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提交 240adef1 编写于 作者: Y Yibing Liu
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Merge branch 'develop' of upstream into margin_rank_loss_op_dev

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CMakeLists.txt
浏览文件 @ 240adef1
......@@ -86,6 +86,14 @@ if(ANDROID OR IOS)
"Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLML OFF CACHE STRING
"Disable MKLML package when cross-compiling for Android and iOS" FORCE)
# Compile PaddlePaddle mobile inference library
if (NOT WITH_C_API)
set(WITH_C_API ON CACHE STRING
"Always compile the C_API when cross-compiling for Android and iOS" FORCE)
endif()
set(MOBILE_INFERENCE ON)
add_definitions(-DPADDLE_MOBILE_INFERENCE)
endif()
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
......@@ -160,9 +168,11 @@ endif(USE_NNPACK)
add_subdirectory(proto)
# "add_subdirectory(go)" should be placed after the following loine,
# because it depends on paddle/optimizer.
add_subdirectory(paddle/optimizer)
if(NOT MOBILE_INFERENCE)
# "add_subdirectory(go)" should be placed after the following loine,
# because it depends on paddle/optimizer.
add_subdirectory(paddle/optimizer)
endif()
# "add_subdirectory(paddle)" and "add_subdirectory(python)" should be
# placed after this block, because they depends on it.
......
cmake/configure.cmake
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......@@ -24,6 +24,10 @@ if(WITH_DOUBLE)
add_definitions(-DPADDLE_TYPE_DOUBLE)
endif(WITH_DOUBLE)
if(WITH_TESTING)
add_definitions(-DPADDLE_WITH_TESTING)
endif(WITH_TESTING)
if(NOT WITH_TIMER)
add_definitions(-DPADDLE_DISABLE_TIMER)
endif(NOT WITH_TIMER)
......@@ -49,11 +53,12 @@ if(NOT WITH_GOLANG)
endif(NOT WITH_GOLANG)
if(NOT WITH_GPU)
add_definitions(-DPADDLE_ONLY_CPU)
add_definitions(-DHPPL_STUB_FUNC)
list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
else()
add_definitions(-DPADDLE_WITH_CUDA)
FIND_PACKAGE(CUDA REQUIRED)
if(${CUDA_VERSION_MAJOR} VERSION_LESS 7)
......
cmake/util.cmake
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......@@ -73,25 +73,43 @@ function(link_paddle_exe TARGET_NAME)
generate_rdma_links()
endif()
target_circle_link_libraries(${TARGET_NAME}
ARCHIVE_START
paddle_gserver
paddle_function
ARCHIVE_END
paddle_pserver
paddle_trainer_lib
paddle_network
paddle_math
paddle_utils
paddle_parameter
paddle_proto
paddle_cuda
paddle_optimizer
${EXTERNAL_LIBS}
${CMAKE_THREAD_LIBS_INIT}
${CMAKE_DL_LIBS}
${RDMA_LD_FLAGS}
${RDMA_LIBS})
if(MOBILE_INFERENCE)
target_circle_link_libraries(${TARGET_NAME}
ARCHIVE_START
paddle_gserver
paddle_function
ARCHIVE_END
paddle_math
paddle_utils
paddle_parameter
paddle_proto
paddle_cuda
${EXTERNAL_LIBS}
${CMAKE_THREAD_LIBS_INIT}
${CMAKE_DL_LIBS}
${RDMA_LD_FLAGS}
${RDMA_LIBS})
else()
target_circle_link_libraries(${TARGET_NAME}
ARCHIVE_START
paddle_gserver
paddle_function
ARCHIVE_END
paddle_pserver
paddle_trainer_lib
paddle_network
paddle_math
paddle_utils
paddle_parameter
paddle_proto
paddle_cuda
paddle_optimizer
${EXTERNAL_LIBS}
${CMAKE_THREAD_LIBS_INIT}
${CMAKE_DL_LIBS}
${RDMA_LD_FLAGS}
${RDMA_LIBS})
endif()
if(ANDROID)
target_link_libraries(${TARGET_NAME} log)
......
doc/api/v1/index_cn.rst
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......@@ -21,7 +21,7 @@ Model Config API
trainer_config_helpers/optimizers.rst
trainer_config_helpers/data_sources.rst
trainer_config_helpers/layers.rst
trainer_config_helpers/activations.rst
trainer_config_helpers/activations.rst
trainer_config_helpers/poolings.rst
trainer_config_helpers/networks.rst
trainer_config_helpers/evaluators.rst
......
doc/api/v2/config/layer.rst
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......@@ -345,6 +345,11 @@ clip
.. autoclass:: paddle.v2.layer.clip
:noindex:
resize
------
.. autoclass:: paddle.v2.layer.resize
:noindex:
slope_intercept
---------------
.. autoclass:: paddle.v2.layer.slope_intercept
......
doc/design/block.md
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......@@ -5,12 +5,12 @@
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.
- TensorFlow, Caffe2, Mxnet: graph 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.
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:
......@@ -24,14 +24,14 @@ A key difference is that a C++ program describes a one pass computation, whereas
## 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:
The existence of the backward pass makes the execution of a block of PaddlePaddle different from traditional programs:
| 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|
| programming languages | PaddlePaddle |
|-----------------------|---------------------------------|
| stack | scope hierarchy |
| stack frame | scope |
| push at entering block| push at entering block |
| pop at leaving block | destroy when minibatch completes|
1. In traditional programs:
......@@ -42,9 +42,9 @@ The existence of the backward makes the execution of a block of traditional prog
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*.
- PaddlePaddle doesn't pop a scope after the execution of the block because variables therein are 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.
- After the processing of a minibatch, PaddlePaddle destroys the scope hierarchy.
## Use Blocks in C++ and PaddlePaddle Programs
......@@ -55,17 +55,23 @@ Let us consolidate the discussion by presenting some examples.
The following C++ programs shows how blocks are used with the `if-else` structure:
```c++
namespace pd = paddle;
int x = 10;
int y = 20;
int out;
int y = 1;
int z = 10;
bool cond = false;
int o1, o2;
if (cond) {
int z = x + y;
out = softmax(z);
o1 = z;
o2 = pd::layer::softmax(z);
} else {
int z = fc(x);
out = z;
int d = pd::layer::fc(z);
o1 = d;
o2 = d+1;
}
```
An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator](./if_else_op.md) is as follows:
......@@ -73,57 +79,55 @@ An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator
```python
import paddle as pd
x = var(10)
y = var(20)
cond = var(false)
ie = pd.create_ifelseop(inputs=[x], output_num=1)
x = minibatch([10, 20, 30]) # shape=[None, 1]
y = var(1) # shape=[1], value=1
z = minibatch([10, 20, 30]) # shape=[None, 1]
cond = larger_than(x, 15) # [false, true, true]
ie = pd.ifelse()
with ie.true_block():
x = ie.inputs(true, 0)
z = operator.add(x, y)
ie.set_output(true, 0, operator.softmax(z))
d = pd.layer.add_scalar(x, y)
ie.output(d, pd.layer.softmax(d))
with ie.false_block():
x = ie.inputs(false, 0)
z = layer.fc(x)
ie.set_output(true, 0, operator.softmax(z))
out = b(cond)
d = pd.layer.fc(z)
ie.output(d, d+1)
o1, o2 = ie(cond)
```
In both examples, the left branch computes `softmax(x+y)` and the right branch computes `fc(x)`.
In both examples, the left branch computes `x+y` and `softmax(x+y)`, the right branch computes `fc(x)` and `x+1` .
The difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances.
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)
The following RNN model in PaddlePaddle 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
x = sequence([10, 20, 30]) # shape=[None, 1]
m = var(0) # shape=[1]
W = var(0.314, param=true) # shape=[1]
U = var(0.375, param=true) # shape=[1]
rnn = pd.rnn()
with rnn.step():
h = rnn.memory(init = m)
h_prev = rnn.previous_memory(h)
a = layer.fc(W, x)
b = layer.fc(U, h_prev)
s = pd.add(a, b)
act = pd.sigmoid(s)
rnn.update_memory(h, act)
rnn.output(a, b)
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* m = {0};
int* W = {0.314};
int* U = {0.375};
int mem[sizeof(x) / sizeof(x[0]) + 1];
int o1[sizeof(x) / sizeof(x[0]) + 1];
......@@ -131,25 +135,21 @@ 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 a = W * x;
int b = Y * mem[i-1];
int s = 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.
Like TensorFlow, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest 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 generation of this protobuf message is similar to how a compiler generates a binary executable file. The execution of the message is similar to how the OS executes the binary file.
## The "Binary Executable File Format"
......@@ -186,8 +186,8 @@ Also, the RNN operator in above example is serialized into a protobuf message of
```
OpDesc {
inputs = {0} // the index of x
outputs = {5, 3} // indices of act and hidden_out
inputs = {0} // the index of x in vars of BlockDesc above
outputs = {5, 3} // indices of act and hidden_out in vars of BlockDesc above
attrs {
"memories" : {1} // the index of h
"step_net" : <above step net>
......@@ -203,32 +203,32 @@ This `OpDesc` value is in the `ops` field of the `BlockDesc` value representing
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
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])
a = pd.Variable(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)
with rnn.stepnet():
x = a.as_step_input()
# reuse fc's parameter
fc_without_b = pd.get_variable("fc.w")
net.set_outputs(fc_without_b)
rnn.output(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.
The method `pd.get_variable` can help retrieve a Variable by the name. The Variable may be stored 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:
`SymbolTable` can do the following:
- 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).
- verify if a variable was declared,
- make it possible to implement type checking (offer Protobuf message pointers to `InferShape` handlers).
```c++
......@@ -240,19 +240,18 @@ class SymbolTable {
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.
// 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 is left default.
VarDesc* NewVar(const string& name="");
// find a VarDesc by name, if recursive true, find parent's SymbolTable
// find a VarDesc by name, if recursive is 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.
// be proposed and embedded into pybind to enable python operation on C++ pointers.
VarDesc* FindVar(const string& name, bool recursive=true);
OpDesc* FindOp(const string& name);
......@@ -270,7 +269,7 @@ class SymbolTable {
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.
The `Block` class takes a `BlockDesc` as input, and provides `Run` and `InferShape` functions.
```c++
......@@ -302,7 +301,7 @@ public:
void CreateVariables(const framework::Scope& scope);
void CreateOperators();
// some other necessary interfaces of NetOp are list below
// some other necessary interfaces of NetOp are listed below
// ...
private:
......@@ -316,15 +315,14 @@ private:
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.
There is another important interface called `Eval`, which takes some arguments called targets and generates a minimal graph which treats 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
// NOTE: The return type is not a Block but the block's description so that this can be distributed
// to a cluster.
BlockDesc Prune(const BlockDesc& desc, vector<string> targets);
......
doc/design/gan_api.md 0 → 100644
浏览文件 @ 240adef1
# Design for GAN
GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas.
It applies several important concepts in machine learning system design, including building and running subgraphs, dependency tracing, different optimizers in one executor and so forth.
In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation.
<p align="center">
<img src="./test.dot.png" width = "35%" align="center"/><br/>
Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run.
</p>
The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563.
<p align="center">
<img src="./dcgan.png" width = "90%" align="center"/><br/>
Figure 2. Photo borrowed from the original DC-GAN paper.
</p>
## The Conditional-GAN might be a class.
This design we adopt the popular open source design in https://github.com/carpedm20/DCGAN-tensorflow and https://github.com/rajathkmp/DCGAN. It contains following data structure:
- DCGAN(object): which contains everything required to build a GAN model. It provides following member functions methods as API:
- __init__(...): Initialize hyper-parameters (like conv dimension and so forth), and declare model parameters of discriminator and generator as well.
- generator(z, y=None): Generate a fake image from input noise z. If the label y is provided, the conditional GAN model will be chosen.
Returns a generated image.
- discriminator(image):
Given an image, decide if it is from a real source or a fake one.
Returns a 0/1 binary label.
- build_model(self):
build the whole GAN model, define training loss for both generator and discrimator.
## Discussion on Engine Functions required to build GAN
- Trace the tensor and variable dependency in the engine executor. (Very critical, otherwise GAN can'be be trained correctly)
- Different optimizers responsible for optimizing different loss.
To be more detailed, we introduce our design of DCGAN as following:
### Class member Function: Initializer
- Set up hyper-parameters, including condtional dimension, noise dimension, batch size and so forth.
- Declare and define all the model variables. All the discriminator parameters are included in the list self.theta_D and all the generator parameters are included in the list self.theta_G.
