From 97cddf7d97cb7bc2f1c99f71b35112be29ec3583 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Sun, 17 Sep 2017 06:13:30 +0000 Subject: [PATCH] Deploy to GitHub Pages: 59c48f9831e4a189ea042cd1ebb9be39e98b6d03 --- develop/doc/_sources/design/block.md.txt | 338 ++++++++++++ develop/doc/design/block.html | 546 +++++++++++++++++++ develop/doc/objects.inv | Bin 2566 -> 2584 bytes develop/doc/searchindex.js | 2 +- develop/doc_cn/_sources/design/block.md.txt | 338 ++++++++++++ develop/doc_cn/design/block.html | 554 ++++++++++++++++++++ develop/doc_cn/objects.inv | Bin 3101 -> 3119 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 1778 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/block.md.txt create mode 100644 develop/doc/design/block.html create mode 100644 develop/doc_cn/_sources/design/block.md.txt create mode 100644 develop/doc_cn/design/block.html diff --git a/develop/doc/_sources/design/block.md.txt b/develop/doc/_sources/design/block.md.txt new file mode 100644 index 00000000000..be880012203 --- /dev/null +++ b/develop/doc/_sources/design/block.md.txt @@ -0,0 +1,338 @@ +# Design Doc: Block and Scope + +## The Representation of Computation + +Both deep learning systems and programming languages help users describe computation procedures. These systems use various representations of computation: + +- Caffe, Torch, and Paddle: sequences of layers. +- TensorFlow, Caffe2, Mxnet: graphs of operators. +- PaddlePaddle: nested blocks, like C++ and Java programs. + +## Block in Programming Languages and Deep Learning + +In programming languages, a block is a pair of curly braces that includes local variables definitions and a sequence of instructions, or operators. + +Blocks work with control flow structures like `if`, `else`, and `for`, which have equivalents in deep learning: + +| programming languages | PaddlePaddle | +|-----------------------|-----------------------| +| for, while loop | RNN, WhileOp | +| if, if-else, switch | IfElseOp, SwitchOp | +| sequential execution | a sequence of layers | + +A key difference is that a C++ program describes a one pass computation, whereas a deep learning program describes both the forward and backward passes. + +## Stack Frames and the Scope Hierarchy + +The existence of the backward makes the execution of a block of traditional programs and PaddlePaddle different to each other: + +| programming languages | PaddlePaddle | +|-----------------------|-------------------------------| +| stack | scope hierarchy | +| stack frame | scope | +| push at entering block| push at entering block | +| pop at leaving block | destroy at minibatch completes| + +1. In traditional programs: + + - When the execution enters the left curly brace of a block, the runtime pushes a frame into the stack, where it realizes local variables. + - After the execution leaves the right curly brace, the runtime pops the frame. + - The maximum number of frames in the stack is the maximum depth of nested blocks. + +1. In PaddlePaddle + + - When the execution enters a block, PaddlePaddle adds a new scope, where it realizes variables. + - PaddlePaddle doesn't pop a scope after the execution of the block because variables therein are to be used by the backward pass. So it has a stack forest known as a *scope hierarchy*. + - The height of the highest tree is the maximum depth of nested blocks. + - After the process of a minibatch, PaddlePaddle destroys the scope hierarchy. + +## Use Blocks in C++ and PaddlePaddle Programs + +Let us consolidate the discussion by presenting some examples. + +### Blocks with `if-else` and `IfElseOp` + +The following C++ programs shows how blocks are used with the `if-else` structure: + +```c++ +int x = 10; +int y = 20; +int out; +bool cond = false; +if (cond) { + int z = x + y; + out = softmax(z); +} else { + int z = fc(x); + out = z; +} +``` + +An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator](./if_else_op.md) is as follows: + +```python +import paddle as pd + +x = var(10) +y = var(20) +cond = var(false) +ie = pd.create_ifelseop(inputs=[x], output_num=1) +with ie.true_block(): + x = ie.inputs(true, 0) + z = operator.add(x, y) + ie.set_output(true, 0, operator.softmax(z)) +with ie.false_block(): + x = ie.inputs(false, 0) + z = layer.fc(x) + ie.set_output(true, 0, operator.softmax(z)) +out = b(cond) +``` + +In both examples, the left branch computes `softmax(x+y)` and the right branch computes `fc(x)`. + +A difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. The `ie.input(true, 0)` invocation returns instances in the 0-th input, `x`, that corresponds to true values in `cond` as the local variable `x`, where `ie.input(false, 0)` returns instances corresponding to false values. + +### Blocks with `for` and `RNNOp` + +The following RNN model from the [RNN design doc](./rnn.md) + +```python +x = sequence([10, 20, 30]) +m = var(0) +W = tensor() +U = tensor() + +rnn = create_rnn(inputs=[input]) +with rnn.stepnet() as net: + x = net.set_inputs(0) + h = net.add_memory(init=m) + fc_out = pd.matmul(W, x) + hidden_out = pd.matmul(U, h.pre(n=1)) + sum = pd.add_two(fc_out, hidden_out) + act = pd.sigmoid(sum) + h.update(act) # update memory with act + net.set_outputs(0, act, hidden_out) # two outputs + +o1, o2 = rnn() +print o1, o2 +``` + +has its equivalent C++ program as follows + +```c++ +int* x = {10, 20, 30}; +int m = 0; +int W = some_value(); +int U = some_other_value(); + +int mem[sizeof(x) / sizeof(x[0]) + 1]; +int o1[sizeof(x) / sizeof(x[0]) + 1]; +int o2[sizeof(x) / sizeof(x[0]) + 1]; +for (int i = 1; i <= sizeof(x)/sizeof(x[0]); ++i) { + int x = x[i-1]; + if (i == 1) mem[0] = m; + int fc_out = W * x; + int hidden_out = Y * mem[i-1]; + int sum = fc_out + hidden_out; + int act = sigmoid(sum); + mem[i] = act; + o1[i] = act; + o2[i] = hidden_out; +} + +print_array(o1); +print_array(o2); +``` + + +## Compilation and Execution + +Like TensorFlow programs, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest part executes the message for training or inference. + +The generation of this protobuf message is like what a compiler generates a binary executable file. The execution of the message that the OS executes the binary file. + +## The "Binary Executable File Format" + +The definition of the protobuf message is as follows: + +```protobuf +message BlockDesc { + repeated VarDesc vars = 1; + repeated OpDesc ops = 2; +} +``` + +The step net in above RNN example would look like + +``` +BlockDesc { + vars = { + VarDesc {...