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未验证 提交 07f509a8 编写于 作者: Y yaoxuefeng 提交者： GitHub
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fix 4 apis test=develop (#25529)

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python/paddle/fluid/tests/unittests/test_addmm_op.py
浏览文件 @ 07f509a8
...@@ -63,18 +63,104 @@ class TestAddMMOpError(unittest.TestCase): ...@@ -63,18 +63,104 @@ class TestAddMMOpError(unittest.TestCase):
def test_errors(self): def test_errors(self):
with program_guard(Program(), Program()): with program_guard(Program(), Program()):
# The input type of addmm_op must be Variable. # The input type of addmm_op must be Variable.
input = fluid.create_lod_tensor( input = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace()) np.array([[-1, -1], [-1, -1]]), [[2]], fluid.CPUPlace())
x1 = fluid.create_lod_tensor( x1 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace()) np.array([[-1, -1], [-1, -1]]), [[2]], fluid.CPUPlace())
x2 = fluid.create_lod_tensor( x2 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace()) np.array([[-1, -1], [-1, -1]]), [[2]], fluid.CPUPlace())
self.assertRaises(TypeError, paddle.addmm, input, x1, x2) self.assertRaises(TypeError, paddle.addmm, input, x1, x2)
# The input dtype of mul_op must be float32 or float64. # The input dtype of mul_op must be float32 or float64.
input = fluid.layers.data(name='input', shape=[4], dtype="int32") input = fluid.layers.data(
x3 = fluid.layers.data(name='x3', shape=[4], dtype="int32") name='input',
x4 = fluid.layers.data(name='x4', shape=[4], dtype="int32") shape=[4, 4],
dtype="int32",
append_batch_size=False)
x3 = fluid.layers.data(
name='x3', shape=[4, 4], dtype="int32", append_batch_size=False)
x4 = fluid.layers.data(
name='x4', shape=[4, 4], dtype="int32", append_batch_size=False)
self.assertRaises(TypeError, paddle.addmm, input, x3, x4) self.assertRaises(TypeError, paddle.addmm, input, x3, x4)
# x and y dimension mismatch
x5 = fluid.layers.data(
name='x5',
shape=[4, 5],
dtype="float32",
append_batch_size=False)
x6 = fluid.layers.data(
name='x6',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
self.assertRaises(ValueError, paddle.addmm, input, x5, x6)
# input and x are not broadcastable
x7 = fluid.layers.data(
name='x7',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
x8 = fluid.layers.data(
name='x8',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
input1 = fluid.layers.data(
name='input1',
shape=[2, 4],
dtype="float32",
append_batch_size=False)
self.assertRaises(ValueError, paddle.addmm, input1, x7, x8)
# input and x are not broadcastable
x9 = fluid.layers.data(
name='x9',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
x10 = fluid.layers.data(
name='x10',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
input2 = fluid.layers.data(
name='input2',
shape=[1, 2],
dtype="float32",
append_batch_size=False)
self.assertRaises(ValueError, paddle.addmm, input2, x9, x10)
x11 = fluid.layers.data(
name='x11',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
x12 = fluid.layers.data(
name='x12',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
input3 = fluid.layers.data(
name='input3',
shape=[4, 2],
dtype="float32",
append_batch_size=False)
self.assertRaises(ValueError, paddle.addmm, input3, x11, x12)
x13 = fluid.layers.data(
name='x13',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
x14 = fluid.layers.data(
name='x14',
shape=[4, 4],
dtype="float32",
append_batch_size=False)
input4 = fluid.layers.data(
name='input4',
shape=[3, 1],
dtype="float32",
append_batch_size=False)
self.assertRaises(ValueError, paddle.addmm, input4, x13, x14)
class TestAddMMOp2(TestAddMMOp): class TestAddMMOp2(TestAddMMOp):
...@@ -147,5 +233,23 @@ class TestAddMMOp4(unittest.TestCase): ...@@ -147,5 +233,23 @@ class TestAddMMOp4(unittest.TestCase):
assert np.allclose(np_input + np.dot(np_x, np_y), out.numpy()) assert np.allclose(np_input + np.dot(np_x, np_y), out.numpy())
'''
class TestAddMMAPI(unittest.TestCase):
def test_api_error(self):
data_x = np.ones((2, 2)).astype(np.float32)
data_y = np.ones((2, 2)).astype(np.float32)
data_input = np.ones((2, 2)).astype(np.float32)
paddle.enable_imperative()
def test_error1():
data_x_wrong = np.ones((2, 3)).astype(np.float32)
x = paddle.imperative.to_variable(data_x_wrong)
y = paddle.imperative.to_variable(data_y)
