【API2.0】add numel API for paddle test=develop (#26311)

paddle/fluid/operators/size_op.cc

@@ -54,5 +54,6 @@ REGISTER_OPERATOR(
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
 REGISTER_OP_CPU_KERNEL(size, ops::SizeKernel<int>, ops::SizeKernel<int32_t>,
+                       ops::SizeKernel<paddle::platform::float16>,
                        ops::SizeKernel<float>, ops::SizeKernel<double>,
                        ops::SizeKernel<bool>);

paddle/fluid/operators/size_op.cu

@@ -14,8 +14,9 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/size_op.h"
 
-REGISTER_OP_CUDA_KERNEL(size, paddle::operators::SizeKernel<int>,
+REGISTER_OP_CUDA_KERNEL(
+    size, paddle::operators::SizeKernel<int>,
     paddle::operators::SizeKernel<int32_t>,
-                        paddle::operators::SizeKernel<float>,
-                        paddle::operators::SizeKernel<bool>,
+    paddle::operators::SizeKernel<paddle::platform::float16>,
+    paddle::operators::SizeKernel<float>, paddle::operators::SizeKernel<bool>,
     paddle::operators::SizeKernel<double>);

python/paddle/__init__.py

@@ -237,6 +237,7 @@ from .tensor.stat import reduce_mean  #DEFINE_ALIAS
 from .tensor.stat import std  #DEFINE_ALIAS
 from .tensor.stat import var  #DEFINE_ALIAS
 from .fluid.data import data
+from .tensor.stat import numel  #DEFINE_ALIAS
 from .device import get_cudnn_version
 from .device import set_device
 from .device import get_device

python/paddle/fluid/layers/nn.py

@@ -11182,6 +11182,7 @@ def rank(input):
     return out
 
 
+@deprecated(since="2.0.0", update_to="paddle.numel")
 def size(input):
     """
     **Size Layer**
@@ -11189,10 +11190,13 @@ def size(input):
     Returns the number of elements for a tensor, which is a int64 Tensor with shape [1].
 
     Args:
-        input (Variable): The input variable.
+        input (Tensor): The input Tensor, it's data type can be bool, float16, float32, float64, int32, int64.
 
     Returns:
-        Variable: The number of elements for the input variable.
+        Tensor: The number of elements for the input Tensor.
+
+    Raises:
+        TypeError: ``input`` must be a Tensor and the data type of ``input`` must be one of bool, float16, float32, float64, int32, int64.
     
     Examples:
         .. code-block:: python
@@ -11204,6 +11208,11 @@ def size(input):
             rank = layers.size(input) # 300
     """
 
+    if in_dygraph_mode():
+        return core.ops.size(x)
+    check_variable_and_dtype(
+        x, 'x', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+        "size")
     helper = LayerHelper('size', **locals())
     out = helper.create_variable_for_type_inference(dtype='int64')
     helper.append_op(type='size', inputs={'Input': input}, outputs={'Out': out})