} // x + VarDesc {...} // h + VarDesc {...} // fc_out + VarDesc {...} // hidden_out + VarDesc {...} // sum + VarDesc {...} // act + } + ops = { + OpDesc {...} // matmul + OpDesc {...} // add_two + OpDesc {...} // sigmoid + } +}; +``` + +Also, the RNN operator in above example is serialized into a protobuf message of type `OpDesc` and would look like: + +``` +OpDesc { + inputs = {0} // the index of x + outputs = {5, 3} // indices of act and hidden_out + attrs { + "memories" : {1} // the index of h + "step_net" : + } +}; +``` + +This `OpDesc` value is in the `ops` field of the `BlockDesc` value representing the global block. + + +## The Compilation of Blocks + +During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator). + +VarDesc in a block should have its name scope to avoid local variables affect parent block's name scope. +Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that stored in parent block. For example + +```python +a = pd.Varaible(shape=[20, 20]) +b = pd.fc(a, params=["fc.w", "fc.b"]) + +rnn = pd.create_rnn() +with rnn.stepnet() as net: + x = net.set_inputs(a) + # reuse fc's parameter + fc_without_b = pd.get_variable("fc.w") + net.set_outputs(fc_without_b) + +out = rnn() +``` +the method `pd.get_variable` can help retrieve a Variable by a name, a Variable may store in a parent block, but might be retrieved in a child block, so block should have a variable scope that supports inheritance. + +In compiler design, the symbol table is a data structure created and maintained by compilers to store information about the occurrence of various entities such as variable names, function names, classes, etc. + +To store the definition of variables and operators, we define a C++ class `SymbolTable`, like the one used in compilers. + +`SymbolTable` can do the following stuff: + +- store the definitions (some names and attributes) of variables and operators, +- to verify if a variable was declared, +- to make it possible to implement type checking (offer Protobuf message pointers to `InferShape` handlers). + + +```c++ +// Information in SymbolTable is enough to trace the dependency graph. So maybe +// the Eval() interface takes a SymbolTable is enough. +class SymbolTable { + public: + SymbolTable(SymbolTable* parent) : parent_(parent) {} + + OpDesc* NewOp(const string& name=""); + + // TODO determine whether name is generated by python or C++ + // currently assume that a unique name will be generated by C++ if the + // argument name left default. + VarDesc* NewVar(const string& name=""); + + // find a VarDesc by name, if recursive true, find parent's SymbolTable + // recursively. + // this interface is introduced to support InferShape, find protobuf messages + // of variables and operators, pass pointers into InferShape. + // operator + // + // NOTE maybe some C++ classes such as VarDescBuilder and OpDescBuilder should + // be proposed and embedded into pybind to enable python operate on C++ pointers. + VarDesc* FindVar(const string& name, bool recursive=true); + + OpDesc* FindOp(const string& name); + + BlockDesc Compile() const; + + private: + SymbolTable* parent_; + + map ops_; + map vars_; +}; +``` + +After all the description of variables and operators is added into SymbolTable, +the block has enough information to run. + +The `Block` class takes a `BlockDesc` as input, and provide `Run` and `InferShape` functions. + + +```c++ +namespace { + +class Block : OperatorBase { +public: + Block(const BlockDesc& desc) desc_(desc) {} + + void InferShape(const framework::Scope& scope) const override { + if (!symbols_ready_) { + CreateVariables(scope); + CreateOperators(); + } + // should run InferShape first. + for (auto& op : runtime_table_.ops()) { + op->InferShape(scope); + } + } + + void Run(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const override { + PADDLE_ENFORCE(symbols_ready_, "operators and variables should be created first."); + for (auto& op : runtime_table_.ops()) { + op->Run(scope, dev_ctx); + } + } + + void CreateVariables(const framework::Scope& scope); + void CreateOperators(); + + // some other necessary interfaces of NetOp are list below + // ... + +private: + BlockDesc desc_; + bool symbols_ready_{false}; +}; +``` + +## The Execution of Blocks + +Block inherits from OperatorBase, which has a Run method. +Block's Run method will run its operators sequentially. + +There is another important interface called `Eval`, which take some arguments called targets, and generate a minimal graph which takes targets as the end points and creates a new Block, +after `Run`, `Eval` will get the latest value and return the targets. + +The definition of Eval is as follows: + +```c++ +// clean a block description by targets using the corresponding dependency graph. +// return a new BlockDesc with minimal number of operators. +// NOTE not return a Block but the block's description so that this can be distributed +// to a cluster. +BlockDesc Prune(const BlockDesc& desc, vector targets); + +void Block::Eval(const vector& targets, + const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) { + BlockDesc min_desc = Prune(desc_, targets); + Block min_block(min_desc); + min_block.Run(scope, dev_ctx); +} +``` diff --git a/develop/doc/design/block.html b/develop/doc/design/block.html new file mode 100644 index 00000000000..8a6a4480f3f --- /dev/null +++ b/develop/doc/design/block.html @@ -0,0 +1,546 @@ + + + + + + + + + + + Design Doc: Block and Scope — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
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+ + + + +
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+ + + +
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+ + + + +
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  • Design Doc: Block and Scope
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Block and Scope