input = paddle.imperative.to_variable(data_input)
out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )
self.assertRaises(ValueError, test_error1)
'''
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()
python/paddle/fluid/tests/unittests/test_bmm_op.py
浏览文件 @ 07f509a8
...@@ -73,5 +73,15 @@ class API_TestDygraphBmm(unittest.TestCase): ...@@ -73,5 +73,15 @@ class API_TestDygraphBmm(unittest.TestCase):
self.assertTrue(np.allclose(expected_result, out_np)) self.assertTrue(np.allclose(expected_result, out_np))
class TestBmmAPIError(unittest.TestCase):
def test_api_error(self):
x_data = np.arange(24, dtype='float32').reshape((2, 3, 4))
y_data = np.arange(16, dtype='float32').reshape((2, 4, 2))
y_data_wrong1 = np.arange(16, dtype='float32').reshape((2, 2, 4))
y_data_wrong2 = np.arange(16, dtype='float32').reshape((2, 2, 2, 2))
self.assertRaises(ValueError, paddle.bmm, x_data, y_data_wrong1)
self.assertRaises(ValueError, paddle.bmm, x_data, y_data_wrong2)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()
python/paddle/fluid/tests/unittests/test_tril_triu_op.py
浏览文件 @ 07f509a8
...@@ -63,7 +63,7 @@ def case_generator(op_type, Xshape, diagonal, expected): ...@@ -63,7 +63,7 @@ def case_generator(op_type, Xshape, diagonal, expected):
"diagonal: TypeError": "diagonal: TypeError":
"diagonal in {} must be a python Int".format(op_type), "diagonal in {} must be a python Int".format(op_type),
"input: ValueError": "input: ValueError":
"input shape in {} must be at least 2-D".format(op_type), "x shape in {} must be at least 2-D".format(op_type),
} }
class FailureCase(unittest.TestCase): class FailureCase(unittest.TestCase):
...@@ -71,7 +71,7 @@ def case_generator(op_type, Xshape, diagonal, expected): ...@@ -71,7 +71,7 @@ def case_generator(op_type, Xshape, diagonal, expected):
data = fluid.data(shape=Xshape, dtype='float64', name=cls_name) data = fluid.data(shape=Xshape, dtype='float64', name=cls_name)
with self.assertRaisesRegexp( with self.assertRaisesRegexp(
eval(expected.split(':')[-1]), errmsg[expected]): eval(expected.split(':')[-1]), errmsg[expected]):
getattr(tensor, op_type)(input=data, diagonal=diagonal) getattr(tensor, op_type)(x=data, diagonal=diagonal)
class SuccessCase(TrilTriuOpDefaultTest): class SuccessCase(TrilTriuOpDefaultTest):
def initTestCase(self): def initTestCase(self):
......
python/paddle/tensor/creation.py
浏览文件 @ 07f509a8
...@@ -490,14 +490,13 @@ def _tril_triu_op(helper): ...@@ -490,14 +490,13 @@ def _tril_triu_op(helper):
"""Base op of tril_op and triu_op """Base op of tril_op and triu_op
""" """
op_type = helper.layer_type op_type = helper.layer_type
x = helper.kwargs.get('input', None) x = helper.kwargs.get('x', None)
assert x is not None, 'x cannot be None in {}'.format(op_type) assert x is not None, 'x cannot be None in {}'.format(op_type)
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'], check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
op_type) op_type)
if len(x.shape) < 2: if len(x.shape) < 2:
raise ValueError("input shape in {} must be at least 2-D".format( raise ValueError("x shape in {} must be at least 2-D".format(op_type))
op_type))
diagonal = helper.kwargs.get('diagonal', 0) diagonal = helper.kwargs.get('diagonal', 0)
if not isinstance(diagonal, (int, )): if not isinstance(diagonal, (int, )):
raise TypeError("diagonal in {} must be a python Int".format(op_type)) raise TypeError("diagonal in {} must be a python Int".format(op_type))
...@@ -521,18 +520,18 @@ def _tril_triu_op(helper): ...@@ -521,18 +520,18 @@ def _tril_triu_op(helper):
return out return out
def tril(input, diagonal=0, name=None): def tril(x, diagonal=0, name=None):
""" """
:alias_main: paddle.tril :alias_main: paddle.tril
:alias: paddle.tril,paddle.tensor.tril,paddle.tensor.creation.tril :alias: paddle.tril,paddle.tensor.tril,paddle.tensor.creation.tril
This op returns the lower triangular part of a matrix (2-D tensor) or batch This op returns the lower triangular part of a matrix (2-D tensor) or batch
of matrices :attr:`input`, the other elements of the result tensor are set of matrices :attr:`x`, the other elements of the result tensor are set
to 0. The lower triangular part of the matrix is defined as the elements to 0. The lower triangular part of the matrix is defined as the elements
on and below the diagonal. on and below the diagonal.
