From 57ab7052c7508a3a42bbcc23c8788b270fd60642 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Fri, 5 Jan 2018 06:58:07 +0000 Subject: [PATCH] Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba --- .../design/concurrent_programming.md.txt | 163 +++++++ .../doc/design/concurrent_programming.html | 429 +++++++++++++++++ develop/doc/design/fluid.html | 2 +- develop/doc/objects.inv | Bin 3931 -> 3960 bytes develop/doc/searchindex.js | 2 +- .../design/concurrent_programming.md.txt | 163 +++++++ .../doc_cn/design/concurrent_programming.html | 443 ++++++++++++++++++ develop/doc_cn/design/fluid.html | 2 +- develop/doc_cn/objects.inv | Bin 4499 -> 4517 bytes develop/doc_cn/searchindex.js | 2 +- 10 files changed, 1202 insertions(+), 4 deletions(-) create mode 100644 develop/doc/_sources/design/concurrent_programming.md.txt create mode 100644 develop/doc/design/concurrent_programming.html create mode 100644 develop/doc_cn/_sources/design/concurrent_programming.md.txt create mode 100644 develop/doc_cn/design/concurrent_programming.html diff --git a/develop/doc/_sources/design/concurrent_programming.md.txt b/develop/doc/_sources/design/concurrent_programming.md.txt new file mode 100644 index 00000000000..afc65e831d5 --- /dev/null +++ b/develop/doc/_sources/design/concurrent_programming.md.txt @@ -0,0 +1,163 @@ +# Design Doc: Concurrent Programming with Fluid + +With PaddlePaddle Fluid, users describe a program other than a model. The program is a [`ProgramDesc`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto) protobuf message. TensorFlow/MxNet/Caffe2 applications generate protobuf messages too, but their protobuf messages represent the model, a graph of operators, but not the program that trains/uses the model. + +Many know that when we program TensorFlow, we can specify the device on which each operator runs. This allows us to create a concurrent/parallel AI application. An interesting questions is **how does a `ProgramDesc` represents a concurrent program?** + +The answer relies on the fact that a `ProgramDesc` is similar to an abstract syntax tree (AST) that describes a program. So users just program a concurrent program that they do with any concurrent programming language, e.g., [Go](https://golang.org). + +## An Analogy + +The following table compares concepts in Fluid and Go + +| Go | Fluid | +|----|-------| +|user-defined functions | [layers](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid) | +| control-flow and built-in functions | [intrinsics/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators) | +| goroutines, channels | [class ThreadPool](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h) | +| runtime | [class Executor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h) | + +## An Example Concurrent Program + +To review all above concepts in an example, let us take a simple program and writes its distributed version. + +Suppose that we want to parallelize a naive Fluid program (written in Go and calling Fluid's Go binding) that multiplies two tensors. + +```go +import "fluid" + +func paddlepaddle() { + X = fluid.read(...) + W = fluid.Tensor(...) + Y = fluid.mult(X, W) +} +``` + +Please be aware that the Fluid's Go binding provides the default `main` function, which calls the `paddlepaddle` function, which, in this case, is defined in above program and creates the following `ProgramDesc` message. + +```protobuf +message ProgramDesc { + block[0] = Block { + vars = [X, W, Y], + ops = [ + read(output = X) + assign(input = ..., output = W) + mult(input = {X, W}, output = Y) + ], + } +} +``` + +Then, the default `main` function calls `fluid.run()`, which creates an instance of the [`class Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h) and calls `Executor.Run(block[0])`, where `block[0]` is the first and only block defined in above `ProgramDesc` message. + +The default `main` function is defined as follows: + +```go +func main() { + paddlepaddle() + fluid.run() +} +``` + +## The Concurrent Version + +By parallelizing the above program, we could support very big tensor X by splitting into small pieces {x_1, x_2, ...