+
+

The Representation of Computation

+

Both deep learning systems and programming languages help users describe computation procedures. These systems use various representations of computation:

+
    +
  • Caffe, Torch, and Paddle: sequences of layers.
    • +
  • TensorFlow, Caffe2, Mxnet: graphs of operators.
    • +
  • PaddlePaddle: nested blocks, like C++ and Java programs.
    • +
+
+
+

Block in Programming Languages and Deep Learning

+

In programming languages, a block is a pair of curly braces that includes local variables definitions and a sequence of instructions, or operators.

+

Blocks work with control flow structures like if, else, and for, which have equivalents in deep learning:

+

| programming languages | PaddlePaddle | +|———————–|———————–| +| for, while loop | RNN, WhileOp | +| if, if-else, switch | IfElseOp, SwitchOp | +| sequential execution | a sequence of layers |

+

A key difference is that a C++ program describes a one pass computation, whereas a deep learning program describes both the forward and backward passes.

+
+
+

Stack Frames and the Scope Hierarchy

+

The existence of the backward makes the execution of a block of traditional programs and PaddlePaddle different to each other:

+

| programming languages | PaddlePaddle | +|———————–|——————————-| +| stack | scope hierarchy | +| stack frame | scope | +| push at entering block| push at entering block | +| pop at leaving block | destroy at minibatch completes|

+
    +
  1. In traditional programs:
      +
    • When the execution enters the left curly brace of a block, the runtime pushes a frame into the stack, where it realizes local variables.
      • +
    • After the execution leaves the right curly brace, the runtime pops the frame.
      • +
    • The maximum number of frames in the stack is the maximum depth of nested blocks.
      • +
    +
    2. +
  2. In PaddlePaddle
      +
    • When the execution enters a block, PaddlePaddle adds a new scope, where it realizes variables.
      • +
    • PaddlePaddle doesn’t pop a scope after the execution of the block because variables therein are to be used by the backward pass. So it has a stack forest known as a scope hierarchy.
      • +
    • The height of the highest tree is the maximum depth of nested blocks.
      • +
    • After the process of a minibatch, PaddlePaddle destroys the scope hierarchy.
      • +
    +
    3. +
+
+
+

Use Blocks in C++ and PaddlePaddle Programs

+

Let us consolidate the discussion by presenting some examples.

+
+

Blocks with if-else and IfElseOp

+

The following C++ programs shows how blocks are used with the if-else structure:

+
int x = 10;
+int y = 20;
+int out;
+bool cond = false;
+if (cond) {
+  int z = x + y;
+  out = softmax(z);
+} else {
+  int z = fc(x);
+  out = z;
+}
+
+
+

An equivalent PaddlePaddle program from the design doc of the IfElseOp operator is as follows:

+
import paddle as pd
+
+x = var(10)
+y = var(20)
+cond = var(false)
+ie = pd.create_ifelseop(inputs=[x], output_num=1)
+with ie.true_block():
+    x = ie.inputs(true, 0)
+    z = operator.add(x, y)
+    ie.set_output(true, 0, operator.softmax(z))
+with ie.false_block():
+    x = ie.inputs(false, 0)
+    z = layer.fc(x)
+    ie.set_output(true, 0, operator.softmax(z))
+out = b(cond)
+
+
+

In both examples, the left branch computes softmax(x+y) and the right branch computes fc(x).

+

A difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. The ie.input(true, 0) invocation returns instances in the 0-th input, x, that corresponds to true values in cond as the local variable x, where ie.input(false, 0) returns instances corresponding to false values.

+
+
+

Blocks with for and RNNOp

+

The following RNN model from the RNN design doc

+
x = sequence([10, 20, 30])
+m = var(0)
+W = tensor()
+U = tensor()
+
+rnn = create_rnn(inputs=[input])
+with rnn.stepnet() as net:
+  x = net.set_inputs(0)
+  h = net.add_memory(init=m)
+  fc_out = pd.matmul(W, x)
+  hidden_out = pd.matmul(U, h.pre(n=1))
+  sum = pd.add_two(fc_out, hidden_out)
+  act = pd.sigmoid(sum)
+  h.update(act)                       # update memory with act
+  net.set_outputs(0, act, hidden_out) # two outputs
+
+o1, o2 = rnn()
+print o1, o2
+
+
+

has its equivalent C++ program as follows

+
int* x = {10, 20, 30};
+int m = 0;
+int W = some_value();
+int U = some_other_value();
+
+int mem[sizeof(x) / sizeof(x[0]) + 1];
+int o1[sizeof(x) / sizeof(x[0]) + 1];
+int o2[sizeof(x) / sizeof(x[0]) + 1];
+for (int i = 1; i <= sizeof(x)/sizeof(x[0]); ++i) {
+  int x = x[i-1];
+  if (i == 1) mem[0] = m;
+  int fc_out = W * x;
+  int hidden_out = Y * mem[i-1];
+  int sum = fc_out + hidden_out;
+  int act = sigmoid(sum);
+  mem[i] = act;
+  o1[i] = act;
+  o2[i] = hidden_out;
+}
+
+print_array(o1);
+print_array(o2);
+
+
+
+
+
+

Compilation and Execution

+

Like TensorFlow programs, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest part executes the message for training or inference.