Args: Args:
input (Variable): The input variable which is a Tensor. x (Variable): The input variable x which is a Tensor.
Support data types: ``float64``, ``float32``, ``int32``, ``int64``. Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
diagonal (int, optional): The diagonal to consider, default value is 0. diagonal (int, optional): The diagonal to consider, default value is 0.
If :attr:`diagonal` = 0, all elements on and below the main diagonal are If :attr:`diagonal` = 0, all elements on and below the main diagonal are
...@@ -545,47 +544,41 @@ def tril(input, diagonal=0, name=None): ...@@ -545,47 +544,41 @@ def tril(input, diagonal=0, name=None):
user to set this property. For more information, please refer to :ref:`api_guide_Name`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Variable: Tensor, results of lower triangular operation by the specified diagonal of input tensor, Variable: Tensor, results of lower triangular operation by the specified diagonal of input tensor x,
it's data type is the same as input's Tensor. it's data type is the same as x's Tensor.
Raises: Raises:
TypeError: diagonal is not a int type. TypeError: diagonal is not a int type.
ValueError: dimension of :attr:`input` is less than 2. ValueError: dimension of :attr:`x` is less than 2.
Examples: Examples:
.. code-block:: python .. code-block:: python
import numpy as np import numpy as np
import paddle.tensor as tensor import paddle
import paddle.fluid as fluid
data = np.arange(1, 13, dtype="int64").reshape(3,-1) data = np.arange(1, 13, dtype="int64").reshape(3,-1)
# array([[ 1, 2, 3, 4], # array([[ 1, 2, 3, 4],
# [ 5, 6, 7, 8], # [ 5, 6, 7, 8],
# [ 9, 10, 11, 12]]) # [ 9, 10, 11, 12]])
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace())
# example 1, default diagonal paddle.enable_imperative()
tril = tensor.tril(x)
tril_out, = exe.run(fluid.default_main_program(), feed={"x": data}, x = paddle.imperative.to_variable(data)
fetch_list=[tril], return_numpy=True)
tril1 = paddle.tensor.tril(x)
# array([[ 1, 0, 0, 0], # array([[ 1, 0, 0, 0],
# [ 5, 6, 0, 0], # [ 5, 6, 0, 0],
# [ 9, 10, 11, 0]]) # [ 9, 10, 11, 0]])
# example 2, positive diagonal value # example 2, positive diagonal value
tril = tensor.tril(x, diagonal=2) tril2 = paddle.tensor.tril(x, diagonal=2)
tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[tril], return_numpy=True)
# array([[ 1, 2, 3, 0], # array([[ 1, 2, 3, 0],
# [ 5, 6, 7, 8], # [ 5, 6, 7, 8],
# [ 9, 10, 11, 12]]) # [ 9, 10, 11, 12]])
# example 3, negative diagonal value # example 3, negative diagonal value
tril = tensor.tril(x, diagonal=-1) tril3 = paddle.tensor.tril(x, diagonal=-1)
tril_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[tril], return_numpy=True)
# array([[ 0, 0, 0, 0], # array([[ 0, 0, 0, 0],
# [ 5, 0, 0, 0], # [ 5, 0, 0, 0],
# [ 9, 10, 0, 0]]) # [ 9, 10, 0, 0]])
...@@ -593,23 +586,23 @@ def tril(input, diagonal=0, name=None): ...@@ -593,23 +586,23 @@ def tril(input, diagonal=0, name=None):
""" """
if in_dygraph_mode(): if in_dygraph_mode():
op = getattr(core.ops, 'tril_triu') op = getattr(core.ops, 'tril_triu')
return op(input, 'diagonal', diagonal, "lower", True) return op(x, 'diagonal', diagonal, "lower", True)
return _tril_triu_op(LayerHelper('tril', **locals())) return _tril_triu_op(LayerHelper('tril', **locals()))
def triu(input, diagonal=0, name=None): def triu(x, diagonal=0, name=None):
""" """
:alias_main: paddle.triu :alias_main: paddle.triu
:alias: paddle.triu,paddle.tensor.triu,paddle.tensor.creation.triu :alias: paddle.triu,paddle.tensor.triu,paddle.tensor.creation.triu
This op returns the upper triangular part of a matrix (2-D tensor) or batch of matrices This op returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
:attr:`input`, the other elements of the result tensor are set to 0. :attr:`x`, the other elements of the result tensor are set to 0.
