提交 63b2e98f 编写于 作者: X Xin Pan

Explain LoD and a few other concepts

test=develop
上级 e1904ac2
...@@ -157,7 +157,50 @@ PYBIND11_PLUGIN(core) { ...@@ -157,7 +157,50 @@ PYBIND11_PLUGIN(core) {
.def("_get_double_element", TensorGetElement<double>) .def("_get_double_element", TensorGetElement<double>)
.def("_dtype", [](Tensor &self) { return ToDataType(self.type()); }); .def("_dtype", [](Tensor &self) { return ToDataType(self.type()); });
py::class_<LoDTensor, Tensor>(m, "LoDTensor") py::class_<LoDTensor, Tensor>(m, "LoDTensor", R"DOC(
LoDTensor is a Tensor with optional LoD information.
np.array(lod_tensor) can convert LoDTensor to numpy array.
lod_tensor.lod() can retrieve the LoD information.
LoD is short for Level of Details and is usually used for varied sequence
length. You can skip the following comment if you don't need optional LoD.
For example:
A LoDTensor X can look like the example below. It contains 2 sequences.
The first has length 2 and the second has length 3, as described by x.lod.
The first tensor dimension 6=2+3 is calculated from LoD if it's available.
It means the total number of sequence element. In X, each element has 2
columns, hence [6, 2].
x.lod = [[2, 3]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.shape = [6, 2]
LoD can have multiple levels (for example, a paragraph can have multiple
sentences and a sentence can have multiple words). In the following
LodTensor Y, the lod_level is 2. It means there are 2 sequence, the
first sequence length is 2 (has 2 sub-sequences), the second one's
length is 1. The first sequence's 2 sub-sequences have length 2 and 2,
respectively. And the second sequence's 1 sub-sequence has length 3.
y.lod = [[2 1], [2 2 3]]
y.shape = [2+2+3, ...]
Note:
In above description, LoD is length-based. In Paddle internal
implementation, lod is offset-based. Hence, internally,
y.lod is represented as [[0, 2, 3], [0, 2, 4, 7]] (length-based
equivlent would be [[2-0, 3-2], [2-0, 4-2, 7-4]]).
Sometimes LoD is called recursive_sequence_length to be more
self-explanatory. In this case, it must be length-based. Due to history
reasons. when LoD is called lod in public API, it might be offset-based.
Users should be careful about it.
)DOC")
.def_buffer( .def_buffer(
[](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); }) [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
.def("__init__", .def("__init__",
......
...@@ -56,7 +56,11 @@ def data(name, ...@@ -56,7 +56,11 @@ def data(name,
Args: Args:
name(str): The name/alias of the function name(str): The name/alias of the function
shape(list): Tuple declaring the shape. shape(list): Tuple declaring the shape.
append_batch_size(bool): Whether or not to append the data as a batch. append_batch_size(bool):
1. If true, it prepends -1 to the shape.
For example if shape=[1], the resulting shape is [-1, 1].
2. If shape contains -1, such as shape=[1, -1],
append_batch_size will be enforced to be be False (ineffective).
dtype(int|float): The type of data : float32, float_16, int etc dtype(int|float): The type of data : float32, float_16, int etc
type(VarType): The output type. By default it is LOD_TENSOR. type(VarType): The output type. By default it is LOD_TENSOR.
lod_level(int): The LoD Level. 0 means the input data is not a sequence. lod_level(int): The LoD Level. 0 means the input data is not a sequence.
......
...@@ -100,7 +100,7 @@ def create_global_var(shape, ...@@ -100,7 +100,7 @@ def create_global_var(shape,
force_cpu=False, force_cpu=False,
name=None): name=None):
""" """
Create a new variable in the global block(block 0). Create a new tensor variable with value in the global block(block 0).
Args: Args:
shape(list[int]): shape of the variable shape(list[int]): shape of the variable
......
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