_shape = [1, 9, 9, 4]
+        data = np.random.random(data_shape).astype('float32')
+        images = fluid.data(name='image', shape=data_shape, dtype='float32')
+        min = fluid.data(name='min', shape=[1], dtype='float32')
+        max = fluid.data(name='max', shape=[1], dtype='float32')
+
+        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        out_1 = paddle.clip(images, min=min, max=max)
+        out_2 = paddle.clip(images, min=0.2, max=0.9)
+        out_3 = paddle.clip(images, min=0.3)
+        out_4 = paddle.clip(images, max=0.7)
+        out_5 = paddle.clip(images, min=min)
+        out_6 = paddle.clip(images, max=max)
+
+        res1, res2, res3, res4, res5, res6 = exe.run(
+            fluid.default_main_program(),
+            feed={
+                "image": data,
+                "min": np.array([0.2]).astype('float32'),
+                "max": np.array([0.8]).astype('float32')
+            },
+            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6])
+
+        self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
+        self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
+        self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
+        self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
+        self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
+        self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
+
+    def test_clip_dygraph(self):
+        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        paddle.disable_static(place)
+        data_shape = [1, 9, 9, 4]
+        data = np.random.random(data_shape).astype('float32')
+        images = paddle.to_variable(data, dtype='float32')
+
+        out_1 = paddle.clip(images, min=0.2, max=0.8)
+        out_2 = paddle.clip(images, min=0.2, max=0.9)
+
+        self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8)))
+        self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9)))
+
+    def test_errors(self):
+        paddle.enable_static()
+        x1 = fluid.data(name='x1', shape=[1], dtype="int16")
+        x2 = fluid.data(name='x2', shape=[1], dtype="int8")
+        x3 = fluid.data(name='x3', shape=[1], dtype="float32")
+        self.assertRaises(TypeError, paddle.clip, x=x1, min=0.2, max=0.8)
+        self.assertRaises(TypeError, paddle.clip, x=x2, min=0.2, max=0.8)
+        self.assertRaises(Exception, paddle.clip, x=x3)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -29,10 +29,10 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         if core.is_compiled_with_cuda():
             self.places.append(paddle.CUDAPlace(0))
 
-    def _get_numpy_out(self, x1, x2, dim=1, eps=1e-8):
-        w12 = np.sum(x1 * x2, axis=dim)
-        w1 = np.sum(x1 * x1, axis=dim)
-        w2 = np.sum(x2 * x2, axis=dim)
+    def _get_numpy_out(self, x1, x2, axis=1, eps=1e-8):
+        w12 = np.sum(x1 * x2, axis=axis)
+        w1 = np.sum(x1 * x1, axis=axis)
+        w2 = np.sum(x2 * x2, axis=axis)
         n12 = np.sqrt(np.clip(w1 * w2, eps * eps, None))
         cos_sim = w12 / n12
         return cos_sim
@@ -42,7 +42,7 @@ class TestCosineSimilarityAPI(unittest.TestCase):
 
         with program_guard(Program(), Program()):
             shape = [10, 15]
-            dim = 1
+            axis = 1
             eps = 1e-8
             np.random.seed(0)
             np_x1 = np.random.rand(*shape).astype(np.float32)
@@ -50,14 +50,14 @@ class TestCosineSimilarityAPI(unittest.TestCase):
 
             x1 = paddle.data(name="x1", shape=shape)
             x2 = paddle.data(name="x2", shape=shape)
-            result = F.cosine_similarity(x1, x2, dim=dim, eps=eps)
+            result = F.cosine_similarity(x1, x2, axis=axis, eps=eps)
             exe = Executor(place)
             fetches = exe.run(default_main_program(),
                               feed={"x1": np_x1,
                                     "x2": np_x2},
                               fetch_list=[result])
 
-            np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+            np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
             self.assertTrue(np.allclose(fetches[0], np_out))
 
     def test_static(self):
@@ -68,16 +68,16 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         paddle.disable_static()
 
         shape = [10, 15]
-        dim = 1
+        axis = 1
         eps = 1e-8
         np.random.seed(1)
         np_x1 = np.random.rand(*shape).astype(np.float32)
         np_x2 = np.random.rand(*shape).astype(np.float32)
-        np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+        np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
         tesnor_x1 = paddle.to_variable(np_x1)
         tesnor_x2 = paddle.to_variable(np_x2)
-        y = F.cosine_similarity(tesnor_x1, tesnor_x2, dim=dim, eps=eps)
+        y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))
 