+

The generation of this protobuf message is like what a compiler generates a binary executable file. The execution of the message that the OS executes the binary file.

+
+
+

The “Binary Executable File Format”

+

The definition of the protobuf message is as follows:

+
message BlockDesc {
+  repeated VarDesc vars = 1;
+  repeated OpDesc ops = 2;
+}
+
+
+

The step net in above RNN example would look like

+
BlockDesc {
+  vars = {
+    VarDesc {...} // x
+    VarDesc {...} // h
+    VarDesc {...} // fc_out
+    VarDesc {...} // hidden_out
+    VarDesc {...} // sum
+    VarDesc {...} // act
+  }
+  ops = {
+    OpDesc {...} // matmul
+    OpDesc {...} // add_two
+    OpDesc {...} // sigmoid
+  }
+};
+
+
+

Also, the RNN operator in above example is serialized into a protobuf message of type OpDesc and would look like:

+
OpDesc {
+  inputs = {0} // the index of x
+  outputs = {5, 3} // indices of act and hidden_out
+  attrs {
+    "memories" : {1} // the index of h
+    "step_net" : <above step net>
+  }
+};
+
+
+

This OpDesc value is in the ops field of the BlockDesc value representing the global block.

+
+
+

The Compilation of Blocks

+

During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator).

+

VarDesc in a block should have its name scope to avoid local variables affect parent block’s name scope. +Child block’s name scopes should inherit the parent’s so that OpDesc in child block can reference a VarDesc that stored in parent block. For example

+
a = pd.Varaible(shape=[20, 20])
+b = pd.fc(a, params=["fc.w", "fc.b"])
+
+rnn = pd.create_rnn()
+with rnn.stepnet() as net:
+    x = net.set_inputs(a)
+    # reuse fc's parameter
+    fc_without_b = pd.get_variable("fc.w")
+    net.set_outputs(fc_without_b)
+
+out = rnn()
+
+
+

the method pd.get_variable can help retrieve a Variable by a name, a Variable may store in a parent block, but might be retrieved in a child block, so block should have a variable scope that supports inheritance.

+

In compiler design, the symbol table is a data structure created and maintained by compilers to store information about the occurrence of various entities such as variable names, function names, classes, etc.

+

To store the definition of variables and operators, we define a C++ class SymbolTable, like the one used in compilers.

+

SymbolTable can do the following stuff:

+
    +
  • store the definitions (some names and attributes) of variables and operators,
    • +
  • to verify if a variable was declared,
    • +
  • to make it possible to implement type checking (offer Protobuf message pointers to InferShape handlers).
    • +
+
// Information in SymbolTable is enough to trace the dependency graph. So maybe
+// the Eval() interface takes a SymbolTable is enough.
+class SymbolTable {
+ public:
+  SymbolTable(SymbolTable* parent) : parent_(parent) {}
+
+  OpDesc* NewOp(const string& name="");
+
+  // TODO determine whether name is generated by python or C++
+  // currently assume that a unique name will be generated by C++ if the
+  // argument name left default.
+  VarDesc* NewVar(const string& name="");
+
+  // find a VarDesc by name, if recursive true, find parent's SymbolTable
+  // recursively.
+  // this interface is introduced to support InferShape, find protobuf messages
+  // of variables and operators, pass pointers into InferShape.
+  // operator
+  //
+  // NOTE maybe some C++ classes such as VarDescBuilder and OpDescBuilder should
+  // be proposed and embedded into pybind to enable python operate on C++ pointers.
+  VarDesc* FindVar(const string& name, bool recursive=true);
+
+  OpDesc* FindOp(const string& name);
+
+  BlockDesc Compile() const;
+
+ private:
+  SymbolTable* parent_;
+
+  map<string, OpDesc> ops_;
+  map<string, VarDesc> vars_;
+};
+
+
+

After all the description of variables and operators is added into SymbolTable, +the block has enough information to run.

+

The Block class takes a BlockDesc as input, and provide Run and InferShape functions.

+
namespace {
+
+class Block : OperatorBase {
+public:
+  Block(const BlockDesc& desc) desc_(desc) {}
+
+  void InferShape(const framework::Scope& scope) const override {
+    if (!symbols_ready_) {
+      CreateVariables(scope);
+      CreateOperators();
+    }
+    // should run InferShape first.
+    for (auto& op : runtime_table_.ops()) {
+      op->InferShape(scope);
+    }
+  }
+
+  void Run(const framework::Scope& scope,
+           const platform::DeviceContext& dev_ctx) const override {
+    PADDLE_ENFORCE(symbols_ready_, "operators and variables should be created first.");
+    for (auto& op : runtime_table_.ops()) {
+      op->Run(scope, dev_ctx);
+    }
+  }
+
+  void CreateVariables(const framework::Scope& scope);
+  void CreateOperators();
+
+  // some other necessary interfaces of NetOp are list below
+  // ...
+
+private:
+  BlockDesc desc_;
+  bool symbols_ready_{false};
+};
+
+
+
+
+

The Execution of Blocks

+

Block inherits from OperatorBase, which has a Run method. +Block’s Run method will run its operators sequentially.

+

There is another important interface called Eval, which take some arguments called targets, and generate a minimal graph which takes targets as the end points and creates a new Block, +after Run, Eval will get the latest value and return the targets.

