[API 2.0: doc] transfer from paddle.fluid.layers.assign() into creati…

…on.py (PaddlePaddle#27999)

* transfer from paddle.fluid.layers.assign() into creation.py,test=develop

* fix ut fail,add support for paddle.assign,test=develop

* fix,test=develop

* fix UT coverage,test=coverage

* fix UT fail,test=coverage

* fix doc,test=develop

python/paddle/__init__.py

@@ -77,6 +77,7 @@
 from .tensor.creation import meshgrid  #DEFINE_ALIAS
 from .tensor.creation import empty  #DEFINE_ALIAS
 from .tensor.creation import empty_like  #DEFINE_ALIAS
+from .tensor.creation import assign  #DEFINE_ALIAS
 from .tensor.linalg import matmul  #DEFINE_ALIAS
 from .tensor.linalg import dot  #DEFINE_ALIAS
 # from .tensor.linalg import einsum        #DEFINE_ALIAS
@@ -262,7 +263,6 @@
 from .fluid.dygraph.base import no_grad_ as no_grad  #DEFINE_ALIAS
 from .fluid.layers import crop_tensor as crop  #DEFINE_ALIAS
 
-
 from . import jit
 from . import static
 from . import amp

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

@@ -17,6 +17,7 @@
 import op_test
 import numpy as np
 import unittest
+import paddle
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
@@ -99,5 +100,81 @@ def test_errors(self):
             self.assertRaises(TypeError, fluid.layers.assign, x5)
 
 
+class TestAssignOApi(unittest.TestCase):
+    def test_assign_LoDTensorArray(self):
+        main_program = Program()
+        startup_program = Program()
+        with program_guard(main_program):
+            x = fluid.data(name='x', shape=[100, 10], dtype='float32')
+            x.stop_gradient = False
+            y = fluid.layers.fill_constant(
+                shape=[100, 10], dtype='float32', value=1)
+            z = fluid.layers.elementwise_add(x=x, y=y)
+            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+            init_array = fluid.layers.array_write(x=z, i=i)
+            array = paddle.assign(init_array)
+            sums = fluid.layers.array_read(array=init_array, i=i)
+            mean = fluid.layers.mean(sums)
+            append_backward(mean)
+
+        place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        feed_x = np.random.random(size=(100, 10)).astype('float32')
+        ones = np.ones((100, 10)).astype('float32')
+        feed_add = feed_x + ones
+        res = exe.run(main_program,
+                      feed={'x': feed_x},
+                      fetch_list=[sums.name, x.grad_name])
+        self.assertTrue(np.allclose(res[0], feed_add))
+        self.assertTrue(np.allclose(res[1], ones / 1000.0))
+
+    def test_assign_NumpyArray(self):
+        with fluid.dygraph.guard():
+            array = np.random.random(size=(100, 10)).astype(np.bool)
+            result1 = paddle.zeros(shape=[3, 3], dtype='float32')
+            paddle.assign(array, result1)
+        self.assertTrue(np.allclose(result1.numpy(), array))
+
+    def test_assign_NumpyArray1(self):
+        with fluid.dygraph.guard():
+            array = np.random.random(size=(100, 10)).astype(np.float32)
+            result1 = paddle.zeros(shape=[3, 3], dtype='float32')
+            paddle.assign(array, result1)
+        self.assertTrue(np.allclose(result1.numpy(), array))
+
+    def test_assign_NumpyArray2(self):
+        with fluid.dygraph.guard():
+            array = np.random.random(size=(100, 10)).astype(np.int32)
+            result1 = paddle.zeros(shape=[3, 3], dtype='float32')
+            paddle.assign(array, result1)
+        self.assertTrue(np.allclose(result1.numpy(), array))
+
+    def test_assign_NumpyArray3(self):
+        with fluid.dygraph.guard():
+            array = np.random.random(size=(100, 10)).astype(np.int64)
+            result1 = paddle.zeros(shape=[3, 3], dtype='float32')
+            paddle.assign(array, result1)
+        self.assertTrue(np.allclose(result1.numpy(), array))
+
+
+class TestAssignOpErrorApi(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The type of input must be Variable or numpy.ndarray.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, paddle.assign, x1)
+            # When the type of input is Variable, the dtype of input must be float16, float32, float64, int32, int64, bool.
+            x3 = fluid.layers.data(name='x3', shape=[4], dtype="uint8")
+            self.assertRaises(TypeError, paddle.assign, x3)
+            # When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
+            x4 = np.array([[2.5, 2.5]], dtype='float64')
+            self.assertRaises(TypeError, paddle.assign, x4)
+            x5 = np.array([[2.5, 2.5]], dtype='uint8')
+            self.assertRaises(TypeError, paddle.assign, x5)
+
+
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()