} and sent each piece to worker process/node for parallel multiplication. + +In this case, we can write a transpiler that takes a `ProgramDesc` message that represents the above example program and outputs two `ProgramDesc` messages, one for running on the master process/node, and the other one for worker processes/nodes. + +### The Master Program + +The master program could look like the following: + +```protobuf +message ProgramDesc { + block[0] = Block { + vars = [X, L, Y], + ops = [ + read(output = X) + kube_get_workers_addrs(output = L) + Y = tensor_array(len(L)) + parallel_for(input = X, output = Y, + attrs = {L, block_id(1)}) # referring to block 1 + ] + } + + block[1] = Block { + parent = 0, + vars = [x, y, index], + ops = [ + slice(input = [X, index], output = x) # index is initialized by parallel_for + send(input = x, attrs = L[index]) + recv(outputs = y, attrs = L[index]) + assign(input = y, output = Y[index]) + ] + } +} +``` + +The equivalent Fluid program (calling the Go binding) is: + +```go +func main() { //// block 0 + X = fluid.read(...) + L = fluid.k8s.get_worker_addrs() + Y = fluid.tensor_array(len(L)) + fluid.parallel_for(X, L, + func(index int) { //// block 1 + x = X[index] + fluid.send(L[index], x) + y = fluid.recv(L[index]) + Y[index] = y + }) +} +``` + +An explanation of the above program: + +- `fluid.k8s` is a package that provides access to Kubernetes API. +- `fluid.k8s.get_worker_addrs` returns the list of IP and ports of all pods of the current job except for the current one (the master pod). +- `fluid.tensor_array` creates a [tensor array](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor_array.h). `fluid.parallel_for` creates a `ParallelFor` intrinsic, which, when executed, + + 1. creates `len(L)` scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named "index" in the scope to an integer value in the range `[0, len(L)-1]`, and + 2. creates `len(L)` threads by calling into the `ThreadPool` singleton, each thread + 1. creates an Executor instance, and + 2. calls `Executor.Run(block)`, where `block` is block 1 as explained above. +1. Please be aware that block 1 is a sub-block of block 0, so ops in block 1 could refer to variables defined in block 0. + +### The Worker Program + +The worker program looks like + +```go +func main() { + W = Tensor(...) + x = fluid.listen_and_do( + fluid.k8s.self_addr(), + func(input Tensor) { + output = fluid.mult(input, W) + }) +} +``` + +where + +- `fluid.listen_and_do` creates a `ListenAndDo` intrinsic, which, when executed, + 1. listens on the current pod's IP address, as returned by `fliud.k8s.self_addr()`, + 2. once a connection is established, + 1. creates a scope of two parameters, "input" and "output", + 2. reads a [Fluid variable](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h) and saves it into "input", + 3. creates an Executor instance and calls `Executor.Run(block)`, where the block is generated by running the lambda specified as the second parameter of `fluid.listen_and_do`. + +## Summarization + +From the above example, we see that: + +1. Fluid enables the imperative programming paradigm by: + 1. letting users describe a program, but not a model (a sequence of layers, or a graph of operators), and + 2. call the `fluid.run` function that runs the program implicitly. +1. The program is described as a `ProgramDesc` protobuf message. +2. Function `Executor.Run` takes a block, instead of a `ProgramDesc`, as its parameter. +3. `fluid.run` calls `Executor.Run` to run the first block in the `ProgramDesc` message. +4. `Executor.Run`'s implementation is extremely simple -- it doesn't plan the execution nor create threads; instead, it runs on the current thread and execute intrinsics/operators' `Run` method sequentially as they appear in the `Block.ops` array. +5. Intrinsics/operators' `Run` method might create threads. For example, the `ListenAndDo` operator creates a thread to handle each incoming request. +6. Threads are not necessarily OS thread; instead, they could be [green threads](https://en.wikipedia.org/wiki/Green_threads) managed by ThreadPool. Multiple green threads might run on the same OS thread. An example green threads is Go's [goroutines](https://tour.golang.org/concurrency/1). diff --git a/develop/doc/design/concurrent_programming.html b/develop/doc/design/concurrent_programming.html new file mode 100644 index 00000000000..a4f165a8619 --- /dev/null +++ b/develop/doc/design/concurrent_programming.html @@ -0,0 +1,429 @@ + + + + + + + + + + + Design Doc: Concurrent Programming with Fluid — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Design Doc: Concurrent Programming with Fluid
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Concurrent Programming with Fluid