The upper triangular part of the matrix is defined as the elements on and The upper triangular part of the matrix is defined as the elements on and
above the diagonal. above the diagonal.
Args: Args:
input (Variable): The input variable which is a Tensor. x (Variable): The input variable x which is a Tensor.
Support data types: ``float64``, ``float32``, ``int32``, ``int64``. Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
diagonal (int, optional): The diagonal to consider, default value is 0. diagonal (int, optional): The diagonal to consider, default value is 0.
If :attr:`diagonal` = 0, all elements on and above the main diagonal are If :attr:`diagonal` = 0, all elements on and above the main diagonal are
...@@ -622,47 +615,41 @@ def triu(input, diagonal=0, name=None): ...@@ -622,47 +615,41 @@ def triu(input, diagonal=0, name=None):
user to set this property. For more information, please refer to :ref:`api_guide_Name`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Variable: Tensor, results of upper triangular operation by the specified diagonal of input tensor, Variable: Tensor, results of upper triangular operation by the specified diagonal of input tensor x,
it's data type is the same as input's Tensor. it's data type is the same as x's Tensor.
Raises: Raises:
TypeError: diagonal is not a int type. TypeError: diagonal is not a int type.
ValueError: dimension of :attr:`input` is less than 2. ValueError: dimension of :attr:`x` is less than 2.
Examples: Examples:
.. code-block:: python .. code-block:: python
import numpy as np import numpy as np
import paddle.fluid as fluid import paddle
import paddle.tensor as tensor
data = np.arange(1, 13, dtype="int64").reshape(3,-1) data = np.arange(1, 13, dtype="int64").reshape(3,-1)
# array([[ 1, 2, 3, 4], # array([[ 1, 2, 3, 4],
# [ 5, 6, 7, 8], # [ 5, 6, 7, 8],
# [ 9, 10, 11, 12]]) # [ 9, 10, 11, 12]])
x = fluid.data(shape=(-1, 4), dtype='int64', name='x')
exe = fluid.Executor(fluid.CPUPlace()) paddle.enable_imperative()
# example 1, default diagonal # example 1, default diagonal
triu = tensor.triu(x) x = paddle.imperative.to_variable(data)
triu_out, = exe.run(fluid.default_main_program(), feed={"x": data}, triu1 = paddle.tensor.triu(x)
fetch_list=[triu], return_numpy=True)
# array([[ 1, 2, 3, 4], # array([[ 1, 2, 3, 4],
# [ 0, 6, 7, 8], # [ 0, 6, 7, 8],
# [ 0, 0, 11, 12]]) # [ 0, 0, 11, 12]])
# example 2, positive diagonal value # example 2, positive diagonal value
triu = tensor.triu(x, diagonal=2) triu2 = paddle.tensor.triu(x, diagonal=2)
triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[triu], return_numpy=True)
# array([[0, 0, 3, 4], # array([[0, 0, 3, 4],
# [0, 0, 0, 8], # [0, 0, 0, 8],
# [0, 0, 0, 0]]) # [0, 0, 0, 0]])
# example 3, negative diagonal value # example 3, negative diagonal value
triu = tensor.triu(x, diagonal=-1) triu3 = paddle.tensor.triu(x, diagonal=-1)
triu_out, = exe.run(fluid.default_main_program(), feed={"x": data},
fetch_list=[triu], return_numpy=True)
# array([[ 1, 2, 3, 4], # array([[ 1, 2, 3, 4],
# [ 5, 6, 7, 8], # [ 5, 6, 7, 8],
# [ 0, 10, 11, 12]]) # [ 0, 10, 11, 12]])
...@@ -670,7 +657,7 @@ def triu(input, diagonal=0, name=None): ...@@ -670,7 +657,7 @@ def triu(input, diagonal=0, name=None):
""" """
if in_dygraph_mode(): if in_dygraph_mode():
op = getattr(core.ops, 'tril_triu') op = getattr(core.ops, 'tril_triu')
return op(input, 'diagonal', diagonal, "lower", False) return op(x, 'diagonal', diagonal, "lower", False)
return _tril_triu_op(LayerHelper('triu', **locals())) return _tril_triu_op(LayerHelper('triu', **locals()))
......