```python
class DCGAN(object):
def __init__(self, y_dim=None):
# hyper parameters
self.y_dim = y_dim # conditional gan or not
self.batch_size = 100
self.z_dim = z_dim # input noise dimension
# define parameters of discriminators
self.D_W0 = pd.Variable(shape=[3,3, 1, 128], data=pd.gaussian_normal_randomizer())
self.D_b0 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data
self.D_W1 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer())
self.D_b1 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data
self.D_W2 = pd.Varialble(np.random.rand(128, 1))
self.D_b2 = pd.Variable(np.zeros(128))
self.theta_D = [self.D_W0, self.D_b0, self.D_W1, self.D_b1, self.D_W2, self.D_b2]
# define parameters of generators
self.G_W0 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer())
self.G_b0 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data
self.G_W1 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer())
self.G_b1 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data
self.G_W2 = pd.Varialble(np.random.rand(128, 1))
self.G_b2 = pd.Variable(np.zeros(128))
self.theta_G = [self.G_W0, self.G_b0, self.G_W1, self.G_b1, self.G_W2, self.G_b2]
```
### Class member Function: Generator
- Given a noisy input z, returns a fake image.
- Concatenation, batch-norm, FC operations required;
- Deconv layer required, which is missing now...
```python
class DCGAN(object):
def generator(self, z, y = None):
# input z: the random noise
# input y: input data label (optional)
# output G_im: generated fake images
if not self.y_dim:
z = pd.layer.concat(1, [z, y])
G_h0 = pd.layer.fc(z, self.G_w0, self.G_b0)
G_h0_bn = pd.layer.batch_norm(G_h0)
G_h0_relu = pd.layer.relu(G_h0_bn)
G_h1 = pd.layer.deconv(G_h0_relu, self.G_w1, self.G_b1)
G_h1_bn = pd.layer.batch_norm(G_h1)
G_h1_relu = pd.layer.relu(G_h1_bn)
G_h2 = pd.layer.deconv(G_h1_relu, self.G_W2, self.G_b2))
G_im = pd.layer.tanh(G_im)
return G_im
```
### Class member function: Discriminator
- Given a noisy input z, returns a fake image.
- Concatenation, Convolution, batch-norm, FC, Leaky-ReLU operations required;
```python
class DCGAN(object):
def discriminator(self, image):
# input image: either generated images or real ones
# output D_h2: binary logit of the label
D_h0 = pd.layer.conv2d(image, w=self.D_w0, b=self.D_b0)
D_h0_bn = pd.layer.batchnorm(h0)
D_h0_relu = pd.layer.lrelu(h0_bn)
D_h1 = pd.layer.conv2d(D_h0_relu, w=self.D_w1, b=self.D_b1)
D_h1_bn = pd.layer.batchnorm(D_h1)
D_h1_relu = pd.layer.lrelu(D_h1_bn)
D_h2 = pd.layer.fc(D_h1_relu, w=self.D_w2, b=self.D_b2)
return D_h2
```
### Class member function: Build the model
- Define data readers as placeholders to hold the data;
- Build generator and discriminators;
- Define two training losses for discriminator and generator, respectively.
If we have execution dependency engine to back-trace all tensors, the module building our GAN model will be like this:
```python
class DCGAN(object):
def build_model(self):
if self.y_dim:
self.y = pd.data(pd.float32, [self.batch_size, self.y_dim])
self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size])
self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size])
self.z = pd.data(tf.float32, [None, self.z_size])
# step 1: generate images by generator, classify real/fake images with discriminator
if self.y_dim: # if conditional GAN, includes label
self.G = self.generator(self.z, self.y)
self.D_t = self.discriminator(self.images)
# generated fake images
self.sampled = self.sampler(self.z, self.y)
self.D_f = self.discriminator(self.G)
else: # original version of GAN
self.G = self.generator(self.z)
self.D_t = self.discriminator(self.images)
# generate fake images
self.sampled = self.sampler(self.z)
self.D_f = self.discriminator(self.images)
# step 2: define the two losses
self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size))
self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size))
self.d_loss = self.d_loss_real + self.d_loss_fake
self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_f, np.ones(self.batch_szie))
```
If we do not have dependency engine but blocks, the module building our GAN model will be like this:
```python
class DCGAN(object):
def build_model(self, default_block):
# input data in the default block
if self.y_dim:
self.y = pd.data(pd.float32, [self.batch_size, self.y_dim])
self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size])
# self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size])
self.z = pd.data(tf.float32, [None, self.z_size])
# step 1: generate images by generator, classify real/fake images with discriminator
with pd.default_block().g_block():
if self.y_dim: # if conditional GAN, includes label
self.G = self.generator(self.z, self.y)
self.D_g = self.discriminator(self.G, self.y)
else: # original version of GAN
self.G = self.generator(self.z)
self.D_g = self.discriminator(self.G, self.y)
self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_g, np.ones(self.batch_szie))
with pd.default_block().d_block():
if self.y_dim: # if conditional GAN, includes label
self.D_t = self.discriminator(self.images, self.y)
self.D_f = self.discriminator(self.G, self.y)
else: # original version of GAN
self.D_t = self.discriminator(self.images)
self.D_f = self.discriminator(self.G)
# step 2: define the two losses
self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size))
self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size))
self.d_loss = self.d_loss_real + self.d_loss_fake
```
Some small confusion and problems with this design:
- D\_g and D\_f are actually the same thing, but has to be written twice; i.e., if we want to run two sub-graphs conceptually, the same codes have to be written twice if they are shared by the graph.
- Requires ability to create a block anytime, rather than in if-else or rnn only;
## Main function for the demo:
Generally, the user of GAN just need to the following things:
- Define an object as DCGAN class;
- Build the DCGAN model;
- Specify two optimizers for two different losses with respect to different parameters.
```python
# pd for short, should be more concise.
from paddle.v2 as pd
import numpy as np
import logging
if __name__ == "__main__":
# dcgan class in the default graph/block
# if we use dependency engine as tensorflow
# the codes, will be slightly different like:
# dcgan = DCGAN()
# dcgan.build_model()
with pd.block() as def_block:
dcgan = DCGAN()
dcgan.build_model(def_block)
# load mnist data
data_X, data_y = self.load_mnist()
# Two subgraphs required!!!
with pd.block().d_block():
d_optim = pd.train.Adam(lr = .001, beta= .1)
d_step = d_optim.minimize(dcgan.d_loss, dcgan.theta_D)
with pd.block.g_block():
g_optim = pd.train.Adam(lr = .001, beta= .1)
g_step = pd.minimize(dcgan.g_loss, dcgan.theta_G)
# executor
sess = pd.executor()
# training
for epoch in xrange(10000):
for batch_id in range(N / batch_size):
idx = ...
# sample a batch
batch_im, batch_label = data_X[idx:idx+batch_size], data_y[idx:idx+batch_size]
# sample z
batch_z = np.random.uniform(-1., 1., [batch_size, z_dim])
if batch_id % 2 == 0:
sess.run(d_step,
feed_dict = {dcgan.images: batch_im,
dcgan.y: batch_label,
dcgan.z: batch_z})
else:
sess.run(g_step,
feed_dict = {dcgan.z: batch_z})
```
# More thinking about dependency engine v.s. block design:
- What if we just want to run an intermediate result? Do we need to run the whole block/graph?
- Should we call eval() to get the fake images in the first stage? And then train the discriminator in the second stage?
doc/design/if_else_op.md
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IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has N instances. If cond[i] == True, input instance input[i] will go through true_block() and generate output[i]; otherwise it will produce output from false_bloack().
# The `IfElse` Operator
```python
import paddle as pd
PaddlePaddle's `IfElse` operator differs from TensorFlow's:
x = var()
y = var()
cond = var()
default_value = var()
b = pd.create_ifelseop(inputs=[x], output_num=1)
with b.true_block():
x = b.inputs(0)
z = operator.add(x, y)
b.set_output(0, operator.softmax(z))
with b.false_block():
x = b.inputs(0)
z = layer.fc(x)
b.set_output(0, operator.softmax(z))
out = b(cond)
```
- the TensorFlow version takes a scalar boolean value as the condition so that the whole mini-batch goes to either the true or the false branch, whereas
- the PaddlePaddle version takes a vector of boolean value as the condition, and instances corresponding to true values go to the true branch, those corresponding to false values go to the false branch.
## Example
The following PaddlePaddle program shows the usage of the IfElse operator:
If only true_block is set in an IfElseOp, a special case is that we can have a default value for false as:
```python
import paddle as pd
x = var()
y = var()
cond = var()
default_value = var()
b = pd.create_ifelseop(inputs=[x], output_num=1, default_value)
with b.true_block():
x = b.inputs(0)
z = operator.add(x, y)
b.set_output(0, operator.softmax(z))
x = minibatch([10, 20, 30]) # shape=[None, 1]
y = var(1) # shape=[1], value=1
z = minibatch([10, 20, 30]) # shape=[None, 1]
cond = larger_than(x, 15) # [false, true, true]
ie = pd.ifelse()
with ie.true_block():
d = pd.layer.add(x, y)
ie.output(d, pd.layer.softmax(d))
with ie.false_block():
d = pd.layer.fc(z)
ie.output(d, d+1)
o1, o2 = ie(cond)
```
out = b(cond)
A challenge to implement the `IfElse` operator is to infer those variables to be split, or, say, to identify the variable of the mini-batch or those derived from the mini-batch.
An equivalent C++ program is as follows:
```c++
namespace pd = paddle;
int x = 10;
int y = 1;
int z = 10;
bool cond = false;
int o1, o2;
if (cond) {
int d = x + y;
o1 = z;
o2 = pd::layer::softmax(z);
} else {
int d = pd::layer::fc(z);
o1 = d;
o2 = d+1;
}
```
where default_value is a list of vars for `cond` == False.
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## Optimizer Design
### The Problem
A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works:
1. the forward pass, which computes intermediate results and the cost(s),
1. the backward pass, which derives gradients from intermediate results and costs, and
1. the optimization pass, which update model parameters to optimize the cost(s).
These works rely on three kinds of operators:
1. forward operators,
1. gradient operators, and
1. optimization operators.
It's true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically.
In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.
### High-level Python API to describe the training process
1. User write code to describe the network:
```python
images = layer.data("images")
labels = layer.data("labels")
w1 = pd.var("w1")
b1 = pd.var("b1")
hidden = layer.fc(images, w=w1, b=b1)
cost = layer.mse(hidden, labels)
```
The above code snippet will create forward operators in [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
2. Users create a certain kind of Optimizer with some argument.
```python
optimizer = AdagradOptimizer(learing_rate=0.001)
```
3. Users use the optimizer to `minimize` a certain `cost` through updating parameters in parameter_list.
```python
opt_op_list = optimizer.minimize(cost, parameter_list=[w1, b1])
```
The above code snippet will create gradient and optimization operators in Block. The return value of `minimize()` is list of optimization operators that will be run by session.
4. Users use Session/Executor to run this opt_op_list as target to do training.
```python
sess.run(target= opt_op_list, ...)
```
#### Optimizer Python interface:
```python
class Optimizer(object):
"""Optimizer Base class.
"""
def __init__(self):
pass
def create_backward_pass(self, loss, parameter_list=None):
"""
create and add gradient Operators in BlockDesc to Compute gradients of `loss`
for parameters in parameter_list
Args:
loss: an variable generated by cost function.
parameter_list: parameters that need to compute gradient and update to optimize the lost.
Returns:
list of (parameters, gradients) pair.
"""
return None
def create_optimization_pass(self, parameters_and_grads):
"""Add optimization operators to update gradients to variables.
Args:
parameters_and_grads: a list of (variable, gradient) pair to update.
Returns:
optmization_op_list: a list of optimization operator that will update parameter using gradient.
"""
return None
def minimize(self, loss, parameter_list):
"""Add operations to minimize `loss` by updating `parameter_list`.
This method combines interface `create_backward_pass()` and
`create_optimization_pass()` into one.
"""
params_grads = self.create_backward_pass(loss, parameter_list)
update_ops = self.create_optimization_pass(params_grads)
return update_ops
```
Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer.
doc/design/program.md
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# Design Doc: ProgramDesc
# Design Doc: PaddlePaddle Programs
The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.
## Compile and Execution
A PaddlePaddle program consists of two parts -- the first generates a `ProgramDesc` protobuf message that describes the program, and the second runs this message using a C++ class `Executor`.
As described in [graph.md](./graph.md), the first five lines of the following PaddlePaddle program
A simple example PaddlePaddle program can be found in [graph.md](./graph.md):
```python
x = layer.data("images")
......@@ -13,36 +15,112 @@ optimize(cost)
train(cost, reader=mnist.train())
```
generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message:
The first five lines of the following PaddlePaddle program generates, or, compiles, the `ProgramDesc` message. The last line runs it.
```protobuf
message ProgramDesc {
repeated BlockDesc blocks = 1;
## Programs and Blocks
The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.
- program: some nested blocks
- [block](./block.md):
- some local variable definitions, and
- a sequence of operators
The concept of block comes from usual programs. For example, the following C++ program has three blocks:
```c++
int main() { // block 0
int i = 0;
if (i < 10) { // block 1
for (int j = 0; j < 10; j++) { // block 2
}
}
return 0;
}
```
The following PaddlePaddle program has three blocks:
```python
import paddle as pd // block 0
x = minibatch([10, 20, 30]) # shape=[None, 1]
y = var(1) # shape=[1], value=1
z = minibatch([10, 20, 30]) # shape=[None, 1]
cond = larger_than(x, 15) # [false, true, true]
ie = pd.ifelse()
with ie.true_block(): // block 1
d = pd.layer.add_scalar(x, y)
ie.output(d, pd.layer.softmax(d))
with ie.false_block(): // block 2
d = pd.layer.fc(z)
ie.output(d, d+1)
o1, o2 = ie(cond)
```
## `BlockDesc` and `ProgramDesc`
All protobuf messages are defined in `framework.proto`.