+

The definition of Eval is as follows:

+
// clean a block description by targets using the corresponding dependency graph.
+// return a new BlockDesc with minimal number of operators.
+// NOTE not return a Block but the block's description so that this can be distributed
+// to a cluster.
+BlockDesc Prune(const BlockDesc& desc, vector<string> targets);
+
+void Block::Eval(const vector<string>& targets,
+                 const framework::Scope& scope,
+                 const platform::DeviceContext& dev_ctx) {
+  BlockDesc min_desc = Prune(desc_, targets);
+  Block min_block(min_desc);
+  min_block.Run(scope, dev_ctx);
+}
+
+
+
+
+ + +
+
+
+ + +
+ +
+

+ © 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/block.md.txt b/develop/doc_cn/_sources/design/block.md.txt new file mode 100644 index 00000000000..be880012203 --- /dev/null +++ b/develop/doc_cn/_sources/design/block.md.txt @@ -0,0 +1,338 @@ +# Design Doc: Block and Scope + +## The Representation of Computation + +Both deep learning systems and programming languages help users describe computation procedures. These systems use various representations of computation: + +- Caffe, Torch, and Paddle: sequences of layers. +- TensorFlow, Caffe2, Mxnet: graphs of operators. +- PaddlePaddle: nested blocks, like C++ and Java programs. + +## Block in Programming Languages and Deep Learning + +In programming languages, a block is a pair of curly braces that includes local variables definitions and a sequence of instructions, or operators. + +Blocks work with control flow structures like `if`, `else`, and `for`, which have equivalents in deep learning: + +| programming languages | PaddlePaddle | +|-----------------------|-----------------------| +| for, while loop | RNN, WhileOp | +| if, if-else, switch | IfElseOp, SwitchOp | +| sequential execution | a sequence of layers | + +A key difference is that a C++ program describes a one pass computation, whereas a deep learning program describes both the forward and backward passes. + +## Stack Frames and the Scope Hierarchy + +The existence of the backward makes the execution of a block of traditional programs and PaddlePaddle different to each other: + +| programming languages | PaddlePaddle | +|-----------------------|-------------------------------| +| stack | scope hierarchy | +| stack frame | scope | +| push at entering block| push at entering block | +| pop at leaving block | destroy at minibatch completes| + +1. In traditional programs: + + - When the execution enters the left curly brace of a block, the runtime pushes a frame into the stack, where it realizes local variables. + - After the execution leaves the right curly brace, the runtime pops the frame. + - The maximum number of frames in the stack is the maximum depth of nested blocks. + +1. In PaddlePaddle + + - When the execution enters a block, PaddlePaddle adds a new scope, where it realizes variables. + - PaddlePaddle doesn't pop a scope after the execution of the block because variables therein are to be used by the backward pass. So it has a stack forest known as a *scope hierarchy*. + - The height of the highest tree is the maximum depth of nested blocks. + - After the process of a minibatch, PaddlePaddle destroys the scope hierarchy. + +## Use Blocks in C++ and PaddlePaddle Programs + +Let us consolidate the discussion by presenting some examples. + +### Blocks with `if-else` and `IfElseOp` + +The following C++ programs shows how blocks are used with the `if-else` structure: + +```c++ +int x = 10; +int y = 20; +int out; +bool cond = false; +if (cond) { + int z = x + y; + out = softmax(z); +} else { + int z = fc(x); + out = z; +} +``` + +An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator](./if_else_op.md) is as follows: + +```python +import paddle as pd + +x = var(10) +y = var(20) +cond = var(false) +ie = pd.create_ifelseop(inputs=[x], output_num=1) +with ie.true_block(): + x = ie.inputs(true, 0) + z = operator.add(x, y) + ie.set_output(true, 0, operator.softmax(z)) +with ie.false_block(): + x = ie.inputs(false, 0) + z = layer.fc(x) + ie.set_output(true, 0, operator.softmax(z)) +out = b(cond) +``` + +In both examples, the left branch computes `softmax(x+y)` and the right branch computes `fc(x)`. + +A difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. The `ie.input(true, 0)` invocation returns instances in the 0-th input, `x`, that corresponds to true values in `cond` as the local variable `x`, where `ie.input(false, 0)` returns instances corresponding to false values. + +### Blocks with `for` and `RNNOp` + +The following RNN model from the [RNN design doc](./rnn.md) + +```python +x = sequence([10, 20, 30]) +m = var(0) +W = tensor() +U = tensor() + +rnn = create_rnn(inputs=[input]) +with rnn.stepnet() as net: + x = net.set_inputs(0) + h = net.add_memory(init=m) + fc_out = pd.matmul(W, x) + hidden_out = pd.matmul(U, h.pre(n=1)) + sum = pd.add_two(fc_out, hidden_out) + act = pd.sigmoid(sum) + h.update(act) # update memory with act + net.set_outputs(0, act, hidden_out) # two outputs + +o1, o2 = rnn() +print o1, o2 +``` + +has its equivalent C++ program as follows + +```c++ +int* x = {10, 20, 30}; +int m = 0; +int W = some_value(); +int U = some_other_value(); + +int mem[sizeof(x) / sizeof(x[0]) + 1]; +int o1[sizeof(x) / sizeof(x[0]) + 1]; +int o2[sizeof(x) / sizeof(x[0]) + 1]; +for (int i = 1; i <= sizeof(x)/sizeof(x[0]); ++i) { + int x = x[i-1]; + if (i == 1) mem[0] = m; + int fc_out = W * x; + int hidden_out = Y * mem[i-1]; + int sum = fc_out + hidden_out; + int act = sigmoid(sum); + mem[i] = act; + o1[i] = act; + o2[i] = hidden_out; +} + +print_array(o1); +print_array(o2); +``` + + +## Compilation and Execution + +Like TensorFlow programs, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest part executes the message for training or inference. + +The generation of this protobuf message is like what a compiler generates a binary executable file. The execution of the message that the OS executes the binary file. + +## The "Binary Executable File Format" + +The definition of the protobuf message is as follows: + +```protobuf +message BlockDesc { + repeated VarDesc vars = 1; + repeated OpDesc ops = 2; +} +``` + +The step net in above RNN example would look like + +``` +BlockDesc { + vars = { + VarDesc {...} // x + VarDesc {...