@@ -85,16 +85,16 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         paddle.disable_static()
 
         shape = [12, 13]
-        dim = 0
+        axis = 0
         eps = 1e-6
         np.random.seed(1)
         np_x1 = np.random.rand(*shape).astype(np.float32)
         np_x2 = np.random.rand(*shape).astype(np.float32)
-        np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+        np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
         tesnor_x1 = paddle.to_variable(np_x1)
         tesnor_x2 = paddle.to_variable(np_x2)
-        y = F.cosine_similarity(tesnor_x1, tesnor_x2, dim=dim, eps=eps)
+        y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))
 
@@ -103,16 +103,16 @@ class TestCosineSimilarityAPI(unittest.TestCase):
 
         shape1 = [10, 12, 10]
         shape2 = [10, 1, 10]
-        dim = 2
+        axis = 2
         eps = 1e-6
         np.random.seed(1)
         np_x1 = np.random.rand(*shape1).astype(np.float32)
         np_x2 = np.random.rand(*shape2).astype(np.float32)
-        np_out = self._get_numpy_out(np_x1, np_x2, dim=dim, eps=eps)
+        np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
         tesnor_x1 = paddle.to_variable(np_x1)
         tesnor_x2 = paddle.to_variable(np_x2)
-        y = F.cosine_similarity(tesnor_x1, tesnor_x2, dim=dim, eps=eps)
+        y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))
 

python/paddle/nn/functional/common.py

@@ -28,9 +28,9 @@ from ...fluid.layers import assign  #DEFINE_ALIAS
 from ...fluid.layers import squeeze  #DEFINE_ALIAS
 from ...fluid.layers import unsqueeze  #DEFINE_ALIAS
 from ...fluid.layers import elementwise_mul  #DEFINE_ALIAS
-from ...tensor import clamp  #DEFINE_ALIAS
-from ...tensor import sum  #DEFINE_ALIAS
-from ...tensor import sqrt  #DEFINE_ALIAS
+from ...tensor import clip
+from ...tensor import sum
+from ...tensor import sqrt
 
 #from ...fluid.layers import fc  #DEFINE_ALIAS
 from ...fluid.layers import pad_constant_like  #DEFINE_ALIAS
@@ -635,17 +635,17 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
     return out
 
 
-def cosine_similarity(x1, x2, dim=1, eps=1e-8):
+def cosine_similarity(x1, x2, axis=1, eps=1e-8):
     """
-    Compute cosine similarity between x1 and x2 along dim.
+    Compute cosine similarity between x1 and x2 along axis.
 
     Parameters:
         x1 (Tensor): First input. float32/double.
         x2 (Tensor): Second input. float32/double.
-        dim (int): Dimension of vectors to compute cosine similarity. Default is 1.
+        axis (int): Dimension of vectors to compute cosine similarity. Default is 1.
         eps(float): Small value to avoid division by zero. Default is 1e-8.
                     
-    Returns: a Tensor representing cosine similarity between x1 and x2 along dim.
+    Returns: a Tensor representing cosine similarity between x1 and x2 along axis.
     Return Type: Tensor
 
     Examples:
@@ -659,7 +659,7 @@ def cosine_similarity(x1, x2, dim=1, eps=1e-8):
                      [0.9098952  0.15715368 0.8671125  0.3156102 ]
                      [0.4427798  0.54136837 0.5276275  0.32394758]
                      [0.3769419  0.8535014  0.48041078 0.9256797 ]]
-                dim = 1
+                axis = 1
                 eps = 1e-8
                 Out: [0.5275037  0.8368967  0.75037485 0.9245899]
 
@@ -675,14 +675,14 @@ def cosine_similarity(x1, x2, dim=1, eps=1e-8):
             x2 = np.random.rand(2,3)
             x1 = paddle.to_tensor(x1)
             x2 = paddle.to_tensor(x2)
-            result = paddle.nn.functional.cosine_similarity(x1, x2, dim=0)
+            result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0)
             print(result.numpy())
             # [0.99806249 0.9817672  0.94987036]
             
     """
-    w12 = sum(elementwise_mul(x1, x2), axis=dim)
-    w1 = sum(elementwise_mul(x1, x1), axis=dim)
-    w2 = sum(elementwise_mul(x2, x2), axis=dim)
-    n12 = sqrt(clamp(w1 * w2, min=eps * eps))
+    w12 = sum(elementwise_mul(x1, x2), axis=axis)
+    w1 = sum(elementwise_mul(x1, x1), axis=axis)
+    w2 = sum(elementwise_mul(x2, x2), axis=axis)
+    n12 = sqrt(clip(w1 * w2, min=eps * eps))
     cos_sim = w12 / n12
     return cos_sim

python/paddle/tensor/__init__.py

@@ -154,7 +154,7 @@ 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 clamp  #DEFINE_ALIAS
+from .math import clip  #DEFINE_ALIAS
 from .math import trace  #DEFINE_ALIAS
 from .math import kron  #DEFINE_ALIAS
 from .math import prod  #DEFINE_ALIAS

python/paddle/tensor/math.py

@@ -121,7 +121,7 @@ __all__ = [
         'erf',
         'addcmul',
         'addmm',
-        'clamp',
+        'clip',
         'trace',
         'kron'
 ]
@@ -1326,14 +1326,14 @@ def addcmul(input, tensor1, tensor2, value=1.0, name=None):
     return out
 