`BlockDesc` is straight-forward -- it includes local variable definitions, `vars`, and a sequence of operators, `ops`.
```protobuf
message BlockDesc {
required int32 parent = 1;
repeated VarDesc vars = 2;
repeated OpDesc ops = 3;
}
```
The parent ID indicates the parent block so that operators in a block can refer to variables defined locally and also those defined in their ancestor blocks.
All hierarchical blocks in a program are flattened and stored in an array. The block ID is the index of the block in this array.
```protobuf
message ProgramDesc {
repeated BlockDesc blocks = 1;
}
```
### Global Block
The global block is the first one in the above array.
## Operators that Use Blocks
In the above example, the operator `IfElseOp` has two blocks -- the true branch and the false branch.
The definition of `OpDesc` shows that an operator could have some attributes:
```protobuf
message OpDesc {
AttrDesc attrs = 1;
...
}
```
and an attribute could be of type block, which is, in fact, a block ID as described above:
```
message AttrDesc {
required AttrType type = 1;
required string name = 1;
// index into ProgramDesc::blocks when type==BLOCK
optional int32 block = 2;
enum AttrType {
INT = 1,
STRING = 2,
...
BLOCK = ...
}
required AttrType type = 2;
optional int32 block = 10; // when type == BLOCK
...
}
```
When each of the first five lines runs, related Python function, e.g., `layer.fc`, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc's accessed by the current OpDesc. These VarDesc's might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block.
A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in `ProgramDesc::blocks`, this implicitly assigns a zero-based ID to each block -- the index of the block in `ProgramDesc::blocks`. So that `AttrDesc::block` could be an integer block ID.
## InferShape
With this design, the InferShape function should take the following parameters:
......
doc/design/python_api.md 0 → 100644
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# Design Doc: Python API
Due to the refactorization of the PaddlePaddle core, we need Python classes to construct corresponding protobuf messages that describe a DL program.
| Python classes | Protobuf messages |
| --- | --- |
| Program | ProgramDesc |
| Block | BlockDesc |
| Operator | OpDesc |
| Variable | VarDesc |
Please be aware that these Python classes need to maintain some construction-time information, which are not part of the protobuf messages.
## Core Concepts
### Program
A `ProgramDesc` describes a [DL program](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md), which is composed of an array of `BlockDesc`s. The `BlockDesc`s in a `ProgramDesc` can have a tree-like hierarchical structure. However, the `ProgramDesc` onlys stores a flattened array of `BlockDesc`s. A `BlockDesc` refers to its parent block by its index in the array. For example, operators in the step block of an RNN operator need to be able to access variables in its ancestor blocks.
Whenever we create a block, we need to set its parent block to the current block, hence the Python class `Program` needs to maintain a data member `current_block`.
```python
class Program(objects):
def __init__(self):
self.desc = core.NewProgram() # a C++ ProgramDesc pointer.
self.blocks = vector<Block>()
self.blocks.append(Block(self, -1)) # the global block
self.current_block = 0 # initialized to the global block
def global_block():
return self.blocks[0]
def current_block():
return self.get_block(self.current_block)
def rollback():
self.current_block = self.current_block().parent_idx
def create_block():
new_block_idx = len(self.block)
self.blocks.append(Block(self, self.current_block))
self.current_block = new_block_idx
return current_block()
```
`Program` is an accessor to the protobuf message `ProgramDesc`, which is created in C++ space, because the InferShape function is in C++, which manipulates `VarDesc` messages, which are in turn members of `BlockDesc`, which is a member of `ProgramDesc`.
`Program` creates the first block as the global block in its constructor. All parameters and their initializer operators are in the global block.
### Block
A [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md) includes
1. a map from variable names to an instance of the Python `Variable` class, and
1. a list of `Operator` instances.
```python
class Block(objects):
def __init__(self, program, parent_idx):
self.desc = core.NewBlock(program.desc)
self.program = program
self.vars = map<string, Variable>()
self.ops = vector<Operator>()
self.parent_idx = parent_idx
def create_var(self, ...):
return Variable(self, ...)
def _create_global_var(self, ...):
program.global_block().create_var(...)
def create_parameter(self, name, ...):
# Parameter is a subclass of variable. See Parameter section for details.
self.vars[name] = Parameter(self._create_global_var(...), ...)
return self.vars[name]
def append_operator(self, ...):
self.ops.append(Operator(self, ...))
def prepend_operator(self, ...): # Parameter's ctor prepands initialize operators.
self.ops.prepend(Operator(self, ...))
```
`create_parameter` is necessary because parameters are global variables, defined in the global block, but can be created in some sub-blocks. For example, an FC layer in the step block of an RNN operator.
`prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block.
### Operator
The `Operator` class fills in the `OpDesc` message and calls the C++ function `InferShape` to infer the output shapes from the input shapes.
```python
class Operator(object):
def __init__(self,
block, # Block
type, # string
inputs, # dict<string, Variable>
outputs,# dict<stirng, Variable>
attrs # dict<string, Any>
):
self.desc = core.NewOpDesc(block.desc, type, inputs, outputs, attrs)
core.infer_shape(self.desc, inputs, outputs)
def type(self):
return self.desc.type()
```
`Operator` creates the `OpDesc` message in C++ space, so that it can call the `InferShape` function, which is in C++.
### Variable
Operators take Variables as its inputs and outputs.
```python
class Variable(object):
def __init__(self,
block=None, # Block
name=None, # string
shape, # tuple
dtype="float32", # string
lod_level=None # int
):
if name is None:
name = unique_name_generator()
self.name = name
self.block = block
self.desc = core.NewVarDesc(block.desc, name, shape, lod_level)
self.writer = None
```
Please be aware of `self.writer`, that tracks operator who creates the variable. It possible that there are more than one operators who write a variable, but in Python space, each write to a variable is represented by a Variable class. This is guaranteed by the fact that **`core.NewVarDesc` must NOT create a new `VarDesc` message if its name already exists in the specified block**.
### Parameter
A parameter is a global variable with an initializer (or load) operator.
```python
class Parameter(Variable):
def __init__(self,
block=None, # Block
name=None, # string
shape, # tuple
dtype="float32", # string
lod_level=None # int
trainable, # bool
initialize_op_attrs,
optimize_op_attrs):
super(Parameter, self).__init__(block, name, shape, dtype, lod_level)
self.trainable = trainable
self.optimize_op_attrs = optimize_op_attrs
block.prepend(Operator(block, # Block
initialize_op_attrs['type'], # string
None, # no inputs
self, # output is the parameter
initialize_op_attrs)
```
When users create a parameter, they can call
```python
program.create_parameter(
...,
init_attr={
type: "uniform_random",
min: -1.0,
max: 1.0,
})
)
```
In above example, `init_attr.type` names an initialize operator. It can also name the load operator
```python
init_attr={
type: "load",
filename: "something.numpy",
}
```
`optimize_op_attrs` is not in the `VarDesc` message, but kept in the Python instance, as it will be used in the Python space when creating the optimize operator's `OpDesc`, and will be in the `OpDesc` message.
## Layer Functions
A layer is a Python function that creates some operators and variables. Layers simplify the work of application programmers.
### Data Layer
```python
def data_layer(name, type, column_name):
block = the_current_program.glolal_block()
var = block.create_global_var(
name=name,
shape=[None] + type.dims(),
dtype=type.dtype)
block.prepend_operator(block,
type="Feed",
inputs = None,
outputs = [var],
{column_name: column_name})
return var
```
The input to the feed operator is a special variable in the global scope, which is the output of [Python readers](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md).
### FC Layer
```python
def fc_layer(input, size, ...):
block = program.current_block()
w = block.create_parameter(...)
b = block.create_parameter(...)
out = block.create_var()
op = block.append_operator("FC", X=input, W=w, b=b, out=out)
out.writer = op
return out
```
## Optimizer
[Optimizer Design Doc](./optimizer.md)
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# Design Doc: Session
## Abstract
The *session* object encapsulates the environment in which the
computation graph is executed.
We will have the *local* session and *remote* session, they offer the
same [interface](#interface). The local session encapsulates the local
runtime environment and the remote session encapsulates the cluster
runtime environment.
The local runtime environment contains:
1. computation devices (i.e., CPU, GPU) handles, and
1. the [scope](../scope.md) which holds all variables.
The remote runtime environment contains:
1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster,
and
1. the distributed [scope](../scope.md) in a cluster which holds all
variables.
The user can create a remote session on Paddle Cloud and evaluate the
computation graph with it. In this way, the user can control the
remote computation resource in a cluster from his local computer.
## Background
The current design has an implicit global session in which
`paddle.eval()` is executed. The pain point is:
Since the user is not able to explicitly switch between runtime
environments, the user cannot run a topology in two independent
environments.
For example, in reinforcement learning, the user may want to have a
stale model for inference and a fresh model for training, and only
replace the stale model with the fresh model periodically.
Furthermore, we have no concept that encapsulates a remote environment
that executes a computation graph.
We need the session object to address above issues.
## Session
A session is an object that owns the runtime environment. All
computations are executed through `session.eval()`.
### Interface
```python
eval(
targets,
feed_dict=None,
)
```
Evaluates the target Operations or Variables in `targets`.
- *targets*: the evaluation targets. Can be a single Operation or
Variable, or a list with the Operations or Variables as
elements. The value returned by `eval()` has the same shape as the
`target` argument.
The PaddlePaddle program is represented by
the [ProgramDesc](../design/program.md), `eval()` will infer the
ProgramDesc from the given targets and run the PaddlePaddle
program. Please
see
[this graph](./distributed_architecture.md#local-training-architecture) for
the detailed illustration for the local session
and
[this graph](./distributed_architecture.md#distributed-training-architecture) for
the detailed illustration for the remote session.
- *feed_dict*: a dictionary that contains the tensors which override
the edges of the computation graph.
feed_dict not only can provide the input data, it can override any
OP's input as well:
```python
a = pd.constant(2.0, name="a")
b = pd.variable(name="b")
c = pd.mul(a,b)
sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0
```
```python
close()
```
Closes the session and releases the scope that the session owns.
### Create a Local Session
```python
session(
devices=None
)
```
Creates a new session. One session owns one global scope, so creating
multiple sessions will create different scopes.
- *devices*: a single `string` or a list of `string` of device names,
the corresponding devices will be the computation devices for
`eval()`. If not specified, all available devices (e.g., all GPUs)
will be used. The user doesn't need to specify the CPU device since
it will be always used. Multiple sessions can use the same device.
#### Example
```Python
a = paddle.constant(1.0)
b = paddle.constant(2.0)
c = a + b
sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"])
sess.eval(c)
sess.close()
```
### Create a Remote Session
```python
create_cloud_job(
name,
num_trainer,
mem_per_trainer,
gpu_per_trainer,
cpu_per_trainer,
num_ps,
mem_per_ps,
cpu_per_ps,
)
```
Creates a Paddle Cloud job. Fails if the job name exists.
```python
get_cloud_job(
name
)
```
Gets a Paddle Cloud job.
```python
remote_session(
job
)
```
- *job*: the Paddle Cloud job.
#### Example
```Python
reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud
image = reader.column(0)
label = reader.column(1)
fc1 = paddle.op.fc(image, size=256, act="sigmoid")
fc2 = paddle.op.fc(fc1, size=10, act="softmax")
cost = paddle.op.cross_entropy(fc2, label)
opt = paddle.optimizer.sgd(cost)
job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1)
sess = paddle.remote_ession(job)
for i in range(1000):
sess.eval(opt)
sess.close()
```
doc/design/refactorization.md
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# Design Doc: Refactorization Overview
The goal of refactorizaiton include:
The goals of refactoring include:
1. Make it easy for external contributors to write new elementory computaiton operations.
1. Make the codebase clean and readable.
1. Introduce a new design of computation representation -- a computation graph of operators and variables.
1. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing.
1. Making it easy for external contributors to write new elementary computation operations.
1. Making the codebase clean and readable.
1. Designing a new computation representation -- a computation graph of operators and variables.
1. Implementing auto-scalability and auto fault recoverable distributed computing with the help of computation graphs.
## Computation Graphs
1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.
1. PaddlePaddle represents the computation, training and inference of Deep Learning models, by computation graphs.
1. Please dig into [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a solid example.
1. Please refer to [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a concrete example.
1. Users write Python programs to describe the graphs and run it (locally or remotely).
1. Users write Python programs to describe the graphs and run them (locally or remotely).
1. A graph is composed of *variables* and *operators*.
1. The description of graphs must be able to be serialized/deserialized, so it
1. The description of graphs must be serializable/deserializable, so that:
1. could to be sent to the cloud for distributed execution, and
1. be sent to clients for mobile or enterprise deployment.
