From 4fd5a9b57396665e395f9855fbf255dbaf241348 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 11 Oct 2017 03:01:25 +0000 Subject: [PATCH] Deploy to GitHub Pages: 23407e7a649d07d0b92219d926179f1dcf6f9326 --- .../doc/_sources/design/selected_rows.md.txt | 74 ++++ develop/doc/design/selected_rows.html | 304 +++++++++++++++++ develop/doc/objects.inv | Bin 2844 -> 2865 bytes develop/doc/searchindex.js | 2 +- .../_sources/design/selected_rows.md.txt | 74 ++++ develop/doc_cn/design/selected_rows.html | 319 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 3421 -> 3442 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 773 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/selected_rows.md.txt create mode 100644 develop/doc/design/selected_rows.html create mode 100644 develop/doc_cn/_sources/design/selected_rows.md.txt create mode 100644 develop/doc_cn/design/selected_rows.html diff --git a/develop/doc/_sources/design/selected_rows.md.txt b/develop/doc/_sources/design/selected_rows.md.txt new file mode 100644 index 00000000000..9e6f3b20cbc --- /dev/null +++ b/develop/doc/_sources/design/selected_rows.md.txt @@ -0,0 +1,74 @@ +# Design Doc: Selected Rows + +`SelectedRows` is a kind of sparse tensor data type, which is designed to support `embedding` operators. The gradient of embedding table is a sparse tensor. Only a few rows are non-zero values in that tensor. It is straightforward to represent the sparse tensor by the following sparse tensor data structure: + +```cpp +class SelectedRows { + private: + vector rows_; + Tensor value_; + int height_; +}; +``` + +The field `height_` shows the first dimension of `SelectedRows`. The `rows` are the indices of which rows of `SelectedRows` are non-zeros. The `value_` field is an N-dim tensor and shape is `[rows.size() /* NUM_ROWS */, ...]`, which supplies values for each row. The dimension of `SelectedRows` satisfies `[height_] + value_.shape[1:]`. + +Suppose that a SelectedRows-typed variable `x` has many rows, but only two of them have values -- row 73 is `[1, 2]` and row 84 is `[3, 4]`, the `SelectedRows` representation would be: + +``` +x = SelectedRow { + rows = [73, 84], + value = [[1, 2], [3,4]] +} +``` + + +## SelectedRows in Protobuf + +`SelectedRows` is a kind of `Variable`. `VarDesc` in protobuf should describe the `SelectedRows` information. Only the tensor dimension of a `SelectedRows` will be described in compile-time since the `rows_` and `value_` are related to training data. +So we use `TensorDesc` to unify `data_type` and `dims`. A LodTensorDesc contains a `TensorDesc` and `lod_level`. The description of `SelectedRows` is a Tensor description. + +```proto +message TensorDesc { + required DataType data_type = 1; + repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480] +} + +message LodTensorDesc { + required TensorDesc tensor = 1; + optional int lod_level = 2; +} + +message VarDesc { + required string name = 1; + enum VarType { + LOD_TENSOR = 0; + SELECTED_ROWS = 1; + } + required VarType type = 2; + optional LodTensorDesc lod_desc = 3; + optional TensorDesc selected_rows_desc = 4; + optional bool persistable = 5 [ default = false ]; +} +``` + +## InferShape for Selected Rows + +Just like `LoD` information, `InferShape` method will inference output tensor type as well. The operator should decide whether its output is a `SelectedRows` or `Dense` tensor. + +For example, the gradient operator of `TableLookup` will always generate `SelectedRows`. Its `InferShape` method should be like following + +```cpp +void TableLookupGrad::InferShape(context) { + ... + context.SetDataType("Embedding.Grad", kSelectedRows); +} +``` + + +## Sparse Operators + +There are several operators should be written to support `SelectedRows`. They are: + +1. Operators which generates `SelectedRows` gradient. e.g. Gradient of `TableLookupOp`. +2. Optimize operators which support `SelectedRows` gradient. e.g. `SGD` or `AdaGrad` for `SelectedRows`. However, there should be only one `SGD` operator. `OpWithKernel::Run` should select a suitable kernel for both `dense` tensor or `SelectedRows`. diff --git a/develop/doc/design/selected_rows.html b/develop/doc/design/selected_rows.html new file mode 100644 index 00000000000..cfdf471e64f --- /dev/null +++ b/develop/doc/design/selected_rows.html @@ -0,0 +1,304 @@ + + + + + + + + + + + Design Doc: Selected Rows — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: Selected Rows
    • +
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+ +
+

Design Doc: Selected Rows

+

SelectedRows is a kind of sparse tensor data type, which is designed to support embedding operators. The gradient of embedding table is a sparse tensor. Only a few rows are non-zero values in that tensor. It is straightforward to represent the sparse tensor by the following sparse tensor data structure:

+
class SelectedRows {
+ private:
+  vector<int> rows_;
+  Tensor value_;
+  int height_;
+};
+
+
+

The field height_ shows the first dimension of SelectedRows. The rows are the indices of which rows of SelectedRows are non-zeros. The value_ field is an N-dim tensor and shape is [rows.size() /* NUM_ROWS */, ...], which supplies values for each row. The dimension of SelectedRows satisfies [height_] + value_.shape[1:].