} // h + VarDesc {...} // fc_out + VarDesc {...} // hidden_out + VarDesc {...} // sum + VarDesc {...} // act + } + ops = { + OpDesc {...} // matmul + OpDesc {...} // add_two + OpDesc {...} // sigmoid + } +}; +``` + +Also, the RNN operator in above example is serialized into a protobuf message of type `OpDesc` and would look like: + +``` +OpDesc { + inputs = {0} // the index of x + outputs = {5, 3} // indices of act and hidden_out + attrs { + "memories" : {1} // the index of h + "step_net" : + } +}; +``` + +This `OpDesc` value is in the `ops` field of the `BlockDesc` value representing the global block. + + +## The Compilation of Blocks + +During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator). + +VarDesc in a block should have its name scope to avoid local variables affect parent block's name scope. +Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that stored in parent block. For example + +```python +a = pd.Varaible(shape=[20, 20]) +b = pd.fc(a, params=["fc.w", "fc.b"]) + +rnn = pd.create_rnn() +with rnn.stepnet() as net: + x = net.set_inputs(a) + # reuse fc's parameter + fc_without_b = pd.get_variable("fc.w") + net.set_outputs(fc_without_b) + +out = rnn() +``` +the method `pd.get_variable` can help retrieve a Variable by a name, a Variable may store in a parent block, but might be retrieved in a child block, so block should have a variable scope that supports inheritance. + +In compiler design, the symbol table is a data structure created and maintained by compilers to store information about the occurrence of various entities such as variable names, function names, classes, etc. + +To store the definition of variables and operators, we define a C++ class `SymbolTable`, like the one used in compilers. + +`SymbolTable` can do the following stuff: + +- store the definitions (some names and attributes) of variables and operators, +- to verify if a variable was declared, +- to make it possible to implement type checking (offer Protobuf message pointers to `InferShape` handlers). + + +```c++ +// Information in SymbolTable is enough to trace the dependency graph. So maybe +// the Eval() interface takes a SymbolTable is enough. +class SymbolTable { + public: + SymbolTable(SymbolTable* parent) : parent_(parent) {} + + OpDesc* NewOp(const string& name=""); + + // TODO determine whether name is generated by python or C++ + // currently assume that a unique name will be generated by C++ if the + // argument name left default. + VarDesc* NewVar(const string& name=""); + + // find a VarDesc by name, if recursive true, find parent's SymbolTable + // recursively. + // this interface is introduced to support InferShape, find protobuf messages + // of variables and operators, pass pointers into InferShape. + // operator + // + // NOTE maybe some C++ classes such as VarDescBuilder and OpDescBuilder should + // be proposed and embedded into pybind to enable python operate on C++ pointers. + VarDesc* FindVar(const string& name, bool recursive=true); + + OpDesc* FindOp(const string& name); + + BlockDesc Compile() const; + + private: + SymbolTable* parent_; + + map ops_; + map vars_; +}; +``` + +After all the description of variables and operators is added into SymbolTable, +the block has enough information to run. + +The `Block` class takes a `BlockDesc` as input, and provide `Run` and `InferShape` functions. + + +```c++ +namespace { + +class Block : OperatorBase { +public: + Block(const BlockDesc& desc) desc_(desc) {} + + void InferShape(const framework::Scope& scope) const override { + if (!symbols_ready_) { + CreateVariables(scope); + CreateOperators(); + } + // should run InferShape first. + for (auto& op : runtime_table_.ops()) { + op->InferShape(scope); + } + } + + void Run(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const override { + PADDLE_ENFORCE(symbols_ready_, "operators and variables should be created first."); + for (auto& op : runtime_table_.ops()) { + op->Run(scope, dev_ctx); + } + } + + void CreateVariables(const framework::Scope& scope); + void CreateOperators(); + + // some other necessary interfaces of NetOp are list below + // ... + +private: + BlockDesc desc_; + bool symbols_ready_{false}; +}; +``` + +## The Execution of Blocks + +Block inherits from OperatorBase, which has a Run method. +Block's Run method will run its operators sequentially. + +There is another important interface called `Eval`, which take some arguments called targets, and generate a minimal graph which takes targets as the end points and creates a new Block, +after `Run`, `Eval` will get the latest value and return the targets. + +The definition of Eval is as follows: + +```c++ +// clean a block description by targets using the corresponding dependency graph. +// return a new BlockDesc with minimal number of operators. +// NOTE not return a Block but the block's description so that this can be distributed +// to a cluster. +BlockDesc Prune(const BlockDesc& desc, vector targets); + +void Block::Eval(const vector& targets, + const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) { + BlockDesc min_desc = Prune(desc_, targets); + Block min_block(min_desc); + min_block.Run(scope, dev_ctx); +} +``` diff --git a/develop/doc_cn/design/block.html b/develop/doc_cn/design/block.html new file mode 100644 index 00000000000..d02471ac45d --- /dev/null +++ b/develop/doc_cn/design/block.html @@ -0,0 +1,554 @@ + + + + + + + + + + + Design Doc: Block and Scope — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ Fork me on Github +
+ + English + + + + +
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    + +
  • Design Doc: Block and Scope
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Block and Scope

+
+

The Representation of Computation

+

Both deep learning systems and programming languages help users describe computation procedures. These systems use various representations of computation:

+
    +
  • Caffe, Torch, and Paddle: sequences of layers.
    • +
  • TensorFlow, Caffe2, Mxnet: graphs of operators.
    • +
  • PaddlePaddle: nested blocks, like C++ and Java programs.
    • +
+
+
+

Block in Programming Languages and Deep Learning

+

In programming languages, a block is a pair of curly braces that includes local variables definitions and a sequence of instructions, or operators.

