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Polish elementwise max min pow document to add more examples. (#19946)

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Polish elementwise max min pow document to add more examples
fix-python-transpose
gongweibao 5 years ago committed by GitHub
parent 2b5b4b3c5e
commit a7512db2bc
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 250 additions and 35 deletions
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18
paddle/fluid/API.spec
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@ -236,15 +236,15 @@ paddle.fluid.layers.unique_with_counts (ArgSpec(args=['x', 'dtype'], varargs=Non
paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7b97042c3ba55fb5fec6a06308523b73'))
paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b992616c1afbd6b0c2a897ac23036381'))
paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', '463e4713806e5adaa4d20a41e2218453'))
paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '5c0fb7298aec32525f96d451ae4c2851'))
paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '1da49b7cda887dd84087ef8c060fcf6a'))
paddle.fluid.layers.elementwise_sub (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '992559c8327c61babd2ed25fc9047fbf'))
paddle.fluid.layers.elementwise_mul (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '213db11a61dcb0f31159d343cc35e2f5'))
paddle.fluid.layers.elementwise_max (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '409167a1409ec31b0d3a2f8852a7943f'))
paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4e1322836eb69473d5606bfe346c5375'))
paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'b9e7e9fa1ca28d8b6f07cc59eadb4a02'))
paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '614984304f810f3ddae6b489ec01296b'))
paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'a8c4b26d899246378e878f169582c7a4'))
paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '7fa4f12d3dad010f3862df271b31e4de'))
paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '39ee2e90c1ede44e47f279fc466f3151'))
paddle.fluid.layers.elementwise_sub (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '890017540bd2f982f80da81a98832609'))
paddle.fluid.layers.elementwise_mul (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '7994818219805a2ec34a37cd9baceeb7'))
paddle.fluid.layers.elementwise_max (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '3b3c2e528712552f6f44aef88796321d'))
paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '817e8ce2b39de9b4a94b1b6d592144e0'))
paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'b5e3964c8711058634cf5b57b4884258'))
paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4101ee1f9280f00dce54054ccc434890'))
paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '67e6101c31314d4082621e8e443cfb68'))
paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28'))
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'ebbf399d4e03190ce5dc9488f05c92f4'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'c39b647b6cf08e058d96ee503d5284fe'))

17
paddle/fluid/operators/elementwise/elementwise_max_op.cc
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@ -24,6 +24,23 @@ class ElementwiseMaxOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "Max"; }
std::string GetEquation() const override { return "Out = max(X, Y)"; }
void AddInputX() override {
AddInput(
"X",
"(Variable), The first tensor holding the elements to be compared.");
}
void AddInputY() override {
AddInput(
"Y",
"(Variable), The second tensor holding the elements to be compared.");
}
std::string GetOpFuntionality() const override {
return "Compare two tensors and returns a new tensor containing the "
"element-wise maxima.";
}
};
class ElementwiseMaxGradOpDescMaker : public framework::SingleGradOpDescMaker {

17
paddle/fluid/operators/elementwise/elementwise_min_op.cc
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@ -24,6 +24,23 @@ class ElementwiseMinOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "Min"; }
std::string GetEquation() const override { return "Out = min(X, Y)"; }
void AddInputX() override {
AddInput(
"X",
"(Variable), The first tensor holding the elements to be compared.");
}
void AddInputY() override {
AddInput(
"Y",
"(Variable), The second tensor holding the elements to be compared.");
}
std::string GetOpFuntionality() const override {
return "Compare two tensors and returns a new tensor containing the "
"element-wise minima.";
}
};
class ElementwiseMinGradOpDescMaker : public framework::SingleGradOpDescMaker {

62
paddle/fluid/operators/elementwise/elementwise_op.h
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@ -96,12 +96,15 @@ class ElementwiseOpInferVarType
class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() final {
AddInput("X", "(Tensor), The first input tensor of elementwise op.");
AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
AddOutput("Out", "The output of elementwise op.");
AddInputX();
AddInputY();
AddOpOutput();
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
"for broadcasting Y onto X.")
"(int, default -1). If X.dimension != Y.dimension,"
"Y.dimension must be a subsequence of x.dimension. And axis "
"is the start dimension index "
"for broadcasting Y onto X. ")
.SetDefault(-1)
.EqualGreaterThan(-1);
AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
@ -120,14 +123,41 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
"Defaults to \"\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("");
AddComment(string::Sprintf(R"DOC(
Elementwise %s Operator
AddOpComment();
}
protected:
virtual void AddInputX() {
AddInput("X", "(Tensor), The first input tensor of elementwise op.");
}
virtual void AddInputY() {
AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
}
virtual void AddOpOutput() {
AddOutput("Out",
"N-dimension tensor. A location into which the result is stored. "
"It's dimension "
"equals with x");
}
virtual void AddOpComment() { AddComment(GetCommentExamples()); }
virtual std::string GetOpFuntionality() const { return ""; }
virtual std::string GetName() const = 0;
virtual std::string GetEquation() const = 0;
std::string GetCommentExamples() const {
return string::Sprintf(R"DOC(
Elementwise %s Operator.
%s
The equation is:
$$%s$$
- $X$: a tensor of any dimension.
- $X$: a tensor of any dimension.
- $Y$: a tensor whose dimensions must be less than or equal to the dimensions of $X$.
There are two cases for this operator:
@ -137,10 +167,10 @@ There are two cases for this operator:
For case 2:
1. Broadcast $Y$ to match the shape of $X$, where $axis$ is the start dimension index
for broadcasting $Y$ onto $X$.
1. Broadcast $Y$ to match the shape of $X$, where $axis$ is the start dimension index
for broadcasting $Y$ onto $X$.
2. If $axis$ is -1 (default), $axis = rank(X) - rank(Y)$.
3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of
3. The trailing dimensions of size 1 for $Y$ will be ignored for the consideration of
subsequence, such as shape(Y) = (2, 1) => (2).
For example:
@ -154,17 +184,9 @@ For example:
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
The inputs $X$ and $Y$ can carry the different LoD information.
But the output only shares the LoD information with the input $X$.
)DOC",
GetName(), GetEquation()));
GetName(), GetOpFuntionality(), GetEquation());
}
protected:
virtual std::string GetName() const = 0;
virtual std::string GetEquation() const = 0;
};
class ElementwiseOpGrad : public framework::OperatorWithKernel {