1. It can be sent to the cloud for distributed execution, and
1. It can be sent to clients for mobile or enterprise deployment.
1. The Python program do
1. The Python program does two things
1. *compilation*: runs a Python program to generate a protobuf message representation of the graph and send it to
1. *Compilation* runs a Python program to generate a protobuf message representation of the graph and send it to
1. the C++ library `libpaddle.so` for local execution,
1. the master process of a distributed training job for training, or
1. the server process of a Kubernetes serving job for distributed serving.
1. *execution*: according to the protobuf message, constructs instances of class `Variable` and `OperatorBase`, and run them.
1. *Execution* executes the graph by constructing instances of class [`Variable`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h#L24) and [`OperatorBase`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L70), according to the protobuf message.
## Description and Realization
## Description and Realization of Computation Graph
At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph.
At compile time, the Python program generates a protobuf message representation of the graph, or a description of the graph.
At runtime, the C++ program realizes the graph and run it.
At runtime, the C++ program realizes the graph and runs it.
| | Representation (protobuf messages) | Realization (C++ class objects) |
|---|---|---|
......@@ -42,30 +42,31 @@ At runtime, the C++ program realizes the graph and run it.
|Operation|[OpDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L35)|[Operator](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L64)|
|Block|BlockDesc|Block|
The word *graph* is exchangable with *block* in this document. A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }.
The word *graph* is interchangeable with *block* in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses(`{` and `}`).
## Compilation and Execution
1. Run an applicaton Python program to describe the graph. In particular,
1. Run a Python program to describe the graph. In particular, the Python application program does the following:
1. create VarDesc to represent local/intermediate variables,
1. create operators and set attributes,
1. validate attribute values,
1. inference the type and the shape of variables,
1. plan for memory-reuse for variables,
1. generate backward and optimization part of the Graph.
1. possiblly split the graph for distributed training.
1. Create `VarDesc` to represent local/intermediate variables,
1. Create operators and set attributes,
1. Validate attribute values,
1. Infer the type and the shape of variables,
1. Plan memory-reuse for variables,
1. Generate the backward graph
1. Add optimization operators to the computation graph.
1. Optionally, split the graph for distributed training.
1. The invocation of `train` or `infer` in the application Python program:
1. The invocation of `train` or [`infer`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/inference.py#L108) methods in the Python program does the following:
1. create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block,
1. Create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block,
1. realize local variables defined in the BlockDesc message in the new scope,
1. a scope is similar to the stack frame in programming languages,
1. create an instance of class `Block`, in which,
1. Create an instance of class `Block`, in which,
1. realize operators in the BlockDesc message,
1. run the Block by calling
1. Run the Block by calling
1. `Block::Eval(vector<Variable>* targets)` for forward and backward computations, or
1. `Block::Eval(vector<Operator>* targets)` for optimization.
......@@ -76,14 +77,14 @@ The word *graph* is exchangable with *block* in this document. A graph represen
Compile Time -> IR -> Runtime
```
### Benefit
### Benefits of IR
- Optimization
```text
Compile Time -> IR -> Optimized IR -> Runtime
```
- Send automatically partitioned IR to different nodes.
- Automatic data parallel
- Automatically send partitioned IR to different nodes.
- Automatic Data Parallelism
```text
Compile Time
|-> Single GPU IR
......@@ -92,7 +93,7 @@ Compile Time -> IR -> Runtime
|-> Node-1 (runs trainer-IR-1)
|-> Node-2 (runs pserver-IR)
```
- Automatic model parallel (planned for future)
- Automatic Model Parallelism (planned for future)
---
......@@ -105,10 +106,10 @@ Compile Time -> IR -> Runtime
# Operator
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
* `Operator` is the fundamental building block as the user interface.
* Operator stores input/output variable name, and attributes.
* The `InferShape` interface is used to infer output variable shapes by its input shapes.
* Use `Run` to compute `input variables` to `output variables`.
* `Operator` is the fundamental building block of the user interface.
* Operator stores input/output variable names and attributes.
* The `InferShape` interface is used to infer the shape of the output variables based on the shapes of the input variables.
* Use `Run` to compute the `output` variables from the `input` variables.
---
......@@ -126,30 +127,29 @@ Compile Time -> IR -> Runtime
# Why separate Kernel and Operator
* Separate GPU and CPU code.
* Make Paddle can run without GPU.
* Make one operator (which is user interface) can contain many implementations.
* Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel.
* Make Paddle capable of running without GPU.
* Make one operator (which is a user interface) and create many implementations.
* For example, same multiplication op can have different implementations kernels such as FP16 kernel, FP32 kernel, MKL, eigen kernel.
---
# Libraries for Kernel development
* `Eigen::Tensor` contains basic math and element-wise functions.
* Note that `Eigen::Tensor` has broadcast implementation.
* Limit number of `tensor.device(dev) = ` in your code.
* `thrust::tranform` and `std::transform`.
* `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel.
* `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`.
* Limit the number of `tensor.device(dev) = ` in your code.
* `thrust::transform` and `std::transform`.
* `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels.
* `thrust`, in addition, supports more complex APIs, like `scan`, `reduce`, `reduce_by_key`.
* Hand-writing `GPUKernel` and `CPU` code
* Do not write `.h`. CPU Kernel should be in `.cc`. GPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
* Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
---
# Operator Register
# Operator Registration
## Why register is necessary?
## Why is registration necessary?
We need a method to build mappings between Op type names and Op classes.
## How to do the register?
Maintain a map, whose key is the type name and value is corresponding Op constructor.
## How is registration implemented?
Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
---
# The Registry Map
......@@ -169,7 +169,7 @@ Maintain a map, whose key is the type name and value is corresponding Op constru
# Related Concepts
### Op_Maker
It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
### Register Macros
```cpp
......@@ -177,34 +177,35 @@ 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.
### USE Macros
Make sure the registration process is executed and linked.
---
# Register Process
1. Write Op class, as well as its gradient Op class if there is.
2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes.
3. Invoke macro `REGISTER_OP`. The macro will
1. call maker class to complete `proto` and `checker`
2. with the completed `proto` and `checker`, build a new key-value pair in the `OpInfoMap`
# Registration Process
1. Write an Op class and its gradient Op class, if required.
2. Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator.
3. Invoke the macro `REGISTER_OP`. This macro will
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 `USE` macro in where the Op is used to make sure it is linked.
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
- Mapping from forwarding Op to backward Op
- Mapping from forward Op to backward Op
![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png)
---
# Backward Module (2/2)
### Build Backward Network
- **Input** graph of forwarding operators
- **Output** graph of backward operators
- **corner case in construction**
- shared variable => insert `Add` operator
- no gradient => insert `fill_zero_grad` operator
- recursive netOp => call `Backward` recursively
- **Input**: a graph of forward operators
- **Output**: a graph of backward operators
- **Corner cases in construction**
- Shared Variables => insert an `Add` operator to combine gradients
- No Gradient => insert a `fill_zero_grad` operator
- Recursive NetOp => call `Backward` recursively
- RNN Op => recursively call `Backward` on stepnet
- RNN Op => recursively call `Backward` on stepnet
......@@ -213,41 +214,41 @@ make sure the registration process is executed and linked.
* `Tensor` is an n-dimension array with type.
* Only dims and data pointers are stored in `Tensor`.
* All operators on `Tensor` is written in `Operator` or global functions.
* variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
* `Variable` is the inputs and outputs of an operator. Not just `Tensor`.
* step_scopes in RNN is a variable and not a tensor.
* `Scope` is where variables store at.
* map<string/*var name */, Variable>
* `Scope` has a hierarchical structure. The local scope can get variable from its parent scope.
* All operations on `Tensor` are written in `Operator` or global functions.
* Variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md)
* `Variable` instances are the inputs and the outputs of an operator, not just `Tensor`.
* `step_scopes` in RNN is a variable and not a tensor.
* `Scope` is where variables are stored.
* map<string `var name`, Variable>
* `Scope` has a hierarchical structure. The local scope can get variables from its parent scope.
---
# Block (in design)
## the difference with original RNNOp
- as an operator is more intuitive than `RNNOp`,
- offers new interface `Eval(targets)` to deduce the minimal block to `Run`,
- fits the compile-time/ runtime separation design.
- during the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc`
- when graph executes, a Block with `BlockDesc` passed in creates `Op` and `Var` then `Run`
## the difference between original RNNOp and Block
- As an operator is more intuitive than `RNNOp`,
- Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
- Fits the compile-time/ runtime separation design paradigm.
- During the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc`
- When graph executes, a Block with `BlockDesc` is passed. It then creates `Op` and `Var` instances and then invokes `Run`.
---
# Milestone
- take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
- model migration
- framework development gives **priority support** to model migration, for example,
- Take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
- Model migration
- Framework development gives **priority support** to model migration, for example,
- the MNIST demo needs a Python interface,
- the RNN models require the framework to support `LoDTensor`.
- determine some timelines,
- heavily-relied Ops need to be migrated first,
- different models can be migrated parallelly.
- improve the framework at the same time
- accept imperfection, concentrated on solving the specific problem at the right price.
- Determine some timelines,
- Frequently used Ops need to be migrated first,
- Different models can be migrated in parallel.
- Improve the framework at the same time
- Accept imperfection, concentrate on solving the specific problem at the right price.
---
# Control the migration quality
- compare the performance of migrated models with old ones.
- follow google C style
- build the automatic workflow of generating Python/C++ documentations
- the documentation of layers and ops should be written inside the code
- take the documentation quality into account when doing PR
- preview the documentations, read and improve them from users' perspective
- Compare the performance of migrated models with old ones.
- Follow the google C++ style guide.
- Build the automatic workflow of generating Python/C++ documentations.
- The documentation of layers and ops should be written inside the code.
- Take the documentation quality into account when submitting pull requests.
- Preview the documentations, read and improve them from a user's perspective.
doc/design/register_grad_op.md 0 → 100644
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# Design Doc: Gradient Operators Registration
## The Problem Posed
In our current operator registration mechanism, for each operator, the programmer should register a *gradient operator creator* function, which takes a C++ operator instance, and returns the corresponding gradient instance.
However, as we decided to separate the *compilation* and *execution* of DL models, we need to reshape the creator to take a protobuf `OpDesc` message, and returns a corresponding message.
More than that, the new registration mechanism need to support the fact that an operators' gradient computation might be a composition of operators.
## Current Implementation
OpInfos store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is
```cpp
struct OpInfo {
std::function<OperatorBase*(...)> creator_;
std::string grad_op_type_;
...
};
map<string, OpInfo> OpInfoMap;
OperatorBase* CreateGradientOperator(const OperatorBase& op) {
return OpInfoMap.at(op.Type()).creator_(...);
}
```
## Proposed Solution
The mapping relationship between an operator and its gradient operators is a function. The interface of that function is:
```cpp
// (OpDesc) --> vector<OpDesc>
std::function<std::vector<OpDescBind>(const OpDescBind&)>;
```
The function takes an `OpDescBind` of the forward operator and returns one or many gradient operator descriptions. `OpDescBind` is a C++ wrapper for protobuf message `OpDesc` to manipulate `OpDesc` fast.
The `GradOpDescMaker` will be registered in `OpInfo`, to replace `grad_op_type_` field. The `OpInfo` should be
```cpp
struct OpInfo {
std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)> grad_op_maker_;
...
};
```
The `grad_op_maker_ ` is `nullptr` if the operator does not have associated gradient operators.
We propose a base class called `GradOpDescMakerBase` to let operator developers generate `Gradient Operators` easily. The public interface of that class is
```cpp
class GradOpDescMakerBase {
public:
GradOpDescMakerBase(const OpDescBind& );
virtual std::vector<std::unique_ptr<OpDescBind>> operator()()const = 0;
};
```
We can convert `GradOpDescMakerBase` to `std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)>` by
```cpp
using GradOpMaker = ...;
std::function<std::vector<OpDescBind>(const OpDescBind&)> func;
func = [] (const OpDescBind& fwd_op) {
GradOpMaker maker(fwd_op);
return maker();
};
```
We can write many helper functions since the `GradOpDescMakerBase` is a class now. The basic helper functions get the variables of `Input`, `Output`, `InputGradient` and `OutputGradient` in the forwarding operator.
We should chagne register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`.
The user interface should be
```cpp
vector<OpDesc> MinusOpGradMaker(OpDesc) {...}
REGISTER_OPERATOR(minus, MinusOp, MinusOpProtoAndCheckerMaker, SumOpGradMaker);
// Developers can still manually implement gradient operator.
REGISTER_OPERATOR(minus_grad, MinusGradOp);
```
The interface of current `REGISTER_OP` macro could not be changed. In `REGISTER_OP`, it will invoke `REGISTER_OPERATOR` two times and generate GradOpDescMaker inside.