+

Suppose that a SelectedRows-typed variable x has many rows, but only two of them have values – row 73 is [1, 2] and row 84 is [3, 4], the SelectedRows representation would be:

+
x = SelectedRow {
+  rows = [73, 84],
+  value = [[1, 2], [3,4]]
+}
+
+
+
+

SelectedRows in Protobuf

+

SelectedRows is a kind of Variable. VarDesc in protobuf should describe the SelectedRows information. Only the tensor dimension of a SelectedRows will be described in compile-time since the rows_ and value_ are related to training data. +So we use TensorDesc to unify data_type and dims. A LodTensorDesc contains a TensorDesc and lod_level. The description of SelectedRows is a Tensor description.

+
message TensorDesc {
+  required DataType data_type = 1;
+  repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
+}
+
+message LodTensorDesc {
+  required TensorDesc tensor = 1;
+  optional int lod_level = 2;
+}
+
+message VarDesc {
+  required string name = 1;
+  enum VarType { 
+    LOD_TENSOR = 0;
+    SELECTED_ROWS = 1;
+  }
+  required VarType type = 2;
+  optional LodTensorDesc lod_desc = 3;
+  optional TensorDesc selected_rows_desc = 4;
+  optional bool persistable = 5 [ default = false ];
+}
+
+
+
+
+

InferShape for Selected Rows

+

Just like LoD information, InferShape method will inference output tensor type as well. The operator should decide whether its output is a SelectedRows or Dense tensor.

+

For example, the gradient operator of TableLookup will always generate SelectedRows. Its InferShape method should be like following

+
void TableLookupGrad::InferShape(context) {
+  ...
+  context.SetDataType("Embedding.Grad", kSelectedRows);
+}
+
+
+
+
+

Sparse Operators

+

There are several operators should be written to support SelectedRows. They are:

+
    +
  1. Operators which generates SelectedRows gradient. e.g. Gradient of TableLookupOp.
    2. +
  2. Optimize operators which support SelectedRows gradient. e.g. SGD or AdaGrad for SelectedRows. However, there should be only one SGD operator. OpWithKernel::Run should select a suitable kernel for both dense tensor or SelectedRows.
    3. +
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/selected_rows.md.txt b/develop/doc_cn/_sources/design/selected_rows.md.txt new file mode 100644 index 00000000000..9e6f3b20cbc --- /dev/null +++ b/develop/doc_cn/_sources/design/selected_rows.md.txt @@ -0,0 +1,74 @@ +# Design Doc: Selected Rows + +`SelectedRows` is a kind of sparse tensor data type, which is designed to support `embedding` operators. The gradient of embedding table is a sparse tensor. Only a few rows are non-zero values in that tensor. It is straightforward to represent the sparse tensor by the following sparse tensor data structure: + +```cpp +class SelectedRows { + private: + vector rows_; + Tensor value_; + int height_; +}; +``` + +The field `height_` shows the first dimension of `SelectedRows`. The `rows` are the indices of which rows of `SelectedRows` are non-zeros. The `value_` field is an N-dim tensor and shape is `[rows.size() /* NUM_ROWS */, ...]`, which supplies values for each row. The dimension of `SelectedRows` satisfies `[height_] + value_.shape[1:]`. + +Suppose that a SelectedRows-typed variable `x` has many rows, but only two of them have values -- row 73 is `[1, 2]` and row 84 is `[3, 4]`, the `SelectedRows` representation would be: + +``` +x = SelectedRow { + rows = [73, 84], + value = [[1, 2], [3,4]] +} +``` + + +## SelectedRows in Protobuf + +`SelectedRows` is a kind of `Variable`. `VarDesc` in protobuf should describe the `SelectedRows` information. Only the tensor dimension of a `SelectedRows` will be described in compile-time since the `rows_` and `value_` are related to training data. +So we use `TensorDesc` to unify `data_type` and `dims`. A LodTensorDesc contains a `TensorDesc` and `lod_level`. The description of `SelectedRows` is a Tensor description. + +```proto +message TensorDesc { + required DataType data_type = 1; + repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480] +} + +message LodTensorDesc { + required TensorDesc tensor = 1; + optional int lod_level = 2; +} + +message VarDesc { + required string name = 1; + enum VarType { + LOD_TENSOR = 0; + SELECTED_ROWS = 1; + } + required VarType type = 2; + optional LodTensorDesc lod_desc = 3; + optional TensorDesc selected_rows_desc = 4; + optional bool persistable = 5 [ default = false ]; +} +``` + +## InferShape for Selected Rows + +Just like `LoD` information, `InferShape` method will inference output tensor type as well. The operator should decide whether its output is a `SelectedRows` or `Dense` tensor. + +For example, the gradient operator of `TableLookup` will always generate `SelectedRows`. Its `InferShape` method should be like following + +```cpp +void TableLookupGrad::InferShape(context) { + ... + context.SetDataType("Embedding.Grad", kSelectedRows); +} +``` + + +## Sparse Operators + +There are several operators should be written to support `SelectedRows`. They are: + +1. Operators which generates `SelectedRows` gradient. e.g. Gradient of `TableLookupOp`. +2. Optimize operators which support `SelectedRows` gradient. e.g. `SGD` or `AdaGrad` for `SelectedRows`. However, there should be only one `SGD` operator. `OpWithKernel::Run` should select a suitable kernel for both `dense` tensor or `SelectedRows`. diff --git a/develop/doc_cn/design/selected_rows.html b/develop/doc_cn/design/selected_rows.html new file mode 100644 index 00000000000..d70938effff --- /dev/null +++ b/develop/doc_cn/design/selected_rows.html @@ -0,0 +1,319 @@ + + + + + + + + + + + Design Doc: Selected Rows — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Design Doc: Selected Rows