9
paddle/fluid/operators/elementwise/elementwise_pow_op.cc
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@ -38,6 +38,15 @@ class ElementwisePowOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "Pow"; }
std::string GetEquation() const override { return "Out = X ^ Y"; }
void AddInputX() override { AddInput("X", "(Variable), The Base."); }
void AddInputY() override { AddInput("Y", "(Variable), The exponents."); }
std::string GetOpFuntionality() const override {
return "First tensor elements raised to powers from the second tensor, "
"element-wise.";
}
};
} // namespace operators
} // namespace paddle

9
python/paddle/fluid/layers/layer_function_generator.py
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@ -61,7 +61,9 @@ def escape_math(text):
_two_dollar_pattern_.sub(r"!!\1!!", text)))
def _generate_doc_string_(op_proto, additional_args_lines=None):
def _generate_doc_string_(op_proto,
additional_args_lines=None,
skip_attrs_set=None):
"""
Generate docstring by OpProto
@ -93,6 +95,11 @@ def _generate_doc_string_(op_proto, additional_args_lines=None):
skip_attrs.add("use_mkldnn")
skip_attrs.add("is_test")
skip_attrs.add("use_cudnn")
if skip_attrs_set:
for t in skip_attrs_set:
skip_attrs.add(t)
for each_attr in op_proto.attrs:
if each_attr.name in skip_attrs:
continue

153
python/paddle/fluid/layers/nn.py
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@ -11718,14 +11718,139 @@ def elementwise_mul(x, y, axis=-1, act=None, name=None):
def elementwise_max(x, y, axis=-1, act=None, name=None):
"""
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data():
return {
"x": np.array([2, 3, 4]),
"y": np.array([1, 5, 2])
}
x = fluid.layers.data(name="x", shape=[3], dtype='float32')
y = fluid.layers.data(name="y", shape=[3], dtype='float32')
z = fluid.layers.elementwise_max(x, y)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
print(z_value) #[2, 5, 4]
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data():
return {
"x": np.ones((2, 3, 4, 5)).astype('float32'),
"y": np.zeros((3, 4)).astype('float32')
}
x = fluid.layers.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.layers.data(name="y", shape=[3,4], dtype='float32')
z = fluid.layers.elementwise_max(x, y, axis=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
print(z_value)#[[[[1., 1., 1., 1., 1.] .... [1., 1., 1., 1., 1.]]]]
"""
return _elementwise_op(LayerHelper('elementwise_max', **locals()))
def elementwise_min(x, y, axis=-1, act=None, name=None):
"""
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data():
return {
"x": np.array([2, 3, 4]),
"y": np.array([1, 5, 2])
}
x = fluid.layers.data(name="x", shape=[3], dtype='float32')
y = fluid.layers.data(name="y", shape=[3], dtype='float32')
z = fluid.layers.elementwise_max(x, y)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
print(z_value) #[1, 3, 2]
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data():
return {
"x": np.ones((2, 3, 4, 5)).astype('float32'),
"y": np.zeros((3, 4)).astype('float32')
}
x = fluid.layers.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.layers.data(name="y", shape=[3,4], dtype='float32')
z = fluid.layers.elementwise_max(x, y, axis=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
print(z_value)#[[[[0., 0., 0., 0., 0.] .... [0., 0., 0., 0., 0.]]]]
"""
return _elementwise_op(LayerHelper('elementwise_min', **locals()))
def elementwise_pow(x, y, axis=-1, act=None, name=None):
"""
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
def gen_data():
return {
"x": np.array([2, 3, 4]),
"y": np.array([1, 5, 2])
}
x = fluid.layers.data(name="x", shape=[3], dtype='float32')
y = fluid.layers.data(name="y", shape=[3], dtype='float32')
z = fluid.layers.elementwise_pow(x, y)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(),
fetch_list=[z.name])
print(z_value) #[2, 243, 16]
"""
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
@ -11738,15 +11863,33 @@ def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
for func in [
elementwise_add,
elementwise_div,
elementwise_sub,
elementwise_mul,
elementwise_max,
elementwise_min,
elementwise_pow,
elementwise_min,
]:
op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
func.__doc__ = _generate_doc_string_(
op_proto,
additional_args_lines=[
"axis (int32, optional): If X.dimension != Y.dimension, \
Y.dimension must be a subsequence of x.dimension. \
And axis is the start dimension index for broadcasting Y onto X. ",
"act (string, optional): Activation applied to the output. \
Default is None. Details: :ref:`api_guide_activations_en` ",
"name (string, optional): Name of the output. \
Default is None. It's used to print debug info for developers. Details: \
:ref:`api_guide_Name` "
],
skip_attrs_set={"x_data_format", "y_data_format", "axis"
}) + """\n""" + str(func.__doc__)
for func in [
elementwise_mod,
elementwise_floordiv,
elementwise_add,
elementwise_div,
elementwise_sub,
elementwise_mul,
]:
op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
func.__doc__ = _generate_doc_string_(

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