```cpp
REGISTER_OP(minus, MinusOp, MinusOpProtoAndCheckerMaker, minus_grad, MinusGradOp);
```
doc/design/selected_rows.md 0 → 100644
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# Design Doc: Selected Rows
`SelectedRows` is a kind of sparse tensor data type, which is designed to support `embedding` operators. The gradient of embedding table is a sparse tensor. Only a few rows are non-zero values in that tensor. It is straightforward to represent the sparse tensor by the following sparse tensor data structure:
```cpp
class SelectedRows {
private:
vector<int> rows_;
Tensor value_;
int height_;
};
```
The field `height_` shows the first dimension of `SelectedRows`. The `rows` are the indices of which rows of `SelectedRows` are non-zeros. The `value_` field is an N-dim tensor and shape is `[rows.size() /* NUM_ROWS */, ...]`, which supplies values for each row. The dimension of `SelectedRows` satisfies `[height_] + value_.shape[1:]`.
Suppose that a SelectedRows-typed variable `x` has many rows, but only two of them have values -- row 73 is `[1, 2]` and row 84 is `[3, 4]`, the `SelectedRows` representation would be:
```
x = SelectedRow {
rows = [73, 84],
value = [[1, 2], [3,4]]
}
```
## SelectedRows in Protobuf
`SelectedRows` is a kind of `Variable`. `VarDesc` in protobuf should describe the `SelectedRows` information. Only the tensor dimension of a `SelectedRows` will be described in compile-time since the `rows_` and `value_` are related to training data.
So we use `TensorDesc` to unify `data_type` and `dims`. A LodTensorDesc contains a `TensorDesc` and `lod_level`. The description of `SelectedRows` is a Tensor description.
```proto
message TensorDesc {
required DataType data_type = 1;
repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
}
message LodTensorDesc {
required TensorDesc tensor = 1;
optional int lod_level = 2;
}
message VarDesc {
required string name = 1;
enum VarType {
LOD_TENSOR = 0;
SELECTED_ROWS = 1;
}
required VarType type = 2;
optional LodTensorDesc lod_desc = 3;
optional TensorDesc selected_rows_desc = 4;
optional bool persistable = 5 [ default = false ];
}
```
## InferShape for Selected Rows
Just like `LoD` information, `InferShape` method will inference output tensor type as well. The operator should decide whether its output is a `SelectedRows` or `Dense` tensor.
For example, the gradient operator of `TableLookup` will always generate `SelectedRows`. Its `InferShape` method should be like following
```cpp
void TableLookupGrad::InferShape(context) {
...
context.SetDataType("Embedding.Grad", kSelectedRows);
}
```
## Sparse Operators
There are several operators should be written to support `SelectedRows`. They are:
1. Operators which generates `SelectedRows` gradient. e.g. Gradient of `TableLookupOp`.
2. Optimize operators which support `SelectedRows` gradient. e.g. `SGD` or `AdaGrad` for `SelectedRows`. However, there should be only one `SGD` operator. `OpWithKernel::Run` should select a suitable kernel for both `dense` tensor or `SelectedRows`.
doc/design/tensor_array.md
浏览文件 @ 240adef1
# Design for TensorArray
This design doc presents the necessity of a new C++ class `TensorArray`.
In addition to the very simple C++ implementation
```c++
class TensorArray {
public:
explicit TensorArray(const LoDTensor&);
explicit TensorArray(size_t size);
private:
vector<LoDTensor> values_;
};
```
We also need to expose it to PaddlePaddle's Python API,
because users would want to use it with our very flexible operators `WhileLoop`.
An example for a RNN based on dynamic operators is
```python
input = pd.data(...)
num_steps = Var(12)
TensorArray states(size=num_steps)
TensorArray step_inputs(unstack_from=input)
TensorArray step_outputs(size=num_steps)
W = Tensor(...)
U = Tensor(...)
default_state = some_op()
step = Var(1)
wloop = paddle.create_whileloop(loop_vars=[step])
with wloop.frame():
wloop.break_if(pd.equal(step, num_steps)
pre_state = states.read(step-1, default_state)
step_input = step_inputs.read(step)
state = pd.sigmoid(pd.matmul(U, pre_state) + pd.matmul(W, step_input))
states.write(step, state)
step_outputs.write(step, state) # output state
step.update(state+1)
output = step_outputs.stack()
```
## Background
Steps are one of the core concepts of RNN. In each time step of RNN, there should be several input segments, states, and output segments; all these components act like arrays, for example, call `states[step_id]` will get the state in `step_id`th time step.
An RNN can be implemented with the following pseudocode
```c++
Array states;
Array input_segments;
Array output_segments;
Parameter W, U;
step = 1
seq_len = 12
while_loop {
if (step == seq_len) break;
states[step] = sigmoid(W * states[step-1] + U * input_segments[step]);
output_segments[step] = states[step] // take state as output
step++;
}
```
According to the [RNN roadmap](https://github.com/PaddlePaddle/Paddle/issues/4561), there are several different RNNs that PaddlePaddle will eventually support.
Currently, the basic RNN implementation supported by PaddlePaddle is the `recurrent_op` which takes tensors as input and splits them into `input_segments`.
Since a tensor cannot store variable-length sequences directly, PaddlePaddle implements the tensor with level of details (`LoDTensor` for short).
Segmenting the `LoDTensor` is much more complicated than splitting a tensor, that makes it necessary to refactor the `recurrent_op` with `LoDTensor` segmenting support.
As the next step in RNN support, `dynamic_recurrent_op` should be introduced to handle inputs with variable-length sequences.
The implementation is similar to `recurrent_op`.
The key difference is the way **the original input `LoDTensors` and outupts are split to get the `input_segments` and the `output_segments`.**
Though it can't be built over `recurrent_op` or `dynamic_recurrent_op` directly,
the logic behind splitting a tensor or a LoD tensor into `input_segments` remains the same.
## Why `TensorArray`
The logic behind splitting the inputs to segments, states and outputs is similar and can be shared in a seperate module.
The array of `states`, `input_segments` and `output_segments` would be exposed to users when writing a dynamic RNN model similar to the above pseudo codes.
So there should be an array-like container, which can store the segments of a tensor or LoD tensor.
**This container can store an array of tensors and provides several methods to split a tensor or a LoD tensor** .
This is where the notion of `TensorArray` comes from.
## Introduce TensorArray to uniform all the three RNNs
TensorArray as a new concept is borrowed from TensorFlow,
it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`.
This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers,
such as `RecurrentGradientMachine`.
such as `recurrent_op`, `RecurrentGradientMachine`.
In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401),
`TensorArray` is used to segment inputs and store states in all time steps.
By providing some methods similar to a C++ array,
the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.
## Dynamic-Related Methods
Some basic methods should be proposed as follows:
### stack()
Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`.
### unstack(axis=0)
Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
### concat()
Return the values in the `TensorArray` as a concatenated Tensor.
### write(index, value, data_shared=true)
Write value into index of the TensorArray.
### read(index)
Read the value at location `index` in the `TensorArray`.
### size()
Return the number of values.
the definition of some state-based dynamic models such as RNN can be more natural and highly flexible.
## Dynamic-operations on TensorArray
`TensorArray` will be used directly when defining dynamic models, so some operators listed below should be implemented
```python
# several helper operators for TensorArray
def tensor_array_stack(ta, tensor):
'''
get a tensor array `ta`, return a packed `tensor`.
'''
pass
def tensor_array_unstack(tensor, ta):
'''
get a `tensor`, unstack it and get a tensor array `ta`.
'''
pass
def tensor_array_write(ta, index, tensor, data_shared):
'''
get a `tensor` and a scalar tensor `index`, write `tensor` into index-th
value of the tensor array `ta`.
`data_shared` is an attribute that specifies whether to copy or reference the tensors.
'''
pass
def tensor_array_read(ta, index, tensor):
'''
get a tensor array `ta`, a scalar tensor `index`, read the index-th value of
`ta` and return as the `tensor`.
'''
pass
def tensor_array_size(ta, tensor):
'''
get a tensor array `ta`, return the size of `ta` and return as the scalar `tensor`.
'''
pass
```
It is trivial for users to use so many low-level operators, so some helper methods should be proposed in python wrapper to make `TensorArray` easier to use,
for example
```python
class TensorArray:
def __init__(self, name):
self.name = name
self.desc = TensorArrayDesc()
def stack(self, name=None):
'''
Pack the values in a `TensorArray` into a tensor with rank one higher
than each tensor in `values`.
`stack` can be used to split tensor into time steps for RNN or whileloop.
@name: str
the name of the variable to output.
'''
tensor = NewVar(name)
tensor_array_stack(self.name, tensor)
return tensor
def unstack(self, input):
'''
Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
`unstack` can be used to concatenate all the time steps for RNN or whileloop.
@input: str
the name of input tensor
'''
tensor_array_unstack(tensor, self.name)
def write(self, index, value, data_shared=True):
'''
Write value into index of the TensorArray.
If `data_shared` is set to True, than the index-th value in TensorArray will
be shared with the tensor passed in.
@index: str
name of a scalar tensor
@value: str
name of a tensor
@data_shared: bool
'''
tensor_array_write(self.name, index, value, data_shared)
def read(self, index, output):
'''
Read the value at location `index` in the `TensorArray`.
@index: str
name of a scalar tensor
@output:
name of a output variable
'''
tensor_array_read(self.name, index, output)
def size(self, output):
'''
Return the number of values.
@output: str
name of a scalar tensor
'''
tensor_array_size(self.name, output)
```
## LoDTensor-related Supports
The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input,
because each step of RNN could only take a tensor-represented batch of data as input,
The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes varience-length sequences as input, and output sequences too.
Since each step of RNN can only take a tensor-represented batch of data as input,
some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches.
Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`.
Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`,
these two operations are similar to `stack` and `unstack` except that they operate on variable-length sequences formated as a LoD tensor rather than a tensor.
Some definitions are like
```python
def unpack(level):
'''
Split LodTensor in some `level` and generate batches, if set `sort_by_length`,
will sort by length.
With these two methods, a variant-sentence-RNN can be implemented like
Returns:
- a new `TensorArray`, whose values are LodTensors and represents batches
of data.
- an int32 Tensor, which stores the map from the new batch's indices to
original LoDTensor
'''
pass
def pack(level, indices_map):
'''
Recover the original LoD-arranged LoDTensor with the values in a `TensorArray`
and `level` and `indices_map`.
'''
pass
```
With these two methods, a varience-length sentence supported RNN can be implemented like
```c++
// input is the varient-length data
......@@ -58,16 +269,3 @@ LoDTensor rnn_output = ta.pack(ta, indice_map);
```
the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`,
the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend.
some details are as follows.
### unpack(level, sort_by_length)
Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length.
Returns:
- a new `TensorArray`, whose values are LodTensors and represents batches of data.
- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor
### pack(level, indices_map)
Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`.
doc/design/test.dot 0 → 100644
浏览文件 @ 240adef1
digraph Test {
z -> generator -> G_img;
G_img -> discriminator -> D_f -> d_loss_f;
label0 -> d_loss_f -> d_loss;
img -> discriminator -> D_t -> d_loss_t;
label1 -> d_loss_t -> d_loss;
d_loss -> d_loss_t[color=red, style=dashed];
d_loss -> d_loss_f[color=red, style=dashed];
d_loss_t -> D_t[color=red, style=dashed];
d_loss_f -> D_f[color=red, style=dashed];
D_t -> discriminator[color=red, style=dashed];
D_f -> discriminator[color=red, style=dashed];
D_f -> g_loss;
label2 -> g_loss;
g_loss -> D_f[color=green, style=dashed];
D_f -> discriminator[color=green, style=dashed];
discriminator -> G_img[color=green, style=dashed];
G_img -> generator[color=green, style=dashed];
discriminator [color=red, shape=box];
generator [color=green, shape=box];
z [shape=diamond];
img [shape=diamond];
label0 [shape=diamond];
label1 [shape=diamond];
label2 [shape=diamond];
d_loss [color=red];
g_loss [color=green];
}
doc/howto/dev/new_op_cn.md
浏览文件 @ 240adef1
......@@ -206,7 +206,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
- `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`。
- `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulGradKernel`类。
- 在 `.cu`文件中注册GPU Kernel。
......@@ -285,41 +285,27 @@ class TestMulGradOp(GradientChecker):
'Y': np.random.random((84, 100)).astype("float32")
}
def test_cpu_gpu_compare(self):
self.compare_grad(self.op, self.inputs)
def test_normal(self):
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(
self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_ignore_x(self):
def test_check_grad_ingore_x(self):
self.check_grad(
self.op,
self.inputs, ["Y"],
"Out",
max_relative_error=0.5,
no_grad_set={"X"})
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_ignore_y(self):
def test_check_grad_ingore_y(self):
self.check_grad(
self.op,
self.inputs, ["X"],
"Out",
max_relative_error=0.5,
no_grad_set={"Y"})
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
下面解释代码中一些关键的地方:
- 调用`create_op("mul")`创建反向Op对应的前向Op。
- 调用`compare_grad`函数对比CPU、GPU计算结果。
- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
- 第一个参数`self.op` : 前向Op。
- 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
- 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。
- `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
- 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。
- 第三个参数`max_relative_error`：指定检测梯度时能容忍的最大错误值。
- `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。
### 编译和执行单元测试
......
doc/howto/dev/new_op_en.md
浏览文件 @ 240adef1
......@@ -205,7 +205,7 @@ The definition of its corresponding backward operator, if applicable, is similar
- `REGISTER_OP` registers the `ops::MulOp` class, type named `mul`, its type `ProtoMaker` is `ops::MulOpMaker`, registering `ops::MulOpGrad` as `mul_grad`.
