refine the concat Op for API 2.0 test=develop (#25307)

paddle/fluid/operators/concat_op.cc

@@ -208,10 +208,14 @@ REGISTER_OP_CPU_KERNEL(
     concat, ops::ConcatKernel<paddle::platform::CPUDeviceContext, double>,
     ops::ConcatKernel<paddle::platform::CPUDeviceContext, float>,
     ops::ConcatKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::ConcatKernel<paddle::platform::CPUDeviceContext,
+                      paddle::platform::float16>,
     ops::ConcatKernel<paddle::platform::CPUDeviceContext, int>);
 REGISTER_OP_CPU_KERNEL(
     concat_grad,
     ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, double>,
     ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, float>,
     ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::ConcatGradKernel<paddle::platform::CPUDeviceContext,
+                          paddle::platform::float16>,
     ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, int>);

python/paddle/fluid/layers/tensor.py

@@ -265,24 +265,26 @@ def concat(input, axis=0, name=None):
     """
 	:alias_main: paddle.concat
 	:alias: paddle.concat,paddle.tensor.concat,paddle.tensor.manipulation.concat
-	:old_api: paddle.fluid.layers.concat
-
-    **Concat**
 
     This OP concatenates the input along the axis.
 
     Args:
-        input(list): List of input Tensors with data type float32, float64, int32,
-            int64.
-        axis(int32|Variable, optional):  A scalar with type ``int32`` or a ``Tensor`` with shape [1] and type ``int32``. Axis to compute indices along. The effective range
-            is [-R, R), where R is Rank(x). when axis<0, it works the same way
+        input(list): List of input Tensors with data type float16, float32, float64, int32,
+            int64. All the Tensors in ``input`` must have the same data type.
+        axis(int|Variable, optional): Specify the axis to operate on the input Tensors.
+            It's a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` or ``int64``.
+            The effective range is [-R, R), where R is Rank(x). When ``axis < 0``, it works the same way
             as axis+R. Default is 0.
         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`.
+    Raises:
+        TypeError: The dtype of input must be one of float16, float32, float64, int32 and int64. 
+        TypeError: The ``axis`` must be int or Variable. The dtype of ``axis`` must be int32 or int64 when it's a Tensor.
+        TypeError: All the Tensors in ``input`` must have the same data type.
 
     Returns:
-        Variable: A Tensor with the same data type as input's.
+        Variable: A Tensor with the same data type as ``input``.
 
     Examples:
         .. code-block:: python
@@ -300,6 +302,8 @@ def concat(input, axis=0, name=None):
                 x1 = fluid.dygraph.to_variable(in1)
                 x2 = fluid.dygraph.to_variable(in2)
                 x3 = fluid.dygraph.to_variable(in3)
+                # When the axis is negative, the real axis is (axis + Rank(x)).
+                # As follows, axis is -1, Rank(x) is 2, the real axis is 1
                 out1 = fluid.layers.concat(input=[x1,x2,x3], axis=-1)
                 out2 = fluid.layers.concat(input=[x1,x2], axis=0)
                 print(out1.numpy())
@@ -315,8 +319,6 @@ def concat(input, axis=0, name=None):
     if in_dygraph_mode():
         if isinstance(axis, Variable):
             axis = axis.numpy()
-            assert axis.shape == (
-                1, ), "axis of type Variable should have shape [1]"
             axis = axis[0]
         return core.ops.concat(input, 'axis', axis)
 
@@ -329,8 +331,16 @@ def concat(input, axis=0, name=None):
         check_variable_and_dtype(
             x, 'input[' + str(id) + ']',
             ['float16', 'float32', 'float64', 'int32', 'int64'], 'concat')
+        if x.dtype != input[0].dtype:
+            raise TypeError(
+                "All the Tensors in the input must have the same data type.")
     check_type(axis, 'axis', (int, Variable), 'concat')
 
+    if isinstance(axis, Variable):
+        check_dtype(
+            axis.dtype, 'axis', ['int32', 'int64'], 'concat',
+            "The data type of axis must be int32 or int64 when axis is a Tensor")
+
     helper = LayerHelper('concat', **locals())
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
 
@@ -652,7 +662,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
 
     Raises:
         TypeError: The dtype must be one of bool, float16, float32, float64, int32 and int64
-        and the data type of out Tensor must be the same as the dtype. 
+            and the data type of out Tensor must be the same as the dtype. 
         TypeError: The shape must be one of list, tuple and Variable.
 