+

With PaddlePaddle Fluid, users describe a program other than a model. The program is a ProgramDesc protobuf message. TensorFlow/MxNet/Caffe2 applications generate protobuf messages too, but their protobuf messages represent the model, a graph of operators, but not the program that trains/uses the model.

+

Many know that when we program TensorFlow, we can specify the device on which each operator runs. This allows us to create a concurrent/parallel AI application. An interesting questions is how does a ProgramDesc represents a concurrent program?

+

The answer relies on the fact that a ProgramDesc is similar to an abstract syntax tree (AST) that describes a program. So users just program a concurrent program that they do with any concurrent programming language, e.g., Go.

+
+

An Analogy

+

The following table compares concepts in Fluid and Go

+

| Go | Fluid | +|—-|——-| +|user-defined functions | layers | +| control-flow and built-in functions | intrinsics/operators | +| goroutines, channels | class ThreadPool | +| runtime | class Executor |

+
+
+

An Example Concurrent Program

+

To review all above concepts in an example, let us take a simple program and writes its distributed version.

+

Suppose that we want to parallelize a naive Fluid program (written in Go and calling Fluid’s Go binding) that multiplies two tensors.

+
import "fluid"
+
+func paddlepaddle() {
+  X = fluid.read(...)
+  W = fluid.Tensor(...)
+  Y = fluid.mult(X, W)
+}
+
+
+

Please be aware that the Fluid’s Go binding provides the default main function, which calls the paddlepaddle function, which, in this case, is defined in above program and creates the following ProgramDesc message.

+
message ProgramDesc {
+  block[0] = Block {
+    vars = [X, W, Y],
+    ops = [
+      read(output = X)
+      assign(input = ..., output = W)
+      mult(input = {X, W}, output = Y)
+    ],
+  }
+}
+
+
+

Then, the default main function calls fluid.run(), which creates an instance of the class Executor and calls Executor.Run(block[0]), where block[0] is the first and only block defined in above ProgramDesc message.

+

The default main function is defined as follows:

+
func main() {
+  paddlepaddle()
+  fluid.run()
+}
+
+
+
+
+

The Concurrent Version

+

By parallelizing the above program, we could support very big tensor X by splitting into small pieces {x_1, x_2, ...} and sent each piece to worker process/node for parallel multiplication.

+

In this case, we can write a transpiler that takes a ProgramDesc message that represents the above example program and outputs two ProgramDesc messages, one for running on the master process/node, and the other one for worker processes/nodes.

+
+

The Master Program

+

The master program could look like the following:

+
message ProgramDesc {
+  block[0] = Block {
+    vars = [X, L, Y],
+    ops = [
+      read(output = X)
+      kube_get_workers_addrs(output = L)
+      Y = tensor_array(len(L))
+      parallel_for(input = X, output = Y, 
+                   attrs = {L, block_id(1)}) # referring to block 1
+    ]
+  }
+  
+  block[1] = Block {
+    parent = 0,
+    vars = [x, y, index],
+    ops = [
+      slice(input = [X, index], output = x) # index is initialized by parallel_for
+      send(input = x, attrs = L[index])
+      recv(outputs = y, attrs = L[index])
+      assign(input = y, output = Y[index])
+    ]
+  }
+}
+
+
+

The equivalent Fluid program (calling the Go binding) is:

+
func main() {  //// block 0
+  X = fluid.read(...)
+  L = fluid.k8s.get_worker_addrs()
+  Y = fluid.tensor_array(len(L))
+  fluid.parallel_for(X, L, 
+                     func(index int) {  //// block 1
+                       x = X[index]
+                       fluid.send(L[index], x)
+                       y = fluid.recv(L[index])
+                       Y[index] = y
+                     })
+}
+
+
+

An explanation of the above program:

+
    +
  • fluid.k8s is a package that provides access to Kubernetes API.
    • +
  • fluid.k8s.get_worker_addrs returns the list of IP and ports of all pods of the current job except for the current one (the master pod).
    • +
  • fluid.tensor_array creates a tensor array. fluid.parallel_for creates a ParallelFor intrinsic, which, when executed,
      +
    1. creates len(L) scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named “index” in the scope to an integer value in the range [0, len(L)-1], and
      2. +
    2. creates len(L) threads by calling into the ThreadPool singleton, each thread
        +
      1. creates an Executor instance, and
        2. +
      2. calls Executor.Run(block), where block is block 1 as explained above.
        3. +
      +
      3. +
    +
    • +
+
    +
  1. Please be aware that block 1 is a sub-block of block 0, so ops in block 1 could refer to variables defined in block 0.
    2. +
+
+
+

The Worker Program

+

The worker program looks like

+
func main() {
+  W = Tensor(...)
+  x = fluid.listen_and_do(
+        fluid.k8s.self_addr(),
+        func(input Tensor) {
+          output = fluid.mult(input, W)
+        })
+}
+
+
+

where

+
    +
  • fluid.listen_and_do creates a ListenAndDo intrinsic, which, when executed,
      +
    1. listens on the current pod’s IP address, as returned by fliud.k8s.self_addr(),
      2. +
    2. once a connection is established,
        +
      1. creates a scope of two parameters, “input” and “output”,
        2. +
      2. reads a Fluid variable and saves it into “input”,
        3. +
      3. creates an Executor instance and calls Executor.Run(block), where the block is generated by running the lambda specified as the second parameter of fluid.listen_and_do.
        4. +
      +
      3. +
    +
    • +
+
+
+
+

Summarization

+

From the above example, we see that:

+
    +
  1. Fluid enables the imperative programming paradigm by:
      +
    1. letting users describe a program, but not a model (a sequence of layers, or a graph of operators), and
      2. +
    2. call the fluid.run function that runs the program implicitly.
      3. +
    +
    2. +
  2. The program is described as a ProgramDesc protobuf message.
    3. +
  3. Function Executor.Run takes a block, instead of a ProgramDesc, as its parameter.
    4. +
  4. fluid.run calls Executor.Run to run the first block in the ProgramDesc message.
    5. +
  5. Executor.Run‘s implementation is extremely simple – it doesn’t plan the execution nor create threads; instead, it runs on the current thread and execute intrinsics/operators’ Run method sequentially as they appear in the Block.ops array.
    6. +
  6. Intrinsics/operators’ Run method might create threads. For example, the ListenAndDo operator creates a thread to handle each incoming request.
    7. +
  7. Threads are not necessarily OS thread; instead, they could be green threads managed by ThreadPool. Multiple green threads might run on the same OS thread. An example green threads is Go’s goroutines.
    8. +
+
+
+ + +
+
+
+ + +
+ +
+

+ © 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/fluid.html b/develop/doc/design/fluid.html index f57296595d2..47e2bfd5259 100644 --- a/develop/doc/design/fluid.html +++ b/develop/doc/design/fluid.html @@ -308,7 +308,7 @@

Towards a Deep Learning Language and the Compiler

We can change the if-then-else and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.