python/paddle/tensor/creation.py

@@ -47,7 +47,8 @@
     'empty_like',
     'triu',
     'tril',
-    'meshgrid'
+    'meshgrid',
+    'assign',
 ]
 
 
@@ -1106,3 +1107,77 @@ def empty_like(x, dtype=None, name=None):
         stop_gradient=True)
     out.stop_gradient = True
     return out
+
+
+def assign(x, output=None):
+    """
+ 
+ 
+    The OP copies the :attr:`x` to the :attr:`output`.
+ 
+    Parameters:
+        x (Tensor|numpy.ndarray): A tensor or numpy ndarray, its data type supports
+            float16, float32, float64, int32 and int64.
+        output (Tensor, optional): A tensor. If :attr:`output` is None, a new tensor will
+            be created as :attr:`output`. Default: None.
+ 
+    Returns:
+        Tensor: A tensor with the same shape, data type and value as :attr:`x`.
+ 
+    Examples:
+        .. code-block:: python
+ 
+          import paddle
+          import numpy as np
+          data = paddle.full(shape=[3, 2], fill_value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
+          array = np.array([[1, 1],
+                            [3, 4],
+                            [1, 3]]).astype(np.int64)
+          result1 = paddle.zeros(shape=[3, 3], dtype='float32')
+          paddle.assign(array, result1) # result1 = [[1, 1], [3 4], [1, 3]]
+          result2 = paddle.assign(data)  # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
+          result3 = paddle.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
+    """
+    helper = LayerHelper('assign', **locals())
+    check_type(x, 'x', (Variable, numpy.ndarray), 'assign')
+    if isinstance(x, Variable):
+        check_dtype(
+            x.dtype, 'x',
+            ['float16', 'float32', 'float64', 'int32', 'int64', 'bool'],
+            'assign', '(When the type of input in assign is Variable.)')
+        if output is None:
+            output = helper.create_variable_for_type_inference(dtype=x.dtype)
+        helper.append_op(
+            type='assign', inputs={'X': [x]}, outputs={'Out': [output]})
+    elif isinstance(x, numpy.ndarray):
+        dtype = convert_np_dtype_to_dtype_(x.dtype)
+        if dtype == VarDesc.VarType.BOOL:
+            value_name = "bool_values"
+            values = [bool(v) for v in x.flat]
+        elif dtype == VarDesc.VarType.FP32:
+            value_name = "fp32_values"
+            values = [float(v) for v in x.flat]
+        elif dtype == VarDesc.VarType.INT32:
+            value_name = "int32_values"
+            values = [int(v) for v in x.flat]
+        elif dtype == VarDesc.VarType.INT64:
+            value_name = "int64_values"
+            values = [int(v) for v in x.flat]
+        else:
+            raise TypeError(
+                "When the type of 'x' in assign is numpy.ndarray, "
+                "the data type of 'x' must be bool, float32, int32 or int64, but "
+                "received %s." % convert_dtype(dtype))
+        if x.size > 1024 * 1024:
+            raise ValueError("The size of input is too big. Please consider "
+                             "saving it to file and 'load_op' to load it")
+        if output is None:
+            output = helper.create_variable_for_type_inference(dtype=x.dtype)
+        helper.append_op(
+            type='assign_value',
+            outputs={'Out': [output]},
+            attrs={'dtype': dtype,
+                   'shape': list(x.shape),
+                   value_name: values})
+
+    return output