python/paddle/tensor/linalg.py
浏览文件 @ 07f509a8
...@@ -729,26 +729,32 @@ def bmm(x, y, name=None): ...@@ -729,26 +729,32 @@ def bmm(x, y, name=None):
Examples: Examples:
import paddle import paddle
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10, 3, 4], dtype='float32')
y = fluid.layers.data(name='y', shape=[10, 4, 5], dtype='float32')
out = paddle.bmm(x, y)
# In dygraph mode: # In imperative mode:
# size input1: (2, 2, 3) and input2: (2, 3, 2) # size input1: (2, 2, 3) and input2: (2, 3, 2)
input1 = np.array([[[1.0, 1.0, 1.0],[2.0, 2.0, 2.0]],[[3.0, 3.0, 3.0],[4.0, 4.0, 4.0]]]) input1 = np.array([[[1.0, 1.0, 1.0],[2.0, 2.0, 2.0]],[[3.0, 3.0, 3.0],[4.0, 4.0, 4.0]]])
input2 = np.array([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],[[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]]) input2 = np.array([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],[[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])
with fluid.dygraph.guard(): paddle.enable_imperative()
x = fluid.dygraph.to_variable(input1)
y = fluid.dygraph.to_variable(input2) x = paddle.imperative.to_variable(input1)
y = paddle.imperative.to_variable(input2)
out = paddle.bmm(x, y) out = paddle.bmm(x, y)
#output size: (2, 2, 2) #output size: (2, 2, 2)
#output value: #output value:
#[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]] #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]
out_np = out.numpy() out_np = out.numpy()
""" """
x_shape = x.shape
y_shape = y.shape
if not len(x_shape) == len(y_shape) == 3:
raise ValueError(
"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}".
format(x_shape, y_shape))
if x_shape[2] != y_shape[1]:
raise ValueError(
"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}".
format(x_shape, y_shape))
helper = LayerHelper('bmm', **locals()) helper = LayerHelper('bmm', **locals())
if in_dygraph_mode(): if in_dygraph_mode():
return core.ops.bmm(x, y) return core.ops.bmm(x, y)
......
python/paddle/tensor/math.py
浏览文件 @ 07f509a8
...@@ -915,7 +915,7 @@ def mm(input, mat2, name=None): ...@@ -915,7 +915,7 @@ def mm(input, mat2, name=None):
return out return out
def addmm(input, x, y, alpha=1.0, beta=1.0, name=None): def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
""" """
:alias_main: paddle.addmm :alias_main: paddle.addmm
:alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm :alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm
...@@ -935,8 +935,8 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None): ...@@ -935,8 +935,8 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None):
input (Variable): The input Tensor/LoDTensor to be added to the final result. input (Variable): The input Tensor/LoDTensor to be added to the final result.
x (Variable): The first input Tensor/LoDTensor for matrix multiplication. x (Variable): The first input Tensor/LoDTensor for matrix multiplication.
y (Variable): The second input Tensor/LoDTensor for matrix multiplication. y (Variable): The second input Tensor/LoDTensor for matrix multiplication.
