未验证 提交 6602ccf4 编写于 作者: S Shibo Tao 提交者: GitHub

delete mul op. (#25499)

* delete mul op. test=develop,test=document_fix

* bug fix. test=develop,test=document_fix
上级 79fe67fb
...@@ -146,7 +146,6 @@ from .tensor.math import exp #DEFINE_ALIAS ...@@ -146,7 +146,6 @@ from .tensor.math import exp #DEFINE_ALIAS
from .tensor.math import floor #DEFINE_ALIAS from .tensor.math import floor #DEFINE_ALIAS
from .tensor.math import increment #DEFINE_ALIAS from .tensor.math import increment #DEFINE_ALIAS
from .tensor.math import log #DEFINE_ALIAS from .tensor.math import log #DEFINE_ALIAS
from .tensor.math import mul #DEFINE_ALIAS
from .tensor.math import multiplex #DEFINE_ALIAS from .tensor.math import multiplex #DEFINE_ALIAS
from .tensor.math import pow #DEFINE_ALIAS from .tensor.math import pow #DEFINE_ALIAS
from .tensor.math import reciprocal #DEFINE_ALIAS from .tensor.math import reciprocal #DEFINE_ALIAS
......
...@@ -119,7 +119,6 @@ from .math import exp #DEFINE_ALIAS ...@@ -119,7 +119,6 @@ from .math import exp #DEFINE_ALIAS
from .math import floor #DEFINE_ALIAS from .math import floor #DEFINE_ALIAS
from .math import increment #DEFINE_ALIAS from .math import increment #DEFINE_ALIAS
from .math import log #DEFINE_ALIAS from .math import log #DEFINE_ALIAS
from .math import mul #DEFINE_ALIAS
from .math import multiplex #DEFINE_ALIAS from .math import multiplex #DEFINE_ALIAS
from .math import pow #DEFINE_ALIAS from .math import pow #DEFINE_ALIAS
from .math import reciprocal #DEFINE_ALIAS from .math import reciprocal #DEFINE_ALIAS
......
...@@ -260,94 +260,6 @@ def pow(input, exponent, out=None, name=None): ...@@ -260,94 +260,6 @@ def pow(input, exponent, out=None, name=None):
return out return out
def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, out=None, name=None):
"""
:alias_main: paddle.mul
:alias: paddle.mul,paddle.tensor.mul,paddle.tensor.math.mul
Mul Operator.
This operator is used to perform matrix multiplication for input $x$ and $y$.
The equation is:
.. math::
Out = x * y
Both the input $x$ and $y$ can carry the LoD (Level of Details) information, or not.
But the output only shares the LoD information with input $x$.
Args:
x (Variable): The first input Tensor/LoDTensor of mul_op.
y (Variable): The second input Tensor/LoDTensor of mul_op.
x_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs.
If the input $x$ is a tensor with more than two dimensions, $x$ will be flattened into a two-dimensional
matrix first. The flattening rule is: the first `num_col_dims` will be flattened to form the first
dimension of the final matrix (the height of the matrix), and the rest `rank(x) - num_col_dims`
dimensions are flattened to form the second dimension of the final matrix (the width of the matrix).
As a result, height of the flattened matrix is equal to the product of $x$'s first `x_num_col_dims` dimensions'
sizes, and width of the flattened matrix is equal to the product of $x$'s last `rank(x) - num_col_dims`
dimensions' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6],
and `x_num_col_dims` = 3. Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default is 1.
y_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the
input $y$ is a tensor with more than two dimensions, $y$ will be flattened into a two-dimensional matrix first.
The attribute `y_num_col_dims` determines how $y$ is flattened. See comments of `x_num_col_dims` for more details.
Default is 1.
out(Variable, optinal): The Variable that stores results of the operation. If out is None,
a new Variable will be created to store the results.
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. If both of out and name are not None,
the output name will be same as out.
Returns:
Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of mul op.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
dataX = fluid.data(name="dataX", shape=[2, 5], dtype="float32")
dataY = fluid.data(name="dataY", shape=[5, 3], dtype="float32")
res = fluid.data(name="output", shape=[2, 3], dtype="float32")
output = paddle.mul(dataX, dataY,
x_num_col_dims = 1,
y_num_col_dims = 1,
out=res)
"""
inputs = {"X": [x], "Y": [y]}
attrs = {"x_num_col_dims": x_num_col_dims, "y_num_col_dims": y_num_col_dims}
if in_dygraph_mode():
outs = core.ops.mul(inputs, attrs)
return outs['Out'][0]
helper = LayerHelper("mul", **locals())
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mul')
check_variable_and_dtype(y, 'y', ['float16', 'float32', 'float64'], 'mul')
if out is None:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
check_dtype(
out.dtype, out.name,
convert_dtype(x.dtype), 'mul',
'(The out data type in pow must be the same with input data type.)')
if name:
warnings.warn(
"The output Variable name of the paddle.tensor.pow operation can only be given by parameter out or name.\
When parameter out and name are set at the same time, out has a higher priority than name. \
Finally, the output Variable name is same as the out name %s"
%
out.name,
category=UserWarning,
stacklevel=2)
helper.append_op(
type="mul", inputs={"X": x,
"Y": y}, attrs=attrs, outputs={"Out": out})
return out
__ops__noattr__ = [ __ops__noattr__ = [
'atan', 'atan',
'sin', 'sin',
......
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