From f7e4b863340b19ff112efc52f1a83e1466b2f302 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Thu, 19 Oct 2017 02:07:53 +0000 Subject: [PATCH] Deploy to GitHub Pages: 6a03a4d9101e97961865ca559d15def3ac025d7a --- develop/doc/_sources/design/prune.md.txt | 63 ++++ .../_sources/design/refactorization.md.txt | 5 - develop/doc/design/prune.html | 297 +++++++++++++++++ develop/doc/design/refactorization.html | 5 - develop/doc/objects.inv | Bin 2740 -> 2751 bytes develop/doc/searchindex.js | 2 +- develop/doc_cn/_sources/design/prune.md.txt | 63 ++++ .../_sources/design/refactorization.md.txt | 5 - develop/doc_cn/design/prune.html | 312 ++++++++++++++++++ develop/doc_cn/design/refactorization.html | 5 - develop/doc_cn/objects.inv | Bin 3306 -> 3318 bytes develop/doc_cn/searchindex.js | 2 +- 12 files changed, 737 insertions(+), 22 deletions(-) create mode 100644 develop/doc/_sources/design/prune.md.txt create mode 100644 develop/doc/design/prune.html create mode 100644 develop/doc_cn/_sources/design/prune.md.txt create mode 100644 develop/doc_cn/design/prune.html diff --git a/develop/doc/_sources/design/prune.md.txt b/develop/doc/_sources/design/prune.md.txt new file mode 100644 index 00000000000..4a5cf10c79a --- /dev/null +++ b/develop/doc/_sources/design/prune.md.txt @@ -0,0 +1,63 @@ +# Prune + +## Motivation + +We want to support running inference, training and checkpointing in one `ProgramDesc`. We implement +`void Prune(const ProgramDesc* input, ProgramDesc* output)` function, which takes a `ProgramDesc` +and generate a pruned `ProgramDesc`. + +## Challenge + +Pruning need to support both variables and operators being evaluation targets. Consider the following +different situations. + +```python +# Case 1: run foward pass. +cost_np = session.run(target=cost) +# Case 2: run backward passing. +opts_np, _ = session.run(target=[cost, opt]) +# Case 3: run checkpointing +_ = session.run(target=checkpoint) +``` + +## Solution + +To support evaluation of operators, we add `is_target` field in the `OpDesc`. + +```c++ +message OpDesc { + required string type = 3; + repeated Var inputs = 1; + repeated Var outputs = 2; + repeated Attr attrs = 4; + optional bool is_target = 5 [ default = false ]; +}; +``` + +To support evaluation of variables, we add [fetch_op](https://github.com/PaddlePaddle/Paddle/pull/4599). +For each variable in the `target`, we insert a `fetch_op` into the `ProgramDesc` with `variable` being +`fetch_op`'s input. Then we also set `fetch_op` is a target. + +### Algorithm + +If an operator needs to be run, it must fall into one of the following cases: + +1. It is the target. +2. It is depended by some other ops, meaning its output is some other op's input. + +The first case can be checked by `op_desc.is_traget()` . The second case can be implement as + +```c++ +bool HasDependentVar(const OpDesc& op_desc, const std::set& dependent_vars) { + for (auto& var : op_desc.outputs()) { + for (auto& argu : var.arguments()) { + if (dependent_vars.count(argu) != 0) { + return true; + } + } + } + return false; +} +``` + +Then the whole algorithm can be implemented as the following [code](https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc). diff --git a/develop/doc/_sources/design/refactorization.md.txt b/develop/doc/_sources/design/refactorization.md.txt index ec51aa1a0ec..f93d6155e17 100644 --- a/develop/doc/_sources/design/refactorization.md.txt +++ b/develop/doc/_sources/design/refactorization.md.txt @@ -177,9 +177,6 @@ REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class) REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) ``` -### USE Macros -Make sure the registration process is executed and linked. - --- # Registration Process 1. Write an Op class and its gradient Op class, if required. @@ -188,8 +185,6 @@ Make sure the registration process is executed and linked. 1. Call maker class to complete `proto` and `checker` 2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap` -4. Invoke the `USE` macro in which the Op is used to make sure that it is linked. - --- # Backward Module (1/2) ### Create Backward Operator diff --git a/develop/doc/design/prune.html b/develop/doc/design/prune.html new file mode 100644 index 00000000000..d10e5b89f8f --- /dev/null +++ b/develop/doc/design/prune.html @@ -0,0 +1,297 @@ + + + + + + + + + + + Prune — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Prune
    • +
+
+ +
+
+
+
+ +
+

Prune

+
+

Motivation

+

We want to support running inference, training and checkpointing in one ProgramDesc. We implement +void Prune(const ProgramDesc* input, ProgramDesc* output) function, which takes a ProgramDesc +and generate a pruned ProgramDesc.