-

Even if we do not invent a new language, as long as we get the ProgramDesc message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using nvcc. Another transpiler could generate MKL-friendly code that should be built using icc from Intel. More interestingly, we can translate a Fluid program into its distributed version of two ProgramDesc messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the concurrent programming design document would be a good pointer. The following figure explains the proposed two-stage process:

+

Even if we do not invent a new language, as long as we get the ProgramDesc message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using nvcc. Another transpiler could generate MKL-friendly code that should be built using icc from Intel. More interestingly, we can translate a Fluid program into its distributed version of two ProgramDesc messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the concurrent programming design document would be a good pointer. The following figure explains the proposed two-stage process:

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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/concurrent_programming.md.txt b/develop/doc_cn/_sources/design/concurrent_programming.md.txt new file mode 100644 index 00000000000..afc65e831d5 --- /dev/null +++ b/develop/doc_cn/_sources/design/concurrent_programming.md.txt @@ -0,0 +1,163 @@ +# Design Doc: Concurrent Programming with Fluid + +With PaddlePaddle Fluid, users describe a program other than a model. The program is a [`ProgramDesc`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto) protobuf message. TensorFlow/MxNet/Caffe2 applications generate protobuf messages too, but their protobuf messages represent the model, a graph of operators, but not the program that trains/uses the model. + +Many know that when we program TensorFlow, we can specify the device on which each operator runs. This allows us to create a concurrent/parallel AI application. An interesting questions is **how does a `ProgramDesc` represents a concurrent program?** + +The answer relies on the fact that a `ProgramDesc` is similar to an abstract syntax tree (AST) that describes a program. So users just program a concurrent program that they do with any concurrent programming language, e.g., [Go](https://golang.org). + +## An Analogy + +The following table compares concepts in Fluid and Go + +| Go | Fluid | +|----|-------| +|user-defined functions | [layers](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid) | +| control-flow and built-in functions | [intrinsics/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators) | +| goroutines, channels | [class ThreadPool](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h) | +| runtime | [class Executor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h) | + +## An Example Concurrent Program + +To review all above concepts in an example, let us take a simple program and writes its distributed version. + +Suppose that we want to parallelize a naive Fluid program (written in Go and calling Fluid's Go binding) that multiplies two tensors. + +```go +import "fluid" + +func paddlepaddle() { + X = fluid.read(...) + W = fluid.Tensor(...) + Y = fluid.mult(X, W) +} +``` + +Please be aware that the Fluid's Go binding provides the default `main` function, which calls the `paddlepaddle` function, which, in this case, is defined in above program and creates the following `ProgramDesc` message. + +```protobuf +message ProgramDesc { + block[0] = Block { + vars = [X, W, Y], + ops = [ + read(output = X) + assign(input = ..., output = W) + mult(input = {X, W}, output = Y) + ], + } +} +``` + +Then, the default `main` function calls `fluid.run()`, which creates an instance of the [`class Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h) and calls `Executor.Run(block[0])`, where `block[0]` is the first and only block defined in above `ProgramDesc` message. + +The default `main` function is defined as follows: + +```go +func main() { + paddlepaddle() + fluid.run() +} +``` + +## The Concurrent Version + +By parallelizing the above program, we could support very big tensor X by splitting into small pieces {x_1, x_2, ...