 
-def clamp(input, min=None, max=None, name=None):
+def clip(x, min=None, max=None, name=None):
     """
-	:alias_main: paddle.clamp
-	:alias: paddle.clamp,paddle.tensor.clamp,paddle.tensor.math.clamp
+        :alias_main: paddle.clip
+        :alias: paddle.clip,paddle.tensor.clip,paddle.tensor.math.clip
 
-    **clampe layer**
+    **clip layer**
 
-    This operator clamps all elements in input into the range [ min, max ] and return
+    This operator clip all elements in input into the range [ min, max ] and return
     a resulting tensor as the following equation:
 
     .. math::
@@ -1341,32 +1341,29 @@ def clamp(input, min=None, max=None, name=None):
         Out = MIN(MAX(x, min), max)
 
     Args:
-        input (Variable): An input N-D Tensor or LoDTensor
-            with data type float32, float64.
-        min (float32|Variable): The lower bound with type ``float32`` or a ``Tensor``
+        x (Tensor): An N-D Tensor with data type float32 or float64.
+        min (float32|Tensor): The lower bound with type ``float32`` or a ``Tensor``
             with shape [1] and type ``int32``, ``float32``, ``float64``.
-        max (float32|Variable): The upper bound with type ``float32`` or a ``Tensor``
+        max (float32|Tensor): The upper bound with type ``float32`` or a ``Tensor``
             with shape [1] and type ``int32``, ``float32``, ``float64``.
         name (str, optional): The default value is None. Normally there is no
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.
 
     Returns:
-        Variable: A Tensor or LodTensor with the same data type and data shape as input's.
+        Tensor: A Tensor with the same data type and data shape as input.
 
     Examples:
         .. code-block:: python
 
             import paddle
-            import paddle.fluid as fluid
             import numpy as np
 
-            in1 = np.array([[1.2,3.5],
-                            [4.5,6.4]]).astype('float32')
-            with fluid.dygraph.guard():
-                x1 = fluid.dygraph.to_variable(in1)
-                out1 = paddle.tensor.clamp(x1, min=3.5, max=5.0)
-                out2 = paddle.tensor.clamp(x1, min=2.5)
+            paddle.disable_static()
+            x = np.array([[1.2,3.5], [4.5,6.4]]).astype('float32')
+            x1 = paddle.to_variable(x)
+            out1 = paddle.clip(x1, min=3.5, max=5.0)
+            out2 = paddle.clip(x1, min=2.5)
             print(out1.numpy())
             # [[3.5, 3.5]
             # [4.5, 5.0]]
@@ -1380,20 +1377,22 @@ def clamp(input, min=None, max=None, name=None):
     if in_dygraph_mode():
         min = sys.float_info.min if min is None else min
         max = sys.float_info.max if max is None else max
-        return core.ops.clip(input, "min", min, "max", max)
+        return core.ops.clip(x, "min", min, "max", max)
 
     if min is not None:
-        check_type(min, 'min', (float, Variable), 'clamp')
+        check_type(min, 'min', (float, int, Variable), 'clip')
         if isinstance(min, Variable):
             check_dtype(min.dtype, 'min', ['float32', 'float64', 'int32'],
-                        'clamp', '(When the type of min in clamp is Variable.)')
+                        'clip', '(When the type of min in clip is Variable.)')
     if max is not None:
-        check_type(max, 'max', (float, Variable), 'clamp')
+        check_type(max, 'max', (float, int, Variable), 'clip')
         if isinstance(max, Variable):
             check_dtype(max.dtype, 'max', ['float32', 'float64', 'int32'],
-                        'clamp', '(When the type of max in clamp is Variable.)')
+                        'clip', '(When the type of max in clip is Variable.)')
 
-    inputs = {'X': input}
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'clip')
+
+    inputs = {'X': x}
     attrs = {'min': sys.float_info.min, 'max': sys.float_info.max}
 
     if isinstance(min, Variable):
@@ -1408,7 +1407,7 @@ def clamp(input, min=None, max=None, name=None):
     elif max is not None:
         attrs['max'] = max
 
-    helper = LayerHelper('clamp', **locals())
+    helper = LayerHelper('clip', **locals())
     output = helper.create_variable_for_type_inference(
         dtype=helper.input_dtype())
     helper.append_op(