+

Blocks work with control flow structures like if, else, and for, which have equivalents in deep learning:

+

| programming languages | PaddlePaddle | +|———————–|———————–| +| for, while loop | RNN, WhileOp | +| if, if-else, switch | IfElseOp, SwitchOp | +| sequential execution | a sequence of layers |

+

A key difference is that a C++ program describes a one pass computation, whereas a deep learning program describes both the forward and backward passes.

+
+
+

Stack Frames and the Scope Hierarchy

+

The existence of the backward makes the execution of a block of traditional programs and PaddlePaddle different to each other:

+

| programming languages | PaddlePaddle | +|———————–|——————————-| +| stack | scope hierarchy | +| stack frame | scope | +| push at entering block| push at entering block | +| pop at leaving block | destroy at minibatch completes|

+
    +
  1. In traditional programs:
      +
    • When the execution enters the left curly brace of a block, the runtime pushes a frame into the stack, where it realizes local variables.
      • +
    • After the execution leaves the right curly brace, the runtime pops the frame.
      • +
    • The maximum number of frames in the stack is the maximum depth of nested blocks.
      • +
    +
    2. +
  2. In PaddlePaddle
      +
    • When the execution enters a block, PaddlePaddle adds a new scope, where it realizes variables.
      • +
    • PaddlePaddle doesn’t pop a scope after the execution of the block because variables therein are to be used by the backward pass. So it has a stack forest known as a scope hierarchy.
      • +
    • The height of the highest tree is the maximum depth of nested blocks.
      • +
    • After the process of a minibatch, PaddlePaddle destroys the scope hierarchy.
      • +
    +
    3. +
+
+
+

Use Blocks in C++ and PaddlePaddle Programs

+

Let us consolidate the discussion by presenting some examples.

+
+

Blocks with if-else and IfElseOp

+

The following C++ programs shows how blocks are used with the if-else structure:

+
int x = 10;
+int y = 20;
+int out;
+bool cond = false;
+if (cond) {
+  int z = x + y;
+  out = softmax(z);
+} else {
+  int z = fc(x);
+  out = z;
+}
+
+
+

An equivalent PaddlePaddle program from the design doc of the IfElseOp operator is as follows:

+
import paddle as pd
+
+x = var(10)
+y = var(20)
+cond = var(false)
+ie = pd.create_ifelseop(inputs=[x], output_num=1)
+with ie.true_block():
+    x = ie.inputs(true, 0)
+    z = operator.add(x, y)
+    ie.set_output(true, 0, operator.softmax(z))
+with ie.false_block():
+    x = ie.inputs(false, 0)
+    z = layer.fc(x)
+    ie.set_output(true, 0, operator.softmax(z))
+out = b(cond)
+
+
+

In both examples, the left branch computes softmax(x+y) and the right branch computes fc(x).

+

A difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. The ie.input(true, 0) invocation returns instances in the 0-th input, x, that corresponds to true values in cond as the local variable x, where ie.input(false, 0) returns instances corresponding to false values.

+
+
+

Blocks with for and RNNOp

+

The following RNN model from the RNN design doc

+
x = sequence([10, 20, 30])
+m = var(0)
+W = tensor()
+U = tensor()
+
+rnn = create_rnn(inputs=[input])
+with rnn.stepnet() as net:
+  x = net.set_inputs(0)
+  h = net.add_memory(init=m)
+  fc_out = pd.matmul(W, x)
+  hidden_out = pd.matmul(U, h.pre(n=1))
+  sum = pd.add_two(fc_out, hidden_out)
+  act = pd.sigmoid(sum)
+  h.update(act)                       # update memory with act
+  net.set_outputs(0, act, hidden_out) # two outputs
+
+o1, o2 = rnn()
+print o1, o2
+
+
+

has its equivalent C++ program as follows

+
int* x = {10, 20, 30};
+int m = 0;
+int W = some_value();
+int U = some_other_value();
+
+int mem[sizeof(x) / sizeof(x[0]) + 1];
+int o1[sizeof(x) / sizeof(x[0]) + 1];
+int o2[sizeof(x) / sizeof(x[0]) + 1];
+for (int i = 1; i <= sizeof(x)/sizeof(x[0]); ++i) {
+  int x = x[i-1];
+  if (i == 1) mem[0] = m;
+  int fc_out = W * x;
+  int hidden_out = Y * mem[i-1];
+  int sum = fc_out + hidden_out;
+  int act = sigmoid(sum);
+  mem[i] = act;
+  o1[i] = act;
+  o2[i] = hidden_out;
+}
+
+print_array(o1);
+print_array(o2);
+
+
+
+
+
+

Compilation and Execution

+

Like TensorFlow programs, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest part executes the message for training or inference.

+

The generation of this protobuf message is like what a compiler generates a binary executable file. The execution of the message that the OS executes the binary file.

