From eaf9796871d2bca08b2e5cdf59df3a390ff20f03 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 17 Oct 2017 02:35:44 +0000 Subject: [PATCH] Deploy to GitHub Pages: f43b1a90d8d0dccb54b4243f33df87660f7679d6 --- .../doc/_sources/design/infer_var_type.md.txt | 78 +++++ develop/doc/design/infer_var_type.html | 303 +++++++++++++++++ develop/doc/objects.inv | Bin 2694 -> 2720 bytes develop/doc/searchindex.js | 2 +- .../_sources/design/infer_var_type.md.txt | 78 +++++ develop/doc_cn/design/infer_var_type.html | 318 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 3262 -> 3288 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 779 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/infer_var_type.md.txt create mode 100644 develop/doc/design/infer_var_type.html create mode 100644 develop/doc_cn/_sources/design/infer_var_type.md.txt create mode 100644 develop/doc_cn/design/infer_var_type.html diff --git a/develop/doc/_sources/design/infer_var_type.md.txt b/develop/doc/_sources/design/infer_var_type.md.txt new file mode 100644 index 00000000000..d9d5397becb --- /dev/null +++ b/develop/doc/_sources/design/infer_var_type.md.txt @@ -0,0 +1,78 @@ +# Design Doc: InferVarType + +## The Problem Posed + +The variable in our design can hold variant types. Such as `LoDTensor` and `SelectedRows`. An operator should be able to inference the variable types of its output. + +For example, a `lookup table` operator takes two `LoDTensor`; one is a float tensor as the embedding table, the other is an int tensor as word ID. The gradient operator of `lookup table` will generate a `SelectedRows` as its output. A `sum` operator can take both `LoDTensor` and `SelectedRows` as its inputs and will generate a `LoDTensor` if any of its inputs is `LoDTensor`, otherwise, the `sum` operator will generate `SelectedRows` as its output. + +The variable type will be constant at runtime. Every variable's type can either be set by the user (input data and parameter) or be inferred by the operator in compile time. + +## Proposed Solution + +The `InferVarType` is a compile-time function which is registered to each operator. The inferface of that function is: + + +```c++ +using InferVarTypeFN = std::function< + void (const OpDescBind& /*op_desc*/, BlockDescBind* /*block*/)>; +``` + +It takes an operator description as its input and will write the output variable type and store them in block description. + +The `InferVarTypeFN` will be registered in `OpInfo`, to replace `infer_var_type_` field. The `OpInfo` should be + +```cpp +struct OpInfo { + InferVarTypeFN infer_var_type_; + ... +}; +``` + +The default `InferVarType` will set output type as `LoDTensor`. It can be done by `GetInferVarType()`. + +```cpp +void DefaultInferVarType(const OpDescBind& op_desc, BlockDescBind* block) { + // set the output type of variable as `LoDTensor`. + // ... +} + +struct OpInfo { + InferVarTypeFN infer_var_type_; + InferVarTypeFN GetInferVarType() const { + if (infer_var_type_) { + return infer_var_type_; + } else { + return DefaultInferVarType; + } + } +}; +``` + +## Register InferVarType + +We provide a thin base class for registering an `InferVarTypeFN`. To use a base class will ease the implementation of registry since we can detect the registry entry is an `InferVarTypeFN` or not. + +```cpp +class VarTypeInferer { +public: + virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const = 0; +} +``` + +Operator developers can write the specialize `VarTypeInferer` as follow. + +```cpp +class SpecialVarTypeInferer : public VarTypeInferer { +public: + virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const { + // .. own logic + } +} +``` + +Then user can register the `InferVarType` just like `GradOpDescMaker` and `OpInfoMaker`. + +``` +REGISTER_OPERATOR(some_op, OpType, SpecialVarTypeInferer, ...); +``` diff --git a/develop/doc/design/infer_var_type.html b/develop/doc/design/infer_var_type.html new file mode 100644 index 00000000000..27c610dc611 --- /dev/null +++ b/develop/doc/design/infer_var_type.html @@ -0,0 +1,303 @@ + + + + + + + + + + + Design Doc: InferVarType — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: InferVarType
    • +
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+

Design Doc: InferVarType

+
+

The Problem Posed

+

The variable in our design can hold variant types. Such as LoDTensor and SelectedRows. An operator should be able to inference the variable types of its output.

+

For example, a lookup table operator takes two LoDTensor; one is a float tensor as the embedding table, the other is an int tensor as word ID. The gradient operator of lookup table will generate a SelectedRows as its output. A sum operator can take both LoDTensor and SelectedRows as its inputs and will generate a LoDTensor if any of its inputs is LoDTensor, otherwise, the sum operator will generate SelectedRows as its output.

+

The variable type will be constant at runtime. Every variable’s type can either be set by the user (input data and parameter) or be inferred by the operator in compile time.