alpha (float): Coefficient of $x*y$.
beta (float): Coefficient of $input$. beta (float): Coefficient of $input$.
alpha (float): Coefficient of $x*y$.
name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns: Returns:
...@@ -947,25 +947,43 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None): ...@@ -947,25 +947,43 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None):
import numpy as np import numpy as np
import paddle import paddle
import paddle.fluid as fluid
input = fluid.data(name='input', shape=[2, 2], dtype='float32')
x = fluid.data(name='x', shape=[2, 2], dtype='float32')
y = fluid.data(name='y', shape=[2, 2], dtype='float32')
out = paddle.addmm( input=input, x=x, y=y, alpha=5.0, beta=0.5 )
data_x = np.ones((2, 2)).astype(np.float32) data_x = np.ones((2, 2)).astype(np.float32)
data_y = np.ones((2, 2)).astype(np.float32) data_y = np.ones((2, 2)).astype(np.float32)
data_input = np.ones((2, 2)).astype(np.float32) data_input = np.ones((2, 2)).astype(np.float32)
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace() paddle.enable_imperative()
exe = fluid.Executor(place)
results = exe.run(fluid.default_main_program(), x = paddle.imperative.to_variable(data_x)
fetch_list=[out], feed={"input": data_input, 'x': data_x, "y": data_y}) y = paddle.imperative.to_variable(data_y)
print( np.array(results[0]) ) input = paddle.imperative.to_variable(data_input)
out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )
print( out.numpy() )
# [[10.5 10.5] # [[10.5 10.5]
# [10.5 10.5]] # [10.5 10.5]]
""" """
input_shape = input.shape
x_shape = x.shape
y_shape = y.shape
if not len(input_shape) == len(x_shape) == len(y_shape) == 2:
raise ValueError("The dimention of input, x, y should be 2 but receive input's shape: {}, x's shape: {}, y's shape: {}".format(input_shape, x_shape, y_shape))
if input_shape[0] != x_shape[0]:
if input_shape[0] != 1:
raise ValueError( "When x's dimension[0] is not equal with input's dimension[0], input's dimension[0] must be 1 but got {}".format(input_shape[0]))
if input_shape[1] != y_shape[1] and input_shape[1] != 1:
raise ValueError( "When y's dimension[1] is not equal with input's dimension[1], input's dimension[1] must be 1 but got {}".format(input_shape[1]))
if input_shape[1] != y_shape[1]:
if input_shape[1] != 1:
raise ValueError( "When y's dimension[1] is not equal with input's dimension[1], input's dimension[1] must be 1 but got {}".format(input_shape[1]))
if input_shape[0] != x_shape[0] and input_shape[0] != 1:
raise ValueError( "When x's dimension[0] is not equal with input's dimension[0], input's dimension[0] must be 1 but got {}".format(input_shape[0]))
if x_shape[1] != y_shape[0]:
raise ValueError("The input Variable x's width must be equal with Variable y' height. But received x's shape = {}, y's shape = {}.".format(x_shape, y_shape))
if in_dygraph_mode(): if in_dygraph_mode():
out = core.ops.addmm(input, x, y, "Alpha", alpha, "Beta", beta) out = core.ops.addmm(input, x, y, "Alpha", alpha, "Beta", beta)
return out return out
...@@ -974,7 +992,7 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None): ...@@ -974,7 +992,7 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None):
attrs = {'Alpha': alpha, 'Beta': beta} attrs = {'Alpha': alpha, 'Beta': beta}
helper = LayerHelper("addmm", **locals()) helper = LayerHelper("addmm", **locals())
check_variable_and_dtype(x, 'Input', ['float32', 'float64'], 'addmm') check_variable_and_dtype(input, 'Input', ['float32', 'float64'], 'addmm')
check_variable_and_dtype(x, 'X', ['float32', 'float64'], 'addmm') check_variable_and_dtype(x, 'X', ['float32', 'float64'], 'addmm')
check_variable_and_dtype(y, 'Y', ['float32', 'float64'], 'addmm') check_variable_and_dtype(y, 'Y', ['float32', 'float64'], 'addmm')
out = helper.create_variable_for_type_inference(dtype=x.dtype) out = helper.create_variable_for_type_inference(dtype=x.dtype)
......
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