+
+
+

The “Binary Executable File Format”

+

The definition of the protobuf message is as follows:

+
message BlockDesc {
+  repeated VarDesc vars = 1;
+  repeated OpDesc ops = 2;
+}
+
+
+

The step net in above RNN example would look like

+
BlockDesc {
+  vars = {
+    VarDesc {...} // x
+    VarDesc {...} // h
+    VarDesc {...} // fc_out
+    VarDesc {...} // hidden_out
+    VarDesc {...} // sum
+    VarDesc {...} // act
+  }
+  ops = {
+    OpDesc {...} // matmul
+    OpDesc {...} // add_two
+    OpDesc {...} // sigmoid
+  }
+};
+
+
+

Also, the RNN operator in above example is serialized into a protobuf message of type OpDesc and would look like:

+
OpDesc {
+  inputs = {0} // the index of x
+  outputs = {5, 3} // indices of act and hidden_out
+  attrs {
+    "memories" : {1} // the index of h
+    "step_net" : <above step net>
+  }
+};
+
+
+

This OpDesc value is in the ops field of the BlockDesc value representing the global block.

+
+
+

The Compilation of Blocks

+

During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator).

+

VarDesc in a block should have its name scope to avoid local variables affect parent block’s name scope. +Child block’s name scopes should inherit the parent’s so that OpDesc in child block can reference a VarDesc that stored in parent block. For example

+
a = pd.Varaible(shape=[20, 20])
+b = pd.fc(a, params=["fc.w", "fc.b"])
+
+rnn = pd.create_rnn()
+with rnn.stepnet() as net:
+    x = net.set_inputs(a)
+    # reuse fc's parameter
+    fc_without_b = pd.get_variable("fc.w")
+    net.set_outputs(fc_without_b)
+
+out = rnn()
+
+
+

the method pd.get_variable can help retrieve a Variable by a name, a Variable may store in a parent block, but might be retrieved in a child block, so block should have a variable scope that supports inheritance.

+

In compiler design, the symbol table is a data structure created and maintained by compilers to store information about the occurrence of various entities such as variable names, function names, classes, etc.

+

To store the definition of variables and operators, we define a C++ class SymbolTable, like the one used in compilers.

+

SymbolTable can do the following stuff:

+
    +
  • store the definitions (some names and attributes) of variables and operators,
    • +
  • to verify if a variable was declared,
    • +
  • to make it possible to implement type checking (offer Protobuf message pointers to InferShape handlers).
    • +
+
// Information in SymbolTable is enough to trace the dependency graph. So maybe
+// the Eval() interface takes a SymbolTable is enough.
+class SymbolTable {
+ public:
+  SymbolTable(SymbolTable* parent) : parent_(parent) {}
+
+  OpDesc* NewOp(const string& name="");
+
+  // TODO determine whether name is generated by python or C++
+  // currently assume that a unique name will be generated by C++ if the
+  // argument name left default.
+  VarDesc* NewVar(const string& name="");
+
+  // find a VarDesc by name, if recursive true, find parent's SymbolTable
+  // recursively.
+  // this interface is introduced to support InferShape, find protobuf messages
+  // of variables and operators, pass pointers into InferShape.
+  // operator
+  //
+  // NOTE maybe some C++ classes such as VarDescBuilder and OpDescBuilder should
+  // be proposed and embedded into pybind to enable python operate on C++ pointers.
+  VarDesc* FindVar(const string& name, bool recursive=true);
+
+  OpDesc* FindOp(const string& name);
+
+  BlockDesc Compile() const;
+
+ private:
+  SymbolTable* parent_;
+
+  map<string, OpDesc> ops_;
+  map<string, VarDesc> vars_;
+};
+
+
+

After all the description of variables and operators is added into SymbolTable, +the block has enough information to run.

+

The Block class takes a BlockDesc as input, and provide Run and InferShape functions.

+
namespace {
+
+class Block : OperatorBase {
+public:
+  Block(const BlockDesc& desc) desc_(desc) {}
+
+  void InferShape(const framework::Scope& scope) const override {
+    if (!symbols_ready_) {
+      CreateVariables(scope);
+      CreateOperators();
+    }
+    // should run InferShape first.
+    for (auto& op : runtime_table_.ops()) {
+      op->InferShape(scope);
+    }
+  }
+
+  void Run(const framework::Scope& scope,
+           const platform::DeviceContext& dev_ctx) const override {
+    PADDLE_ENFORCE(symbols_ready_, "operators and variables should be created first.");
+    for (auto& op : runtime_table_.ops()) {
+      op->Run(scope, dev_ctx);
+    }
+  }
+
+  void CreateVariables(const framework::Scope& scope);
+  void CreateOperators();
+
+  // some other necessary interfaces of NetOp are list below
+  // ...
+
+private:
+  BlockDesc desc_;
+  bool symbols_ready_{false};
+};
+
+
+
+
+

The Execution of Blocks

+

Block inherits from OperatorBase, which has a Run method. +Block’s Run method will run its operators sequentially.

+

There is another important interface called Eval, which take some arguments called targets, and generate a minimal graph which takes targets as the end points and creates a new Block, +after Run, Eval will get the latest value and return the targets.

+

The definition of Eval is as follows:

+
// clean a block description by targets using the corresponding dependency graph.
+// return a new BlockDesc with minimal number of operators.
+// NOTE not return a Block but the block's description so that this can be distributed
+// to a cluster.
+BlockDesc Prune(const BlockDesc& desc, vector<string> targets);
+
+void Block::Eval(const vector<string>& targets,
+                 const framework::Scope& scope,
+                 const platform::DeviceContext& dev_ctx) {
+  BlockDesc min_desc = Prune(desc_, targets);
+  Block min_block(min_desc);
+  min_block.Run(scope, dev_ctx);
+}
+
+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

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