+
+
+

Proposed Solution

+

The InferVarType is a compile-time function which is registered to each operator. The inferface of that function is:

+
using InferVarTypeFN = std::function<
+    void (const OpDescBind& /*op_desc*/, BlockDescBind* /*block*/)>;
+
+
+

It takes an operator description as its input and will write the output variable type and store them in block description.

+

The InferVarTypeFN will be registered in OpInfo, to replace infer_var_type_ field. The OpInfo should be

+
struct OpInfo {
+  InferVarTypeFN infer_var_type_;
+  ...
+};
+
+
+

The default InferVarType will set output type as LoDTensor. It can be done by GetInferVarType().

+
void DefaultInferVarType(const OpDescBind& op_desc, BlockDescBind* block) {
+  // set the output type of variable as `LoDTensor`.
+  // ...
+}
+
+struct OpInfo {
+  InferVarTypeFN infer_var_type_;
+  InferVarTypeFN GetInferVarType() const {
+    if (infer_var_type_) {
+      return infer_var_type_;
+    } else {
+      return DefaultInferVarType;
+    }
+  }
+};
+
+
+
+
+

Register InferVarType

+

We provide a thin base class for registering an InferVarTypeFN. To use a base class will ease the implementation of registry since we can detect the registry entry is an InferVarTypeFN or not.

+
class VarTypeInferer {
+public:
+  virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const = 0;
+}
+
+
+

Operator developers can write the specialize VarTypeInferer as follow.

+
class SpecialVarTypeInferer : public VarTypeInferer {
+public:
+  virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const {
+    // .. own logic
+  }
+}
+
+
+

Then user can register the InferVarType just like GradOpDescMaker and OpInfoMaker.

+
REGISTER_OPERATOR(some_op, OpType, SpecialVarTypeInferer, ...);
+
+
+
+
+ + +
+
+
+ + +
+ +
+

+ © 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/objects.inv b/develop/doc/objects.inv index 6daa2614507827e2c4c9d753aa698055f281b875..bb2e402d61bf09f83c65f6cb1717ad7ac366b41c 100644 GIT binary patch delta 2604 zcmV+{3e)w56`&Q6kADD7$%WNm+{SAe_eMprEG& zXd07qSVcm{_hJ^IzyKBNni~y{4|-nsI=BaA6Td+}NF`@+uJ5C_XKumrEj8oM;v zKs;0*o8l>vOUBRESW<8pkVAXbfMJ=WbNcZ*OG*|4bW~h6kTS*+nohfMEQ8%_b5UR@ z=k-XcWy7z$7@ZKT;s6{V!Y4Q)2&b{8oDx~y*qp^A34e`F-|jec1O7-Yp`l*Nx#L4T zPs4;wY2-mFX$EDa;+NV1Wz|7CA)KUCwIyF{;$Ds6v=lLCA4w`75Gv3q~3i44- zQb}J9kcnh;d*fq}1EgeHt5IU}gzLYhlkYQw&LB3`?kkfilnp}-2&A7Bx(8o=R< zBpDo|(0?9_P|$e>A&JhXienKf_w;D@A199yfmIM##=s~DK!M^|1V({J>o(R(axVW* zb}Hzag{~%T+v4szg^h@(Rf2>Y_yA+4ig+DJRm`&>ff1n46QeW)pF{O3rFT-630cLzaeHN zTz+bxpJyt1-Uix8?&*II$$}&<@+7(qDo9PD1`y>!s(5-UJSD86B`RTD^aS4?T*+$e z0>p)$({pmGtu=!BuZvhu`8UKYMD71xG>^lWNKBKE*6L#ydd8NL^>cxzq^|_BqGIcN 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operator","How to use Eigen in Paddle","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Cluster bootstrapping tool survey","<no title>","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Model Zoo - ImageNet","Quick 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/infer_var_type.md.txt b/develop/doc_cn/_sources/design/infer_var_type.md.txt new file mode 100644 index 00000000000..d9d5397becb --- /dev/null +++ b/develop/doc_cn/_sources/design/infer_var_type.md.txt @@ -0,0 +1,78 @@ +# Design Doc: InferVarType + +## The Problem Posed + +The variable in our design can hold variant types. Such as `LoDTensor` and `SelectedRows`. An operator should be able to inference the variable types of its output. + +For example, a `lookup table` operator takes two `LoDTensor`; one is a float tensor as the embedding table, the other is an int tensor as word ID. The gradient operator of `lookup table` will generate a `SelectedRows` as its output. A `sum` operator can take both `LoDTensor` and `SelectedRows` as its inputs and will generate a `LoDTensor` if any of its inputs is `LoDTensor`, otherwise, the `sum` operator will generate `SelectedRows` as its output. + +The variable type will be constant at runtime. Every variable's type can either be set by the user (input data and parameter) or be inferred by the operator in compile time. + +## Proposed Solution + +The `InferVarType` is a compile-time function which is registered to each operator. The inferface of that function is: + + +```c++ +using InferVarTypeFN = std::function< + void (const OpDescBind& /*op_desc*/, BlockDescBind* /*block*/)>; +``` + +It takes an operator description as its input and will write the output variable type and store them in block description. + +The `InferVarTypeFN` will be registered in `OpInfo`, to replace `infer_var_type_` field. The `OpInfo` should be + +```cpp +struct OpInfo { + InferVarTypeFN infer_var_type_; + ... +}; +``` + +The default `InferVarType` will set output type as `LoDTensor`. It can be done by `GetInferVarType()`. + +```cpp +void DefaultInferVarType(const OpDescBind& op_desc, BlockDescBind* block) { + // set the output type of variable as `LoDTensor`. + // ... +} + +struct OpInfo { + InferVarTypeFN infer_var_type_; + InferVarTypeFN GetInferVarType() const { + if (infer_var_type_) { + return infer_var_type_; + } else { + return DefaultInferVarType; + } + } +}; +``` + +## Register InferVarType + +We provide a thin base class for registering an `InferVarTypeFN`. To use a base class will ease the implementation of registry since we can detect the registry entry is an `InferVarTypeFN` or not. + +```cpp +class VarTypeInferer { +public: + virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const = 0; +} +``` + +Operator developers can write the specialize `VarTypeInferer` as follow. + +```cpp +class SpecialVarTypeInferer : public VarTypeInferer { +public: + virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const { + // .. own logic + } +} +``` + +Then user can register the `InferVarType` just like `GradOpDescMaker` and `OpInfoMaker`. + +``` +REGISTER_OPERATOR(some_op, OpType, SpecialVarTypeInferer, ...); +``` diff --git a/develop/doc_cn/design/infer_var_type.html b/develop/doc_cn/design/infer_var_type.html new file mode 100644 index 00000000000..d05cd89af1a --- /dev/null +++ b/develop/doc_cn/design/infer_var_type.html @@ -0,0 +1,318 @@ + + + + + + + + + + + Design Doc: InferVarType — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
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+ +
+ + + + +
+ + + + + + + + + + + +
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    + +
  • Design Doc: InferVarType
    • +
+
+ +
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+ +
+