python/paddle/fluid/tests/unittests/test_numel_op.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
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import functools
+import paddle
+
+
+class TestNumelOp(OpTest):
+    def setUp(self):
+        self.op_type = "size"
+        self.init()
+        x = np.random.random((self.shape)).astype("float64")
+        self.inputs = {'Input': x, }
+        self.outputs = {'Out': np.array([np.size(x)])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def init(self):
+        self.shape = (6, 56, 8, 55)
+
+
+class TestNumelOp1(TestNumelOp):
+    def init(self):
+        self.shape = (11, 66)
+
+
+class TestNumelOp2(TestNumelOp):
+    def init(self):
+        self.shape = (0, )
+
+
+class TestNumelOoAPI(unittest.TestCase):
+    def test_numel_static(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+            shape1 = [2, 1, 4, 5]
+            shape2 = [1, 4, 5]
+            x_1 = paddle.data(shape=shape1, dtype='int32', name='x_1')
+            x_2 = paddle.data(shape=shape2, dtype='int32', name='x_2')
+            input_1 = np.random.random(shape1).astype("int32")
+            input_2 = np.random.random(shape2).astype("int32")
+            out_1 = paddle.numel(x_1)
+            out_2 = paddle.numel(x_2)
+            exe = paddle.static.Executor(place=paddle.CPUPlace())
+            res_1, res_2 = exe.run(feed={
+                "x_1": input_1,
+                "x_2": input_2,
+            },
+                                   fetch_list=[out_1, out_2])
+            assert (np.array_equal(
+                res_1, np.array([np.size(input_1)]).astype("int64")))
+            assert (np.array_equal(
+                res_2, np.array([np.size(input_2)]).astype("int64")))
+
+    def test_numel_imperative(self):
+        paddle.disable_static(paddle.CPUPlace())
+        input_1 = np.random.random([2, 1, 4, 5]).astype("int32")
+        input_2 = np.random.random([1, 4, 5]).astype("int32")
+        x_1 = paddle.to_variable(input_1)
+        x_2 = paddle.to_variable(input_2)
+        out_1 = paddle.numel(x_1)
+        out_2 = paddle.numel(x_2)
+        assert (np.array_equal(out_1.numpy().item(0), np.size(input_1)))
+        assert (np.array_equal(out_2.numpy().item(0), np.size(input_2)))
+        paddle.enable_static()
+
+    def test_error(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+
+            def test_x_type():
+                shape = [1, 4, 5]
+                input_1 = np.random.random(shape).astype("int32")
+                out_1 = paddle.numel(input_1)
+
+            self.assertRaises(TypeError, test_x_type)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -181,5 +181,7 @@ from .stat import mean  #DEFINE_ALIAS
 from .stat import reduce_mean  #DEFINE_ALIAS
 from .stat import std  #DEFINE_ALIAS
 from .stat import var  #DEFINE_ALIAS
+from .stat import numel  #DEFINE_ALIAS
+# from .tensor import Tensor        #DEFINE_ALIAS
 # from .tensor import LoDTensor        #DEFINE_ALIAS
 # from .tensor import LoDTensorArray        #DEFINE_ALIAS

python/paddle/tensor/stat.py

@@ -15,9 +15,10 @@
 # TODO: define statistical functions of a tensor  
 from ..fluid.layers import reduce_mean  #DEFINE_ALIAS
 
-__all__ = ['mean', 'reduce_mean', 'std', 'var']
+__all__ = ['mean', 'reduce_mean', 'std', 'var', 'numel']
 
 import numpy as np
+from ..fluid.framework import Variable
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.framework import core, in_dygraph_mode
 from ..fluid import layers
@@ -244,3 +245,41 @@ def std(input, axis=None, keepdim=False, unbiased=True, out=None, name=None):
         return out
     else:
         return tmp
+
+
+def numel(x, name=None):
+    """
+    Returns the number of elements for a tensor, which is a int64 Tensor with shape [1] in static mode
+    or a scalar value in imperative mode
+
+    Args:
+        x (Tensor): The input Tensor, it's data type can be bool, float16, float32, float64, int32, int64.
+
+    Returns:
+        Tensor: The number of elements for the input Tensor.
+    
+    Raises:
+        TypeError: ``x`` must be a Tensor and the data type of ``x`` must be one of bool, float16, float32, float64, int32, int64.
+
+
+    Examples:
+        .. code-block:: python
+
+        import paddle
+        
+        paddle.disable_static()
+        x = paddle.full(shape=[4, 5, 7], fill_value=0, dtype='int32')
+        numel = paddle.numel(x) # 140
+
+
+    """
+    if in_dygraph_mode():
+        return core.ops.size(x)
+
+    if not isinstance(x, Variable):
+        raise TypeError("x must be a Tensor in numel")
+    helper = LayerHelper('numel', **locals())
+    out = helper.create_variable_for_type_inference(
+        dtype=core.VarDesc.VarType.INT64)
+    helper.append_op(type='size', inputs={'Input': x}, outputs={'Out': out})
+    return out