} and sent each piece to worker process/node for parallel multiplication. + +In this case, we can write a transpiler that takes a `ProgramDesc` message that represents the above example program and outputs two `ProgramDesc` messages, one for running on the master process/node, and the other one for worker processes/nodes. + +### The Master Program + +The master program could look like the following: + +```protobuf +message ProgramDesc { + block[0] = Block { + vars = [X, L, Y], + ops = [ + read(output = X) + kube_get_workers_addrs(output = L) + Y = tensor_array(len(L)) + parallel_for(input = X, output = Y, + attrs = {L, block_id(1)}) # referring to block 1 + ] + } + + block[1] = Block { + parent = 0, + vars = [x, y, index], + ops = [ + slice(input = [X, index], output = x) # index is initialized by parallel_for + send(input = x, attrs = L[index]) + recv(outputs = y, attrs = L[index]) + assign(input = y, output = Y[index]) + ] + } +} +``` + +The equivalent Fluid program (calling the Go binding) is: + +```go +func main() { //// block 0 + X = fluid.read(...) + L = fluid.k8s.get_worker_addrs() + Y = fluid.tensor_array(len(L)) + fluid.parallel_for(X, L, + func(index int) { //// block 1 + x = X[index] + fluid.send(L[index], x) + y = fluid.recv(L[index]) + Y[index] = y + }) +} +``` + +An explanation of the above program: + +- `fluid.k8s` is a package that provides access to Kubernetes API. +- `fluid.k8s.get_worker_addrs` returns the list of IP and ports of all pods of the current job except for the current one (the master pod). +- `fluid.tensor_array` creates a [tensor array](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor_array.h). `fluid.parallel_for` creates a `ParallelFor` intrinsic, which, when executed, + + 1. creates `len(L)` scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named "index" in the scope to an integer value in the range `[0, len(L)-1]`, and + 2. creates `len(L)` threads by calling into the `ThreadPool` singleton, each thread + 1. creates an Executor instance, and + 2. calls `Executor.Run(block)`, where `block` is block 1 as explained above. +1. Please be aware that block 1 is a sub-block of block 0, so ops in block 1 could refer to variables defined in block 0. + +### The Worker Program + +The worker program looks like + +```go +func main() { + W = Tensor(...) + x = fluid.listen_and_do( + fluid.k8s.self_addr(), + func(input Tensor) { + output = fluid.mult(input, W) + }) +} +``` + +where + +- `fluid.listen_and_do` creates a `ListenAndDo` intrinsic, which, when executed, + 1. listens on the current pod's IP address, as returned by `fliud.k8s.self_addr()`, + 2. once a connection is established, + 1. creates a scope of two parameters, "input" and "output", + 2. reads a [Fluid variable](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h) and saves it into "input", + 3. creates an Executor instance and calls `Executor.Run(block)`, where the block is generated by running the lambda specified as the second parameter of `fluid.listen_and_do`. + +## Summarization + +From the above example, we see that: + +1. Fluid enables the imperative programming paradigm by: + 1. letting users describe a program, but not a model (a sequence of layers, or a graph of operators), and + 2. call the `fluid.run` function that runs the program implicitly. +1. The program is described as a `ProgramDesc` protobuf message. +2. Function `Executor.Run` takes a block, instead of a `ProgramDesc`, as its parameter. +3. `fluid.run` calls `Executor.Run` to run the first block in the `ProgramDesc` message. +4. `Executor.Run`'s implementation is extremely simple -- it doesn't plan the execution nor create threads; instead, it runs on the current thread and execute intrinsics/operators' `Run` method sequentially as they appear in the `Block.ops` array. +5. Intrinsics/operators' `Run` method might create threads. For example, the `ListenAndDo` operator creates a thread to handle each incoming request. +6. Threads are not necessarily OS thread; instead, they could be [green threads](https://en.wikipedia.org/wiki/Green_threads) managed by ThreadPool. Multiple green threads might run on the same OS thread. An example green threads is Go's [goroutines](https://tour.golang.org/concurrency/1). diff --git a/develop/doc_cn/design/concurrent_programming.html b/develop/doc_cn/design/concurrent_programming.html new file mode 100644 index 00000000000..584ed60ae35 --- /dev/null +++ b/develop/doc_cn/design/concurrent_programming.html @@ -0,0 +1,443 @@ + + + + + + + + + + + Design Doc: Concurrent Programming with Fluid — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: Concurrent Programming with Fluid
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Concurrent Programming with Fluid

+

With PaddlePaddle Fluid, users describe a program other than a model. The program is a ProgramDesc protobuf message. TensorFlow/MxNet/Caffe2 applications generate protobuf messages too, but their protobuf messages represent the model, a graph of operators, but not the program that trains/uses the model.