     Examples:

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

@@ -19,6 +19,7 @@ import numpy as np
 from op_test import OpTest, skip_check_grad_ci
 import paddle.fluid as fluid
 from paddle.fluid import compiler, Program, program_guard, core
+import paddle
 
 
 class TestConcatOp(OpTest):
@@ -175,8 +176,6 @@ create_test_AxisTensor(TestConcatOp6)
 
 
 def create_test_fp16(parent):
-    @unittest.skipIf(not core.is_compiled_with_cuda(),
-                     "core is not compiled with CUDA")
     class TestConcatFp16(parent):
         def get_dtype(self):
             return np.float16
@@ -206,12 +205,13 @@ class TestConcatOpError(unittest.TestCase):
             x3 = fluid.create_lod_tensor(
                 np.array([[-1]]), [[1]], fluid.CPUPlace())
             self.assertRaises(TypeError, fluid.layers.concat, [x2])
-            # The input dtype of concat_op must be float16(only support on GPU), float32, float64, int32, int64.
+            # The input dtype of concat_op must be float16, float32, float64, int32, int64.
             x4 = fluid.layers.data(shape=[4], dtype='uint8', name='x4')
             x5 = fluid.layers.data(shape=[4], dtype='uint8', name='x5')
             self.assertRaises(TypeError, fluid.layers.concat, [x4, x5])
             x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6')
             x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7')
+            x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8')
             fluid.layers.concat([x6, x7])
 
             # The type of axis in concat_op should be int or Variable.
@@ -220,9 +220,14 @@ class TestConcatOpError(unittest.TestCase):
 
             self.assertRaises(TypeError, test_axis_type)
 
+            def test_input_same_dtype():
+                fluid.layers.concat([x7, x8])
+
+            self.assertRaises(TypeError, test_input_same_dtype)
+
 
 class TestConcatAPI(unittest.TestCase):
-    def test_api(self):
+    def test_fluid_api(self):
         x_1 = fluid.data(shape=[None, 1, 4, 5], dtype='int32', name='x_1')
         fluid.layers.concat([x_1, x_1], 0)
 
@@ -247,6 +252,77 @@ class TestConcatAPI(unittest.TestCase):
         assert np.array_equal(res_2, np.concatenate((input_2, input_3), axis=1))
         assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
 
+    def test_api(self):
+        x_1 = paddle.data(shape=[None, 1, 4, 5], dtype='int32', name='x_1')
+        paddle.concat([x_1, x_1], 0)
+
+        input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
+        input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
+        x_2 = fluid.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2')
+        x_3 = fluid.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3')
+        positive_1_int32 = paddle.fill_constant([1], "int32", 1)
+        positive_1_int64 = paddle.fill_constant([1], "int64", 1)
+        negative_int64 = paddle.fill_constant([1], "int64", -3)
+        out_1 = paddle.concat(x=[x_2, x_3], axis=1)
+        out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32)
+        out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64)
+        out_4 = paddle.concat(x=[x_2, x_3], axis=negative_int64)
+
+        exe = paddle.Executor(place=paddle.CPUPlace())
+        [res_1, res_2, res_3, res_4] = exe.run(
+            paddle.default_main_program(),
+            feed={"x_1": input_2,
+                  "x_2": input_2,
+                  "x_3": input_3},
+            fetch_list=[out_1, out_2, out_3, out_4])
+        assert np.array_equal(res_1, np.concatenate((input_2, input_3), axis=1))
+        assert np.array_equal(res_2, np.concatenate((input_2, input_3), axis=1))
+        assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
+        assert np.array_equal(res_4, np.concatenate((input_2, input_3), axis=1))
+
+    def test_imperative(self):
+        in1 = np.array([[1, 2, 3], [4, 5, 6]])
+        in2 = np.array([[11, 12, 13], [14, 15, 16]])
+        in3 = np.array([[21, 22], [23, 24]])
+        with paddle.imperative.guard():
+            x1 = paddle.imperative.to_variable(in1)
+            x2 = paddle.imperative.to_variable(in2)
+            x3 = paddle.imperative.to_variable(in3)
+            out1 = fluid.layers.concat(input=[x1, x2, x3], axis=-1)
+            out2 = paddle.concat(x=[x1, x2], axis=0)
+            np_out1 = np.concatenate([in1, in2, in3], axis=-1)
+            np_out2 = np.concatenate([in1, in2], axis=0)
+        self.assertEqual((out1.numpy() == np_out1).all(), True)
+        self.assertEqual((out2.numpy() == np_out2).all(), True)
+
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The item in input must be Variable.
+            x2 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            x3 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, paddle.concat, [x2])
+            # The input dtype of concat_op must be float16, float32, float64, int32, int64.
+            x4 = paddle.data(shape=[4], dtype='uint8', name='x4')
+            x5 = paddle.data(shape=[4], dtype='uint8', name='x5')
+            self.assertRaises(TypeError, fluid.layers.concat, [x4, x5])
+
+            # The type of axis in concat_op should be int or Variable.
+            x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6')
+            x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7')
+            x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8')
+
+            def test_axis_type():
+                paddle.concat([x6, x7], 3.2)
+
+            self.assertRaises(TypeError, test_axis_type)
+
+            def test_input_same_dtype():
+                paddle.concat([x7, x8])
+
+            self.assertRaises(TypeError, test_input_same_dtype)
+
 
 class TestConcatAPIWithLoDTensorArray(unittest.TestCase):
     """

python/paddle/tensor/creation.py

@@ -134,7 +134,7 @@ def ones(shape, dtype=None, name=None):
 
     Raises:
         TypeError: The dtype must be one of bool, float16, float32, float64, int32, int64 and None
-        and the data type of out Tensor must be the same as the dtype. 
+            and the data type of out Tensor must be the same as the dtype. 
         TypeError: The `shape` must be one of list, tuple and Variable.
     