+
+
+

Challenge

+

Pruning need to support both variables and operators being evaluation targets. Consider the following +different situations.

+
# Case 1: run foward pass.
+cost_np = session.run(target=cost)
+# Case 2: run backward passing.
+opts_np, _ = session.run(target=[cost, opt])
+# Case 3: run checkpointing
+_ = session.run(target=checkpoint)
+
+
+
+
+

Solution

+

To support evaluation of operators, we add is_target field in the OpDesc.

+
message OpDesc {
+  required string type = 3;
+  repeated Var inputs = 1;
+  repeated Var outputs = 2;
+  repeated Attr attrs = 4;
+  optional bool is_target = 5 [ default = false ];
+};
+
+
+

To support evaluation of variables, we add fetch_op. +For each variable in the target, we insert a fetch_op into the ProgramDesc with variable being +fetch_op‘s input. Then we also set fetch_op is a target.

+
+

Algorithm

+

If an operator needs to be run, it must fall into one of the following cases:

+
    +
  1. It is the target.
    2. +
  2. It is depended by some other ops, meaning its output is some other op’s input.
    3. +
+

The first case can be checked by op_desc.is_traget() . The second case can be implement as

+
bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {
+  for (auto& var : op_desc.outputs()) {
+    for (auto& argu : var.arguments()) {
+      if (dependent_vars.count(argu) != 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+
+

Then the whole algorithm can be implemented as the following code.

+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/design/refactorization.html b/develop/doc/design/refactorization.html index c280706efc9..af5f60b3af9 100644 --- a/develop/doc/design/refactorization.html +++ b/develop/doc/design/refactorization.html @@ -393,10 +393,6 @@ -
-

USE Macros

-

Make sure the registration process is executed and linked.

-
@@ -409,7 +405,6 @@
  • Using the completed proto and checker, it will add a new key-value pair to the OpInfoMap
    • -
  • Invoke the USE macro in which the Op is used to make sure that it is linked.
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/prune.md.txt b/develop/doc_cn/_sources/design/prune.md.txt new file mode 100644 index 00000000000..4a5cf10c79a --- /dev/null +++ b/develop/doc_cn/_sources/design/prune.md.txt @@ -0,0 +1,63 @@ +# Prune + +## Motivation + +We want to support running inference, training and checkpointing in one `ProgramDesc`. We implement +`void Prune(const ProgramDesc* input, ProgramDesc* output)` function, which takes a `ProgramDesc` +and generate a pruned `ProgramDesc`. + +## Challenge + +Pruning need to support both variables and operators being evaluation targets. Consider the following +different situations. + +```python +# Case 1: run foward pass. +cost_np = session.run(target=cost) +# Case 2: run backward passing. +opts_np, _ = session.run(target=[cost, opt]) +# Case 3: run checkpointing +_ = session.run(target=checkpoint) +``` + +## Solution + +To support evaluation of operators, we add `is_target` field in the `OpDesc`. + +```c++ +message OpDesc { + required string type = 3; + repeated Var inputs = 1; + repeated Var outputs = 2; + repeated Attr attrs = 4; + optional bool is_target = 5 [ default = false ]; +}; +``` + +To support evaluation of variables, we add [fetch_op](https://github.com/PaddlePaddle/Paddle/pull/4599). +For each variable in the `target`, we insert a `fetch_op` into the `ProgramDesc` with `variable` being +`fetch_op`'s input. Then we also set `fetch_op` is a target. + +### Algorithm + +If an operator needs to be run, it must fall into one of the following cases: + +1. It is the target. +2. It is depended by some other ops, meaning its output is some other op's input. + +The first case can be checked by `op_desc.is_traget()` . The second case can be implement as + +```c++ +bool HasDependentVar(const OpDesc& op_desc, const std::set& dependent_vars) { + for (auto& var : op_desc.outputs()) { + for (auto& argu : var.arguments()) { + if (dependent_vars.count(argu) != 0) { + return true; + } + } + } + return false; +} +``` + +Then the whole algorithm can be implemented as the following [code](https://github.com/tonyyang-svail/Paddle/blob/prune_impl/paddle/framework/prune.cc). diff --git a/develop/doc_cn/_sources/design/refactorization.md.txt b/develop/doc_cn/_sources/design/refactorization.md.txt index ec51aa1a0ec..f93d6155e17 100644 --- a/develop/doc_cn/_sources/design/refactorization.md.txt +++ b/develop/doc_cn/_sources/design/refactorization.md.txt @@ -177,9 +177,6 @@ REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class) REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) ``` -### USE Macros -Make sure the registration process is executed and linked. - --- # Registration Process 1. Write an Op class and its gradient Op class, if required. @@ -188,8 +185,6 @@ Make sure the registration process is executed and linked. 1. Call maker class to complete `proto` and `checker` 2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap` -4. Invoke the `USE` macro in which the Op is used to make sure that it is linked. - --- # Backward Module (1/2) ### Create Backward Operator diff --git a/develop/doc_cn/design/prune.html b/develop/doc_cn/design/prune.html new file mode 100644 index 00000000000..d3b1d76fe42 --- /dev/null +++ b/develop/doc_cn/design/prune.html @@ -0,0 +1,312 @@ + + + + + + + + + + + Prune — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + Fork me on Github +
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    Motivation