- `REGISTER_OP_WITHOUT_GRADIENT` registers an operator without gradient.
- `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulKernel`.
- `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulGradKernel`.
- Registering GPU Kernel in `.cu` files
......@@ -293,41 +293,27 @@ class TestMulGradOp(GradientChecker):
'Y': np.random.random((84, 100)).astype("float32")
}
def test_cpu_gpu_compare(self):
self.compare_grad(self.op, self.inputs)
def test_normal(self):
def test_check_grad_normal(self):
# mul op will enlarge the relative error
self.check_grad(
self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
def test_ignore_x(self):
def test_check_grad_ingore_x(self):
self.check_grad(
self.op,
self.inputs, ["Y"],
"Out",
max_relative_error=0.5,
no_grad_set={"X"})
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
def test_ignore_y(self):
def test_check_grad_ingore_y(self):
self.check_grad(
self.op,
self.inputs, ["X"],
"Out",
max_relative_error=0.5,
no_grad_set={"Y"})
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
```
Some key points in the code above include:
- `create_op("mul")` creates the backward operator's corresponding forward operator.
- `compare_grad` compares results between utilizing the CPU and the GPU.
- `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods.
- The first variable `self.op` denotes the forward operator.
- The second variable `self.inputs` denotes the input dictionary, which has its key value identical to its `ProtoMaker` definitions.
- The third variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
- The fourth variable `"Out"` points to the network's final output target `Out`.
- `test_ignore_x` and `test_ignore_y`branches test the cases where there is only one scaling input.
- The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested.
- The second variable `"Out"` points to the network's final output target `Out`.
- The third variable `max_relative_error` points to the maximum relative tolerance error during scaling tests.
- `test_check_grad_ingore_x` and `test_check_grad_ingore_y`branches test the cases where there is only one scaling input.
### Compiling and Running
......
paddle/CMakeLists.txt
浏览文件 @ 240adef1
add_subdirectory(cuda)
add_subdirectory(function)
add_subdirectory(utils)
add_subdirectory(testing)
add_subdirectory(math)
add_subdirectory(parameter)
add_subdirectory(gserver)
add_subdirectory(pserver)
add_subdirectory(trainer)
add_subdirectory(scripts)
add_subdirectory(string)
if(Boost_FOUND)
add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
endif()
add_subdirectory(parameter)
add_subdirectory(testing)
if(WITH_C_API)
if(MOBILE_INFERENCE)
add_subdirectory(capi)
endif()
else()
add_subdirectory(pserver)
add_subdirectory(trainer)
add_subdirectory(string)
add_subdirectory(scripts)
if(WITH_C_API)
add_subdirectory(capi)
endif()
if(Boost_FOUND)
add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
endif()
if(WITH_SWIG_PY)
add_subdirectory(api)
if(WITH_SWIG_PY)
add_subdirectory(api)
endif()
endif()
paddle/api/Util.cpp
浏览文件 @ 240adef1
......@@ -47,7 +47,7 @@ bool isUsingGpu() { return FLAGS_use_gpu; }
void setUseGpu(bool useGpu) { FLAGS_use_gpu = useGpu; }
bool isGpuVersion() {
#ifdef PADDLE_ONLY_CPU
#ifndef PADDLE_WITH_CUDA
return false;
#else
return true;
......
paddle/capi/CMakeLists.txt
浏览文件 @ 240adef1
......@@ -37,9 +37,7 @@ set(PADDLE_CAPI_INFER_LIBS
paddle_cuda
paddle_function
paddle_gserver
paddle_proto
paddle_pserver
paddle_network)
paddle_proto)
cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_CAPI_INFER_LIBS})
......
paddle/capi/Matrix.cpp
浏览文件 @ 240adef1
......@@ -46,7 +46,7 @@ paddle_error paddle_matrix_set_row(paddle_matrix mat,
if (rowID >= ptr->mat->getHeight()) return kPD_OUT_OF_RANGE;
paddle::real* buf = ptr->mat->getRowBuf(rowID);
size_t width = ptr->mat->getWidth();
#ifndef PADDLE_ONLY_CPU
#ifdef PADDLE_WITH_CUDA
hl_memcpy(buf, rowArray, sizeof(paddle::real) * width);
#else
std::copy(rowArray, rowArray + width, buf);
......
paddle/capi/tests/CMakeLists.txt
浏览文件 @ 240adef1
......@@ -4,11 +4,12 @@ add_unittest(capi_test_mats test_Vector.cpp
target_include_directories(capi_test_mats PUBLIC ${PADDLE_CAPI_INC_PATH})
target_link_libraries(capi_test_mats paddle_capi)
add_unittest_without_exec(capi_test_gradientMachine test_GradientMachine.cpp)
target_include_directories(capi_test_gradientMachine PUBLIC
${PADDLE_CAPI_INC_PATH})
target_link_libraries(capi_test_gradientMachine paddle_capi)
add_test(NAME capi_test_gradientMachine
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests)
if(NOT MOBILE_INFERENCE)
add_unittest_without_exec(capi_test_gradientMachine test_GradientMachine.cpp)
target_include_directories(capi_test_gradientMachine PUBLIC
${PADDLE_CAPI_INC_PATH})
target_link_libraries(capi_test_gradientMachine paddle_capi)
add_test(NAME capi_test_gradientMachine
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests)
endif()
paddle/framework/CMakeLists.txt
浏览文件 @ 240adef1
......@@ -19,17 +19,15 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope)
proto_library(framework_proto SRCS framework.proto)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim)
cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
......@@ -43,3 +41,13 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward ${GLOB_OP_LIB})
if(WITH_GPU)
nv_test(executor_test SRCS executor_test.cc DEPS executor)
else()
cc_test(executor_test SRCS executor_test.cc DEPS executor)
endif()
cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)
paddle/framework/attribute.h
浏览文件 @ 240adef1
......@@ -21,20 +21,12 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/type_defs.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/variant.h"
namespace paddle {
namespace framework {
// The order should be as same as framework.proto
typedef boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc*>
Attribute;
typedef std::unordered_map<std::string, Attribute> AttributeMap;
ProgramDesc& GetProgramDesc();
template <typename T>
......
paddle/framework/backward.cc
浏览文件 @ 240adef1
......@@ -13,10 +13,13 @@
limitations under the License. */
#include "paddle/framework/backward.h"
#include "paddle/operators/net_op.h"
#include <deque>
#include <list>
#include <memory>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
......@@ -24,6 +27,35 @@
namespace paddle {
namespace framework {
static inline std::unique_ptr<OperatorBase> CreateGradOp(
const OperatorBase& op) {
OpDescBind op_desc;
op_desc.SetInputMap(op.Inputs());
op_desc.SetOutputMap(op.Outputs());
op_desc.SetType(op.Type());
op_desc.SetAttrMap(op.Attrs());
auto& info = OpInfoMap::Instance().Get(op.Type());
auto grad_descs = info.GradOpMaker()(op_desc);
std::vector<std::unique_ptr<OperatorBase>> grad_ops;
grad_ops.reserve(grad_descs.size());
std::transform(grad_descs.begin(), grad_descs.end(),
std::back_inserter(grad_ops),
[](const std::unique_ptr<OpDescBind>& grad_desc) {
return OpRegistry::CreateOp(*grad_desc);
});
PADDLE_ENFORCE(!grad_ops.empty());
if (grad_ops.size() == 1) {
return std::move(grad_ops[0]);
} else {
auto net_op = new operators::NetOp();
for (auto& grad_op : grad_ops) {
net_op->AppendOp(std::move(grad_op));
}
net_op->CompleteAddOp();
return std::unique_ptr<OperatorBase>(net_op);
}
}
template <typename Map, typename T>
static void ForEachVarName(const Map& names, T callback) {
for (auto& name : names) {
......@@ -140,13 +172,14 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
std::to_string(i));
net->ops_[op_offset]->Rename(name, dup_outputs.back());
}
// collect all the offset to append `add` op for each alias
// collect all the offset for each alias,
// insert a sum operator to add all aliases to output
insert_position.push_back(
{dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}},
{dup_op.back(), OpRegistry::CreateOp("sum", {{"X", dup_outputs}},
{{"Out", {name}}}, {})});
}
// make sure the inserted `add` ops follow the BFS order.
// make sure the inserted `sum` ops follow the BFS order.
insert_position.sort(
[](const Pos& l, const Pos& r) { return l.first > r.first; });
......@@ -154,7 +187,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
net->InsertOp(pos.first + 1, std::move(pos.second));
}
} else {
std::unique_ptr<OperatorBase> grad_op(OpRegistry::CreateGradOp(forwardOp));
std::unique_ptr<OperatorBase> grad_op(CreateGradOp(forwardOp));
ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op](
const std::string& grad_input) {
......@@ -182,7 +215,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
// process recurrent gradient op as a special operator.
if (forwardOp.Type() == "recurrent") {
// NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or
// NOTE clean up cycle call somewhere (RNN's stepnet constains itself),
// or
// this will result in infinite loop.
const auto& rnnop =
*static_cast<const operators::RecurrentOp*>(&forwardOp);
......@@ -222,5 +256,145 @@ std::unique_ptr<OperatorBase> Backward(
return BackwardRecursive(forwardOp, no_grad_names, uid);
}
// ==================================== //
static bool AllGradInSet(const std::vector<std::string>& names,
const std::unordered_set<std::string>& set) {
for (const std::string& name : names) {
if (!set.count(GradVarName(name))) {
return false;
}
}
return true;
}
std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
const std::unique_ptr<OpDescBind>& op_desc,
std::unordered_set<std::string>& no_grad_vars) {
std::vector<std::unique_ptr<OpDescBind>> grad_op_descs;
// All input gradients of forwarding operator do not need to calculat.
const std::vector<std::string>& inputs = op_desc->InputArgumentNames();
if (AllGradInSet(inputs, no_grad_vars)) {
return grad_op_descs; // empty vector
}
// All output gradients of forwarding operator do not need to calculate.
