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

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fix_copy_if_different
wangchaochaohu 5 years ago committed by GitHub
parent 43f3d0cce3
commit 1e4ab728fb
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6 changed files with 168 additions and 18 deletions
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4
paddle/fluid/operators/concat_op.cc
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@ -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>);

32
python/paddle/fluid/layers/tensor.py
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@ -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:

84
python/paddle/fluid/tests/unittests/test_concat_op.py
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@ -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):
"""

4
python/paddle/tensor/creation.py
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@ -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

61
python/paddle/tensor/manipulation.py
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@ -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

1
python/paddle/tensor/search.py
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@ -162,6 +162,7 @@ def index_select(x, index, axis=0, name=None):
Examples:
.. code-block:: python
import paddle
import numpy as np

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