    +

    We want to support running inference, training and checkpointing in one ProgramDesc. We implement +void Prune(const ProgramDesc* input, ProgramDesc* output) function, which takes a ProgramDesc +and generate a pruned ProgramDesc.

    +
    +
    +

    Challenge

    +

    Pruning need to support both variables and operators being evaluation targets. Consider the following +different situations.

    +
    # Case 1: run foward pass.
+cost_np = session.run(target=cost)
+# Case 2: run backward passing.
+opts_np, _ = session.run(target=[cost, opt])
+# Case 3: run checkpointing
+_ = session.run(target=checkpoint)
+
    +
    +
    +
    +

    Solution

    +

    To support evaluation of operators, we add is_target field in the OpDesc.

    +
    message OpDesc {
+  required string type = 3;
+  repeated Var inputs = 1;
+  repeated Var outputs = 2;
+  repeated Attr attrs = 4;
+  optional bool is_target = 5 [ default = false ];
+};
+
    +
    +

    To support evaluation of variables, we add fetch_op. +For each variable in the target, we insert a fetch_op into the ProgramDesc with variable being +fetch_op‘s input. Then we also set fetch_op is a target.

    +
    +

    Algorithm

    +

    If an operator needs to be run, it must fall into one of the following cases:

    +
      +
    1. It is the target.
      2. +
    2. It is depended by some other ops, meaning its output is some other op’s input.
      3. +
    +

    The first case can be checked by op_desc.is_traget() . The second case can be implement as

    +
    bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {
+  for (auto& var : op_desc.outputs()) {
+    for (auto& argu : var.arguments()) {
+      if (dependent_vars.count(argu) != 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
    +
    +

    Then the whole algorithm can be implemented as the following code.

    +
    +
    +
    + + +
    +
    +
    + + +
    + +
    +

    + © Copyright 2016, PaddlePaddle developers. + +

    +
    + Built with Sphinx using a theme provided by Read the Docs. + +
    + +
    +
    + +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc_cn/design/refactorization.html b/develop/doc_cn/design/refactorization.html index a8e61b16df6..97ff94dd243 100644 --- a/develop/doc_cn/design/refactorization.html +++ b/develop/doc_cn/design/refactorization.html @@ -407,10 +407,6 @@ -
    -

    USE Macros

    -

    Make sure the registration process is executed and linked.

    -
    @@ -423,7 +419,6 @@
  • Using the completed proto and checker, it will add a new key-value pair to the OpInfoMap
    • -
  • Invoke the USE macro in which the Op is used to make sure that it is linked.
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