     Examples:
@@ -242,7 +242,7 @@ def zeros(shape, dtype=None, name=None):
     The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 0.
 
     Args:
-        shape(tuple|list): Shape of output tensor.
+        shape(tuple|list|Variable): Shape of output tensor. The data type of shape is int32 or int64.
         dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of output tensor, it supports
             bool, float16, float32, float64, int32 and int64. Default: if None, the date type is float32.
         name(str, optional): The default value is None.  Normally there is no need for user to set this

python/paddle/tensor/manipulation.py

@@ -23,7 +23,6 @@ from ..fluid.layers import utils
 import numpy as np
 # TODO: define functions to manipulate a tensor  
 from ..fluid.layers import cast  #DEFINE_ALIAS
-from ..fluid.layers import concat  #DEFINE_ALIAS
 from ..fluid.layers import expand  #DEFINE_ALIAS
 from ..fluid.layers import expand_as  #DEFINE_ALIAS
 from ..fluid.layers import flatten  #DEFINE_ALIAS
@@ -41,6 +40,7 @@ from ..fluid.layers import scatter_nd  #DEFINE_ALIAS
 from ..fluid.layers import shard_index  #DEFINE_ALIAS
 from ..fluid.layers import unique_with_counts  #DEFINE_ALIAS
 from ..fluid import layers
+import paddle
 
 __all__ = [
     'cast', 'concat', 'expand', 'expand_as', 'flatten', 'gather', 'gather_nd',
@@ -51,6 +51,65 @@ __all__ = [
 ]
 
 
+def concat(x, axis=0, name=None):
+    """
+	:alias_main: paddle.concat
+	:alias: paddle.concat,paddle.tensor.concat,paddle.tensor.manipulation.concat
+
+    This OP concatenates the input along the axis.
+
+    Args:
+        x(list): List of input Tensors with data type float16, float32, float64, int32, int64.
+            All the Tensors in ``x`` must have same data type.
+        axis(int|Variable, optional): Specify the axis to operate on the input Tensors.
+            It's a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` 
+            or ``int64``. The effective range is [-R, R), where R is Rank(x). When ``axis < 0``,
+            it works the same way as axis+R. Default is 0.
+        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`.
+    Raises:
+        TypeError: The dtype of ``x`` must be one of float16, float32, float64, int32 and int64. 
+        TypeError: The ``axis`` must be int or Variable. The dtype of ``axis`` must be int32 or int64 when it's a Tensor.
+        TypeError: All the Tensors in ``x`` must have the same data type.
+
+    Returns:
+        Variable: A Tensor with the same data type as ``x``.
+
+    Examples:
+        .. code-block:: python
+            
+            import paddle
+            import numpy as np
+            
+            paddle.enable_imperative()  # Now we are in imperative mode
+            in1 = np.array([[1,2,3],
+                            [4,5,6]])
+            in2 = np.array([[11,12,13],
+                            [14,15,16]])
+            in3 = np.array([[21,22],
+                            [23,24]])
+            x1 = paddle.imperative.to_variable(in1)
+            x2 = paddle.imperative.to_variable(in2)
+            x3 = paddle.imperative.to_variable(in3)
+            zero = paddle.full(shape=[1], dtype='int32', fill_value=0)
+            # When the axis is negative, the real axis is (axis + Rank(x))
+            # As follow, axis is -1, Rank(x) is 2, the real axis is 1
+            out1 = paddle.concat(x=[x1,x2,x3], axis=-1)
+            out2 = paddle.concat(x=[x1,x2], axis=0)
+            out3 = paddle.concat(x=[x1,x2], axis=zero)
+            # out1
+            # [[ 1  2  3 11 12 13 21 22]
+            #  [ 4  5  6 14 15 16 23 24]]
+            # out2 out3
+            # [[ 1  2  3]
+            #  [ 4  5  6]
+            #  [11 12 13]
+            #  [14 15 16]]
+    """
+    return paddle.fluid.layers.concat(input=x, axis=axis, name=name)
+
+
 def flip(x, axis, name=None):
     """
 	:alias_main: paddle.flip

python/paddle/tensor/search.py

@@ -162,6 +162,7 @@ def index_select(x, index, axis=0, name=None):
 
     Examples:
         .. code-block:: python
+            
             import paddle
             import numpy as np