+

Many know that when we program TensorFlow, we can specify the device on which each operator runs. This allows us to create a concurrent/parallel AI application. An interesting questions is how does a ProgramDesc represents a concurrent program?

+

The answer relies on the fact that a ProgramDesc is similar to an abstract syntax tree (AST) that describes a program. So users just program a concurrent program that they do with any concurrent programming language, e.g., Go.

+
+

An Analogy

+

The following table compares concepts in Fluid and Go

+

| Go | Fluid | +|—-|——-| +|user-defined functions | layers | +| control-flow and built-in functions | intrinsics/operators | +| goroutines, channels | class ThreadPool | +| runtime | class Executor |

+
+
+

An Example Concurrent Program

+

To review all above concepts in an example, let us take a simple program and writes its distributed version.

+

Suppose that we want to parallelize a naive Fluid program (written in Go and calling Fluid’s Go binding) that multiplies two tensors.

+
import "fluid"
+
+func paddlepaddle() {
+  X = fluid.read(...)
+  W = fluid.Tensor(...)
+  Y = fluid.mult(X, W)
+}
+
+
+

Please be aware that the Fluid’s Go binding provides the default main function, which calls the paddlepaddle function, which, in this case, is defined in above program and creates the following ProgramDesc message.

+
message ProgramDesc {
+  block[0] = Block {
+    vars = [X, W, Y],
+    ops = [
+      read(output = X)
+      assign(input = ..., output = W)
+      mult(input = {X, W}, output = Y)
+    ],
+  }
+}
+
+
+

Then, the default main function calls fluid.run(), which creates an instance of the class Executor and calls Executor.Run(block[0]), where block[0] is the first and only block defined in above ProgramDesc message.

+

The default main function is defined as follows:

+
func main() {
+  paddlepaddle()
+  fluid.run()
+}
+
+
+
+
+

The Concurrent Version

+

By parallelizing the above program, we could support very big tensor X by splitting into small pieces {x_1, x_2, ...} and sent each piece to worker process/node for parallel multiplication.

+

In this case, we can write a transpiler that takes a ProgramDesc message that represents the above example program and outputs two ProgramDesc messages, one for running on the master process/node, and the other one for worker processes/nodes.

+
+

The Master Program

+

The master program could look like the following:

+
message ProgramDesc {
+  block[0] = Block {
+    vars = [X, L, Y],
+    ops = [
+      read(output = X)
+      kube_get_workers_addrs(output = L)
+      Y = tensor_array(len(L))
+      parallel_for(input = X, output = Y, 
+                   attrs = {L, block_id(1)}) # referring to block 1
+    ]
+  }
+  
+  block[1] = Block {
+    parent = 0,
+    vars = [x, y, index],
+    ops = [
+      slice(input = [X, index], output = x) # index is initialized by parallel_for
+      send(input = x, attrs = L[index])
+      recv(outputs = y, attrs = L[index])
+      assign(input = y, output = Y[index])
+    ]
+  }
+}
+
+
+

The equivalent Fluid program (calling the Go binding) is:

+
func main() {  //// block 0
+  X = fluid.read(...)
+  L = fluid.k8s.get_worker_addrs()
+  Y = fluid.tensor_array(len(L))
+  fluid.parallel_for(X, L, 
+                     func(index int) {  //// block 1
+                       x = X[index]
+                       fluid.send(L[index], x)
+                       y = fluid.recv(L[index])
+                       Y[index] = y
+                     })
+}
+
+
+

An explanation of the above program:

+
    +
  • fluid.k8s is a package that provides access to Kubernetes API.
    • +
  • fluid.k8s.get_worker_addrs returns the list of IP and ports of all pods of the current job except for the current one (the master pod).
    • +
  • fluid.tensor_array creates a tensor array. fluid.parallel_for creates a ParallelFor intrinsic, which, when executed,
      +
    1. creates len(L) scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named “index” in the scope to an integer value in the range [0, len(L)-1], and
      2. +
    2. creates len(L) threads by calling into the ThreadPool singleton, each thread
        +
      1. creates an Executor instance, and
        2. +
      2. calls Executor.Run(block), where block is block 1 as explained above.
        3. +
      +
      3. +
    +
    • +
+
    +
  1. Please be aware that block 1 is a sub-block of block 0, so ops in block 1 could refer to variables defined in block 0.
    2. +
+
+
+

The Worker Program

+

The worker program looks like

+
func main() {
+  W = Tensor(...)
+  x = fluid.listen_and_do(
+        fluid.k8s.self_addr(),
+        func(input Tensor) {
+          output = fluid.mult(input, W)
+        })
+}
+
+
+

where

+
    +
  • fluid.listen_and_do creates a ListenAndDo intrinsic, which, when executed,
      +
    1. listens on the current pod’s IP address, as returned by fliud.k8s.self_addr(),
      2. +
    2. once a connection is established,
        +
      1. creates a scope of two parameters, “input” and “output”,
        2. +
      2. reads a Fluid variable and saves it into “input”,
        3. +
      3. creates an Executor instance and calls Executor.Run(block), where the block is generated by running the lambda specified as the second parameter of fluid.listen_and_do.
        4. +
      +
      3. +
    +
    • +
+
+
+
+

Summarization

+

From the above example, we see that:

+
    +
  1. Fluid enables the imperative programming paradigm by:
      +
    1. letting users describe a program, but not a model (a sequence of layers, or a graph of operators), and
      2. +
    2. call the fluid.run function that runs the program implicitly.
      3. +
    +
    2. +
  2. The program is described as a ProgramDesc protobuf message.
    3. +
  3. Function Executor.Run takes a block, instead of a ProgramDesc, as its parameter.
    4. +
  4. fluid.run calls Executor.Run to run the first block in the ProgramDesc message.
    5. +
  5. Executor.Run‘s implementation is extremely simple – it doesn’t plan the execution nor create threads; instead, it runs on the current thread and execute intrinsics/operators’ Run method sequentially as they appear in the Block.ops array.
    6. +
  6. Intrinsics/operators’ Run method might create threads. For example, the ListenAndDo operator creates a thread to handle each incoming request.
    7. +
  7. Threads are not necessarily OS thread; instead, they could be green threads managed by ThreadPool. Multiple green threads might run on the same OS thread. An example green threads is Go’s goroutines.
    8. +
+
+
+ + +
+
+
+ + +
+ +
+

+ © 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/fluid.html b/develop/doc_cn/design/fluid.html index 8757096f764..012291a52d9 100644 --- a/develop/doc_cn/design/fluid.html +++ b/develop/doc_cn/design/fluid.html @@ -321,7 +321,7 @@

Towards a Deep Learning Language and the Compiler

We can change the if-then-else and loop structure a little bit in the above Fluid example programs, to make it into a new programming language, different than Python.

-

Even if we do not invent a new language, as long as we get the ProgramDesc message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using nvcc. Another transpiler could generate MKL-friendly code that should be built using icc from Intel. More interestingly, we can translate a Fluid program into its distributed version of two ProgramDesc messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the concurrent programming design document would be a good pointer. The following figure explains the proposed two-stage process:

+

Even if we do not invent a new language, as long as we get the ProgramDesc message filled in, we can write a transpiler, which translates each invocation to an operator, into a C++ call to a kernel function of that operator. For example, a transpiler that weaves the CUDA kernels outputs an NVIDIA-friendly C++ program, which can be built using nvcc. Another transpiler could generate MKL-friendly code that should be built using icc from Intel. More interestingly, we can translate a Fluid program into its distributed version of two ProgramDesc messages, one for running on the trainer process, and the other one for the parameter server. For more details of the last example, the concurrent programming design document would be a good pointer. The following figure explains the proposed two-stage process:

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