+

SelectedRows is a kind of sparse tensor data type, which is designed to support embedding operators. The gradient of embedding table is a sparse tensor. Only a few rows are non-zero values in that tensor. It is straightforward to represent the sparse tensor by the following sparse tensor data structure:

+
class SelectedRows {
+ private:
+  vector<int> rows_;
+  Tensor value_;
+  int height_;
+};
+
+
+

The field height_ shows the first dimension of SelectedRows. The rows are the indices of which rows of SelectedRows are non-zeros. The value_ field is an N-dim tensor and shape is [rows.size() /* NUM_ROWS */, ...], which supplies values for each row. The dimension of SelectedRows satisfies [height_] + value_.shape[1:].

+

Suppose that a SelectedRows-typed variable x has many rows, but only two of them have values – row 73 is [1, 2] and row 84 is [3, 4], the SelectedRows representation would be:

+
x = SelectedRow {
+  rows = [73, 84],
+  value = [[1, 2], [3,4]]
+}
+
+
+
+

SelectedRows in Protobuf

+

SelectedRows is a kind of Variable. VarDesc in protobuf should describe the SelectedRows information. Only the tensor dimension of a SelectedRows will be described in compile-time since the rows_ and value_ are related to training data. +So we use TensorDesc to unify data_type and dims. A LodTensorDesc contains a TensorDesc and lod_level. The description of SelectedRows is a Tensor description.

+
message TensorDesc {
+  required DataType data_type = 1;
+  repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
+}
+
+message LodTensorDesc {
+  required TensorDesc tensor = 1;
+  optional int lod_level = 2;
+}
+
+message VarDesc {
+  required string name = 1;
+  enum VarType { 
+    LOD_TENSOR = 0;
+    SELECTED_ROWS = 1;
+  }
+  required VarType type = 2;
+  optional LodTensorDesc lod_desc = 3;
+  optional TensorDesc selected_rows_desc = 4;
+  optional bool persistable = 5 [ default = false ];
+}
+
+
+
+
+

InferShape for Selected Rows

+

Just like LoD information, InferShape method will inference output tensor type as well. The operator should decide whether its output is a SelectedRows or Dense tensor.

+

For example, the gradient operator of TableLookup will always generate SelectedRows. Its InferShape method should be like following

+
void TableLookupGrad::InferShape(context) {
+  ...
+  context.SetDataType("Embedding.Grad", kSelectedRows);
+}
+
+
+
+
+

Sparse Operators

+

There are several operators should be written to support SelectedRows. They are:

+
    +
  1. Operators which generates SelectedRows gradient. e.g. Gradient of TableLookupOp.
    2. +
  2. Optimize operators which support SelectedRows gradient. e.g. SGD or AdaGrad for SelectedRows. However, there should be only one SGD operator. OpWithKernel::Run should select a suitable kernel for both dense tensor or SelectedRows.
    3. +
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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