const std::vector<std::string>& outputs = op_desc->OutputArgumentNames();
if (AllGradInSet(outputs, no_grad_vars)) {
for (const std::string& name : inputs) {
no_grad_vars.insert(GradVarName(name));
}
return grad_op_descs; // empty vector
}
grad_op_descs = OpRegistry::CreateGradOpDescs(op_desc.get());
std::list<std::unique_ptr<OpDescBind>> pending_fill_zeros_ops;
for (auto& desc : grad_op_descs) {
for (const std::string& in_name : desc->InputArgumentNames()) {
if (no_grad_vars.count(in_name)) {
std::string prefix = in_name.substr(
0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
std::string new_name = prefix + kZeroVarSuffix;
desc->Rename(in_name, new_name);
std::unique_ptr<OpDescBind> fill_zeros_op(new OpDescBind(
"fill_zeros_like", {{"X", {prefix}}}, {{"Y", {new_name}}}, {}));
pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
}
}
for (const std::string& out_name : desc->OutputArgumentNames()) {
if (no_grad_vars.count(out_name)) {
desc->Rename(out_name, kEmptyVarName);
}
}
}
for (auto& p : pending_fill_zeros_ops) {
grad_op_descs.insert(grad_op_descs.begin(), std::move(p));
}
return grad_op_descs;
}
std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>& no_grad_vars) {
BlockDescBind* cur_block = program_desc.Block(block_idx);
std::deque<std::unique_ptr<OpDescBind>>& op_descs = cur_block->ops_;
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
size_t grad_desc_idx = 0;
std::vector<std::unique_ptr<OpDescBind>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
std::vector<std::unique_ptr<OpDescBind>> op_grads =
MakeOpGrad(*it, no_grad_vars);
if ((*it)->Type() == "recurrent") {
PADDLE_ENFORCE_EQ(
op_grads.size(), size_t(1),
"rnn_op's gradient process should contain only one op.");
int step_block_idx = (*it)->GetBlockAttr("stop_block");
auto backward_block_op_descs =
MakeBlockBackward(program_desc, step_block_idx, no_grad_vars);
BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block);
for (auto& ptr : backward_block_op_descs) {
backward_block->ops_.push_back(std::move(ptr));
}
op_grads[0]->SetBlockAttr("step_block", *backward_block);
}
for (const auto& desc : op_grads) {
for (const std::string& out_name : desc->OutputArgumentNames()) {
dup_out_ops[out_name].emplace_back(grad_desc_idx);
}
++grad_desc_idx;
}
std::transform(
op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs),
[](std::unique_ptr<OpDescBind>& ptr) { return std::move(ptr); });
}
// Check whether some variables are written more than once
std::list<std::pair<size_t, std::unique_ptr<OpDescBind>>> pending_sum_ops;
for (const auto& dup : dup_out_ops) {
const std::string& out_name = dup.first;
const std::vector<size_t> dup_op = dup.second;
if (out_name != kEmptyVarName && dup_op.size() > 1) {
std::vector<std::string> sum_op_inputs;
for (size_t i = 0; i < dup_op.size(); ++i) {
std::string new_name = out_name + "@RENAME@" + std::to_string(i);
backward_descs[dup_op[i]]->Rename(out_name, new_name);
sum_op_inputs.emplace_back(new_name);
}
std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
"sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
}
}
pending_sum_ops.sort(
[](const std::pair<size_t, std::unique_ptr<OpDescBind>>& a,
const std::pair<size_t, std::unique_ptr<OpDescBind>>& b) {
return a.first > b.first;
});
for (auto& p : pending_sum_ops) {
backward_descs.insert(backward_descs.begin() + p.first + 1,
std::move(p.second));
}
return backward_descs;
}
void AppendBackward(ProgramDescBind& program_desc,
const std::unordered_set<std::string>& no_grad_vars) {
std::unordered_set<std::string> no_grad_var_names;
no_grad_var_names.reserve(no_grad_vars.size() + 1);
no_grad_var_names.insert(std::string(kEmptyVarName) + kGradVarSuffix);
for (auto& name : no_grad_vars) {
no_grad_var_names.insert(GradVarName(name));
}
const int root_block_idx = 0;
auto backward_op_descs =
MakeBlockBackward(program_desc, root_block_idx, no_grad_var_names);
auto& forw_op_descs = program_desc.Block(root_block_idx)->ops_;
for (auto& ptr : backward_op_descs) {
forw_op_descs.push_back(std::move(ptr));
}
}
} // namespace framework
} // namespace paddle
paddle/framework/backward.h
浏览文件 @ 240adef1
......@@ -13,8 +13,11 @@
limitations under the License. */
#pragma once
#include <unordered_set>
#include "operator.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/program_desc.h"
namespace paddle {
namespace framework {
......@@ -23,5 +26,9 @@ namespace framework {
extern std::unique_ptr<OperatorBase> Backward(
const OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars);
void AppendBackward(ProgramDescBind& program_desc,
const std::unordered_set<std::string>& no_grad_vars);
} // namespace framework
} // namespace paddle
paddle/framework/backward_test.cc
浏览文件 @ 240adef1
......@@ -15,30 +15,42 @@
#include "paddle/framework/backward.h"
#include <gtest/gtest.h>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace framework {
using OperatorBase = framework::OperatorBase;
using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
using OpProto = framework::OpProto;
using OpAttrChecker = framework::OpAttrChecker;
using Scope = framework::Scope;
using DeviceContext = platform::DeviceContext;
class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input X of Add").NotInGradient();
AddInput("b", "Bias of Add").NotInGradient();
AddOutput("Out", "Out of Add").NotInGradient();
AddInput("X", "Input X of Add");
AddInput("b", "Bias of Add");
AddOutput("Out", "Out of Add");
AddComment("Add Op");
}
};
class RowWiseAddGradMaker : public SingleGradOpDescMaker {
public:
using SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<OpDescBind> Apply() const override {
auto grad_op = new OpDescBind();
grad_op->SetInput(GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(GradVarName("X"), InputGrad("X"));
grad_op->SetOutput(GradVarName("b"), InputGrad("b"));
grad_op->SetType("rowwise_add_grad");
return std::unique_ptr<OpDescBind>(grad_op);
}
};
class MulOpMaker : public OpProtoAndCheckerMaker {
public:
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
......@@ -46,6 +58,8 @@ class MulOpMaker : public OpProtoAndCheckerMaker {
AddInput("X", "A");
AddInput("Y", "B");
AddOutput("Out", "Out");
AddAttr<int>("x_num_col_dims", "").SetDefault(1).EqualGreaterThan(1);
AddAttr<int>("y_num_col_dims", "").SetDefault(1).EqualGreaterThan(1);
AddComment("Mul");
}
};
......@@ -133,42 +147,46 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker {
}
};
class AddOpMaker : public OpProtoAndCheckerMaker {
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input tensors of sum operator.").AsDuplicable();
AddOutput("Out", "the output tensor of sum operator.");
AddComment("");
}
};
class MultInOutOpMaker : public OpProtoAndCheckerMaker {
public:
AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
MultInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x").AsDuplicable();
AddOutput("Out", "out");
AddInput("X", "x");
AddInput("H", "h");
AddOutput("Y", "y");
AddOutput("Z", "z");
AddComment("");
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
namespace ops = paddle::operators;
using EnforceNotMet = paddle::platform::EnforceNotMet;
REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
f::NOP);
REGISTER_OPERATOR(rowwise_add, f::NOP, f::RowWiseAddOpMaker,
f::RowWiseAddGradMaker);
REGISTER_OPERATOR(rowwise_add_grad, f::NOP);
REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
REGISTER_OP(sum, f::NOP, f::SumOpMaker, sum_grad, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
f::NOP);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(1UL, gop->Inputs().size());
ASSERT_EQ("rowwise_add_grad", gop->Type());
ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
}
REGISTER_OP(mult_in_out, f::NOP, f::MultInOutOpMaker, mult_in_out_grad, f::NOP);
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp(
......@@ -283,18 +301,7 @@ TEST(Backward, net_shared_weight) {
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
ASSERT_EQ(3UL, bwd_net->ops_.size());
ASSERT_EQ("add", bwd_net->ops_[2]->Type());
}
TEST(Backward, op_register_grad_not_for_network) {
auto fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_out"}},
{"add_result", {"add_out"}},
{"Out", {"out1"}}},
{{"temporary_index", std::vector<int>{0, 1}}});
ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet);
ASSERT_EQ("sum", bwd_net->ops_[2]->Type());
}
TEST(Backward, op_all_input_are_not_need) {
......@@ -399,3 +406,315 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL);
}
// =================================== //
f::ProgramDesc *GetNewProgramDesc() {
auto *program_desc = new f::ProgramDesc();
auto *root_block = program_desc->add_blocks();
root_block->set_idx(0);
root_block->set_parent_idx(-1);
return program_desc;
}
TEST(Backward, simple_single_op) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("rowwise_add");
op->SetInput("X", {"x"});
op->SetInput("b", {"b"});
op->SetOutput("Out", {"out"});
AppendBackward(program, {});
ASSERT_EQ(block->AllOps().size(), 2UL);
f::OpDescBind *grad_op = block->AllOps()[1];
EXPECT_EQ(grad_op->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op->InputNames().size(), 1UL);
ASSERT_EQ(grad_op->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x")}));
EXPECT_EQ(grad_op->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b")}));
}
TEST(Backward, default_attribute) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("mul");
op->SetInput("X", {"x"});
op->SetInput("Y", {"y"});
op->SetOutput("Out", {"out"});
AppendBackward(program, {});
ASSERT_EQ(block->AllOps().size(), 2UL);
EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(op->GetAttr("y_num_col_dims")), 1);
f::OpDescBind *grad_op = block->AllOps()[1];
ASSERT_EQ(grad_op->Type(), "mul_grad");
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("x_num_col_dims")), 1);
EXPECT_EQ(boost::get<int>(grad_op->GetAttr("y_num_col_dims")), 1);
}
TEST(Backward, simple_mult_op) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"out1"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out2"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
AppendBackward(program, {});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *grad_op1 = block->AllOps()[5];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op2 = block->AllOps()[4];
EXPECT_EQ(grad_op2->Type(), "mul_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 4UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op2->Input("X"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op2->Input("Y"), std::vector<std::string>({"y2"}));
EXPECT_EQ(grad_op2->Input("Out"), std::vector<std::string>({"out2"}));
EXPECT_EQ(grad_op2->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out2")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *grad_op3 = block->AllOps()[3];
EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out3")}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out2")}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b3")}));
}
TEST(Backward, intermedia_var_no_grad) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"x2"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out2"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
f::OpDescBind *op4 = block->AppendOp();
op4->SetType("mul");
op4->SetInput("X", {"out1"});
op4->SetInput("Y", {"out3"});
op4->SetOutput("Out", {"out4"});
AppendBackward(program, {"out3"});
ASSERT_EQ(block->AllOps().size(), 6UL);
f::OpDescBind *grad_op1 = block->AllOps()[5];
EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
f::OpDescBind *grad_op4 = block->AllOps()[4];
EXPECT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op4->Input("X"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op4->Input("Y"), std::vector<std::string>({"out3"}));
EXPECT_EQ(grad_op4->Input("Out"), std::vector<std::string>({"out4"}));
EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out4")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
std::vector<std::string>({f::kEmptyVarName}));
}
TEST(Backward, var_no_grad) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("mult_in_out");
op1->SetInput("X", {"x1"});
op1->SetInput("H", {"h1"});
op1->SetOutput("Y", {"y1"});
op1->SetOutput("Z", {"z1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mult_in_out");
op2->SetInput("X", {"y1"});
op2->SetInput("H", {"z1"});
op2->SetOutput("Y", {"y2"});
op2->SetOutput("Z", {"z2"});
AppendBackward(program, {"z1"});
ASSERT_EQ(block->AllOps().size(), 5UL);
f::OpDescBind *grad_op2 = block->AllOps()[2];
ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op2->InputNames().size(), 6UL);
ASSERT_EQ(grad_op2->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op2->Input("X"), std::vector<std::string>({"y1"}));
EXPECT_EQ(grad_op2->Input("H"), std::vector<std::string>({"z1"}));
EXPECT_EQ(grad_op2->Input("Y"), std::vector<std::string>({"y2"}));
EXPECT_EQ(grad_op2->Input("Z"), std::vector<std::string>({"z2"}));
EXPECT_EQ(grad_op2->Input(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
EXPECT_EQ(grad_op2->Input(f::GradVarName("Z")),
std::vector<std::string>({f::GradVarName("z2")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("y1")}));
EXPECT_EQ(grad_op2->Output(f::GradVarName("H")),
std::vector<std::string>({f::kEmptyVarName}));
f::OpDescBind *fill_zero_op = block->AllOps()[3];
ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like");
ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL);
ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL);
EXPECT_EQ(fill_zero_op->Input("X"), std::vector<std::string>({"z1"}));
EXPECT_EQ(fill_zero_op->Output("Y"),
std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
f::OpDescBind *grad_op1 = block->AllOps()[4];
ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 6UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input("X"), std::vector<std::string>({"x1"}));
EXPECT_EQ(grad_op1->Input("H"), std::vector<std::string>({"h1"}));
EXPECT_EQ(grad_op1->Input("Y"), std::vector<std::string>({"y1"}));
EXPECT_EQ(grad_op1->Input("Z"), std::vector<std::string>({"z1"}));
EXPECT_EQ(grad_op1->Input(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y1")}));
EXPECT_EQ(grad_op1->Input(f::GradVarName("Z")),
std::vector<std::string>({std::string("z1") + f::kZeroVarSuffix}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("H")),
std::vector<std::string>({f::GradVarName("h1")}));
}
TEST(Backward, shared_var) {
f::ProgramDesc *program_desc = GetNewProgramDesc();
f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
f::BlockDescBind *block = program.Block(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
op1->SetInput("b", {"b1"});
op1->SetOutput("Out", {"out1"});
f::OpDescBind *op2 = block->AppendOp();
op2->SetType("mul");
op2->SetInput("X", {"out1"});
op2->SetInput("Y", {"y2"});
op2->SetOutput("Out", {"out2"});
f::OpDescBind *op3 = block->AppendOp();
op3->SetType("rowwise_add");
op3->SetInput("X", {"out1"});
op3->SetInput("b", {"b3"});
op3->SetOutput("Out", {"out3"});
AppendBackward(program, {});
ASSERT_EQ(block->AllOps().size(), 7UL);
f::OpDescBind *grad_op3 = block->AllOps()[3];
ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op3->InputNames().size(), 1UL);
ASSERT_EQ(grad_op3->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out3")}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1") + "@RENAME@0"}));
EXPECT_EQ(grad_op3->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b3")}));
f::OpDescBind *grad_op4 = block->AllOps()[4];
ASSERT_EQ(grad_op4->Type(), "mul_grad");
ASSERT_EQ(grad_op4->InputNames().size(), 4UL);
ASSERT_EQ(grad_op4->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op4->Input("X"), std::vector<std::string>({"out1"}));
EXPECT_EQ(grad_op4->Input("Y"), std::vector<std::string>({"y2"}));
EXPECT_EQ(grad_op4->Input("Out"), std::vector<std::string>({"out2"}));
EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out2")}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("out1") + "@RENAME@1"}));
EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")),
std::vector<std::string>({f::GradVarName("y2")}));
f::OpDescBind *sum_op = block->AllOps()[5];
ASSERT_EQ(sum_op->Type(), "sum");
ASSERT_EQ(sum_op->InputNames().size(), 1UL);
ASSERT_EQ(sum_op->OutputNames().size(), 1UL);
EXPECT_EQ(sum_op->Input("X"),
std::vector<std::string>({f::GradVarName("out1") + "@RENAME@0",
f::GradVarName("out1") + "@RENAME@1"}));
EXPECT_EQ(sum_op->Output("Out"),
std::vector<std::string>({f::GradVarName("out1")}));
f::OpDescBind *grad_op1 = block->AllOps()[6];
ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad");
ASSERT_EQ(grad_op1->InputNames().size(), 1UL);
ASSERT_EQ(grad_op1->OutputNames().size(), 2UL);
EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")),
std::vector<std::string>({f::GradVarName("out1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("X")),
std::vector<std::string>({f::GradVarName("x1")}));
EXPECT_EQ(grad_op1->Output(f::GradVarName("b")),
std::vector<std::string>({f::GradVarName("b1")}));
}
\ No newline at end of file
paddle/framework/block_desc.cc
浏览文件 @ 240adef1
......@@ -34,6 +34,10 @@ VarDescBind *BlockDescBind::Var(const std::string &name) const {
return it->second.get();
}
bool BlockDescBind::HasVar(const std::string &name) const {
return vars_.find(name) != vars_.end();
}
std::vector<VarDescBind *> BlockDescBind::AllVars() const {
std::vector<VarDescBind *> res;
for (const auto &p : vars_) {
......@@ -70,6 +74,12 @@ void BlockDescBind::Sync() {
for (auto &op_desc : ops_) {
op_field.AddAllocated(op_desc->Proto());
}
auto &var_field = *this->desc_->mutable_vars();
var_field.Clear();
var_field.Reserve(static_cast<int>(vars_.size()));
for (auto &var_desc : vars_) {
var_field.AddAllocated(var_desc.second->Proto());
}
need_update_ = false;
}
}
......