Design Doc: InferVarType

+
+

The Problem Posed

+

The variable in our design can hold variant types. Such as LoDTensor and SelectedRows. An operator should be able to inference the variable types of its output.

+

For example, a lookup table operator takes two LoDTensor; one is a float tensor as the embedding table, the other is an int tensor as word ID. The gradient operator of lookup table will generate a SelectedRows as its output. A sum operator can take both LoDTensor and SelectedRows as its inputs and will generate a LoDTensor if any of its inputs is LoDTensor, otherwise, the sum operator will generate SelectedRows as its output.

+

The variable type will be constant at runtime. Every variable’s type can either be set by the user (input data and parameter) or be inferred by the operator in compile time.

+
+
+

Proposed Solution

+

The InferVarType is a compile-time function which is registered to each operator. The inferface of that function is:

+
using InferVarTypeFN = std::function<
+    void (const OpDescBind& /*op_desc*/, BlockDescBind* /*block*/)>;
+
+
+

It takes an operator description as its input and will write the output variable type and store them in block description.

+

The InferVarTypeFN will be registered in OpInfo, to replace infer_var_type_ field. The OpInfo should be

+
struct OpInfo {
+  InferVarTypeFN infer_var_type_;
+  ...
+};
+
+
+

The default InferVarType will set output type as LoDTensor. It can be done by GetInferVarType().

+
void DefaultInferVarType(const OpDescBind& op_desc, BlockDescBind* block) {
+  // set the output type of variable as `LoDTensor`.
+  // ...
+}
+
+struct OpInfo {
+  InferVarTypeFN infer_var_type_;
+  InferVarTypeFN GetInferVarType() const {
+    if (infer_var_type_) {
+      return infer_var_type_;
+    } else {
+      return DefaultInferVarType;
+    }
+  }
+};
+
+
+
+
+

Register InferVarType

+

We provide a thin base class for registering an InferVarTypeFN. To use a base class will ease the implementation of registry since we can detect the registry entry is an InferVarTypeFN or not.

+
class VarTypeInferer {
+public:
+  virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const = 0;
+}
+
+
+

Operator developers can write the specialize VarTypeInferer as follow.

+
class SpecialVarTypeInferer : public VarTypeInferer {
+public:
+  virtual void operator()(const OpDescBind& op_desc, BlockDescBind* block) const {
+    // .. own logic
+  }
+}
+
+
+

Then user can register the InferVarType just like GradOpDescMaker and OpInfoMaker.

+
REGISTER_OPERATOR(some_op, OpType, SpecialVarTypeInferer, ...);
+
+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

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