paddle/framework/block_desc.h
浏览文件 @ 240adef1
......@@ -15,10 +15,12 @@ limitations under the License. */
#pragma once
#include <deque>
#include <memory>
#include <unordered_map>
#include <vector>
#include "paddle/framework/op_desc.h"
#include "paddle/framework/var_desc.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
......@@ -31,12 +33,17 @@ class ProgramDescBind;
class BlockDescBind {
public:
friend std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind &program_desc, int block_idx,
std::unordered_set<std::string> &no_grad_vars);
friend void AppendBackward(
ProgramDescBind &program_desc,
const std::unordered_set<std::string> &no_grad_vars);
BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
: prog_(prog), desc_(desc), need_update_(false) {}
BlockDescBind(const BlockDescBind &o) = delete;
BlockDescBind &operator=(const BlockDescBind &o) = delete;
int32_t ID() const { return desc_->idx(); }
int32_t Parent() const { return desc_->parent_idx(); }
......@@ -45,6 +52,8 @@ class BlockDescBind {
VarDescBind *Var(const std::string &name_bytes) const;
bool HasVar(const std::string &var_name) const;
std::vector<VarDescBind *> AllVars() const;
BlockDescBind *ParentBlock() const;
......@@ -66,6 +75,8 @@ class BlockDescBind {
std::deque<std::unique_ptr<OpDescBind>> ops_;
std::unordered_map<std::string, std::unique_ptr<VarDescBind>> vars_;
DISABLE_COPY_AND_ASSIGN(BlockDescBind);
};
} // namespace framework
} // namespace paddle
paddle/framework/data_type.h 0 → 100644
浏览文件 @ 240adef1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <typeindex>
#include "paddle/framework/framework.pb.h"
namespace paddle {
namespace framework {
inline DataType ToDataType(std::type_index type) {
if (typeid(float).hash_code() == type.hash_code()) {
return DataType::FP32;
} else if (typeid(double).hash_code() == type.hash_code()) {
return DataType::FP64;
} else if (typeid(int).hash_code() == type.hash_code()) {
return DataType::INT32;
} else {
PADDLE_THROW("Not supported");
}
}
} // namespace framework
} // namespace paddle
paddle/framework/details/op_registry.h 0 → 100644
浏览文件 @ 240adef1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/grad_op_desc_maker.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/op_proto_maker.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
namespace details {
enum OpInfoFillType {
kOperator = 0,
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2
};
template <typename T>
struct OpInfoFillTypeID {
static constexpr OpInfoFillType ID() {
return std::is_base_of<OperatorBase, T>::value
? kOperator
: (std::is_base_of<OpProtoAndCheckerMaker, T>::value
? kOpProtoAndCheckerMaker
: (std::is_base_of<GradOpDescMakerBase, T>::value
? kGradOpDescMaker
: static_cast<OpInfoFillType>(-1)));
}
};
template <typename T, OpInfoFillType = OpInfoFillTypeID<T>::ID()>
struct OpInfoFiller;
template <size_t I, bool at_end, typename... ARGS>
class OperatorRegistrarRecursive;
template <size_t I, typename... ARGS>
class OperatorRegistrarRecursive<I, false, ARGS...> {
public:
using T = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {
OpInfoFiller<T> fill;
fill(op_type, info);
constexpr auto size = sizeof...(ARGS);
OperatorRegistrarRecursive<I + 1, I + 1 == size, ARGS...> reg(op_type,
info);
(void)(reg);
}
};
template <size_t I, typename... ARGS>
class OperatorRegistrarRecursive<I, true, ARGS...> {
public:
OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {}
};
template <typename T>
struct OpInfoFiller<T, kOperator> {
void operator()(const char* op_type, OpInfo* info) const {
info->creator_ = [](const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs,
const AttributeMap& attrs) {
return new T(type, inputs, outputs, attrs);
};
}
};
template <typename T>
struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
void operator()(const char* op_type, OpInfo* info) const {
info->proto_ = new OpProto;
info->checker_ = new OpAttrChecker();
auto maker = T(info->proto_, info->checker_);
maker.Validate();
info->proto_->set_type(op_type);
PADDLE_ENFORCE(
info->proto_->IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, info->proto_->InitializationErrorString());
}
};
template <typename T>
struct OpInfoFiller<T, kGradOpDescMaker> {
void operator()(const char* op_type, OpInfo* info) const {
info->grad_op_maker_ = [](const OpDescBind& fwd_op) {
T maker(fwd_op);
return maker();
};
}
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/framework/executor.cc 0 → 100644
浏览文件 @ 240adef1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/executor.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <set>
#include <vector>
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
namespace paddle {
namespace framework {
const std::string kFeedOpType = "feed";
const std::string kFetchOpType = "fetch";
Executor::Executor(const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
device_contexts_.resize(places.size());
for (size_t i = 0; i < places.size(); i++) {
if (platform::is_cpu_place(places[i])) {
device_contexts_[i] = new platform::CPUDeviceContext(
boost::get<platform::CPUPlace>(places[i]));
} else if (platform::is_gpu_place(places[i])) {
#ifdef PADDLE_WITH_CUDA
device_contexts_[i] = new platform::CUDADeviceContext(
boost::get<platform::GPUPlace>(places[i]));
#else
PADDLE_THROW(
"'GPUPlace' is not supported, Please re-compile with WITH_GPU "
"option");
#endif
}
}
}
Executor::~Executor() {
for (auto& device_context : device_contexts_) {
delete device_context;
}
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
// - will change to use multiple blocks for RNN op and Cond Op
PADDLE_ENFORCE_GT(pdesc.blocks_size(), block_id);
auto& block = pdesc.blocks(block_id);
auto& device = device_contexts_[0];
// Instantiate all the vars in the global scope
for (auto& var : block.vars()) {
scope->NewVar(var.name());
}
Scope& local_scope = scope->NewScope();
std::vector<bool> should_run = Prune(pdesc, block_id);
PADDLE_ENFORCE_EQ(should_run.size(), static_cast<size_t>(block.ops_size()));
for (size_t i = 0; i < should_run.size(); ++i) {
if (should_run[i]) {
for (auto& var : block.ops(i).outputs()) {
for (auto& argu : var.arguments()) {
if (local_scope.FindVar(argu) == nullptr) {
local_scope.NewVar(argu);
}
}
}
auto op = paddle::framework::OpRegistry::CreateOp(block.ops(i));
op->Run(local_scope, *device);
}
}
// TODO(tonyyang-svail):
// - Destroy local_scope
}
std::vector<bool> Prune(const ProgramDesc& pdesc, int block_id) {
// TODO(tonyyang-svail):
// - will change to use multiple blocks for RNN op and Cond Op
auto& block = pdesc.blocks(block_id);
auto& ops = block.ops();
bool expect_feed = true;
for (auto& op_desc : ops) {
PADDLE_ENFORCE(op_desc.type() != kFeedOpType || expect_feed,
"All FeedOps are at the beginning of the ProgramDesc");
expect_feed = (op_desc.type() == kFeedOpType);
}
bool expect_fetch = true;
for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
auto& op_desc = *op_iter;
PADDLE_ENFORCE(op_desc.type() != kFetchOpType || expect_fetch,
"All FetchOps must at the end of the ProgramDesc");
expect_fetch = (op_desc.type() == kFetchOpType);
}
std::set<std::string> dependent_vars;
std::vector<bool> should_run;
for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
auto& op_desc = *op_iter;
bool found_dependent_vars = false;
for (auto& var : op_desc.outputs()) {
for (auto& argu : var.arguments()) {
if (dependent_vars.count(argu) != 0) {
found_dependent_vars = true;
}
}
}
if (op_desc.type() == kFetchOpType || found_dependent_vars) {
// erase its output to the dependency graph
for (auto& var : op_desc.outputs()) {
for (auto& argu : var.arguments()) {
dependent_vars.erase(argu);
}
}
// insert its input to the dependency graph
for (auto& var : op_desc.inputs()) {
for (auto& argu : var.arguments()) {
dependent_vars.insert(argu);
}
}
should_run.push_back(true);
} else {
should_run.push_back(false);
}
}
// TODO(tonyyang-svail):
// - check this after integration of Init
// PADDLE_ENFORCE(dependent_vars.empty());
// since we are traversing the ProgramDesc in reverse order
// we reverse the should_run vector
std::reverse(should_run.begin(), should_run.end());
return should_run;
}
} // namespace framework
} // namespace paddle
paddle/framework/executor.h 0 → 100644
浏览文件 @ 240adef1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace framework {
class Executor {
public:
explicit Executor(const std::vector<platform::Place>& places);
~Executor();
/* @Brief
* Runtime evaluation of the given ProgramDesc under certain Scope
*
* @param
* ProgramDesc
* Scope
*/
void Run(const ProgramDesc&, Scope*, int);
private:
std::vector<platform::DeviceContext*> device_contexts_;
};
/* @Brief
* Pruning the graph
*
* @param
* ProgramDesc
*
* @return
* vector<bool> Same size as ops. Indicates whether an op should be run.
*/
std::vector<bool> Prune(const ProgramDesc& pdesc, int block_id);
} // namespace framework
} // namespace paddle
paddle/framework/executor_test.cc 0 → 100644
浏览文件 @ 240adef1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/executor.h"
#include <memory>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/backward.h"
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(elementwise_add);
USE_OP(gaussian_random);
USE_OP(feed);
USE_OP(fetch);
USE_OP(mul);
USE_OP(sum);
USE_OP(squared_l2_distance);
USE_OP(fill_constant);
USE_OP(sgd);
using namespace paddle::platform;
using namespace paddle::framework;
void AddOp(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, AttributeMap attrs,
paddle::framework::BlockDescBind* block) {
// insert output
for (auto kv : outputs) {
for (auto v : kv.second) {
auto var = block->NewVar(v);
var->SetDataType(paddle::framework::DataType::FP32);
}
}
// insert op
auto op = block->AppendOp();
op->SetType(type);
for (auto& kv : inputs) {
op->SetInput(kv.first, kv.second);
}
for (auto& kv : outputs) {
op->SetOutput(kv.first, kv.second);
}
op->SetAttrMap(attrs);
}
// Tensors in feed value variable will only be in CPUPlace
// So we can memcpy the data from vector<T> to feed_value
template <typename T>
void SetFeedVariable(const std::vector<std::vector<T>>& inputs,
const std::vector<std::vector<int64_t>>& dims) {
Variable* g_feed_value = GetGlobalScope().FindVar("feed_value");
auto& feed_inputs =
*(g_feed_value->GetMutable<std::vector<paddle::framework::Tensor>>());
size_t size = inputs.size();
feed_inputs.resize(size);
for (size_t i = 0; i < size; i++) {
T* dst = feed_inputs[i].mutable_data<T>(make_ddim(dims[i]), CPUPlace());
memcpy(dst, inputs[i].data(), inputs[i].size() * sizeof(T));
}
}
// Tensors in fetch value variable will only be in CPUPlace
// So we can memcpy the data from fetch_value to vector<T>
template <typename T>
std::vector<std::vector<T>> GetFetchVariable() {
Variable* g_fetch_value = GetGlobalScope().FindVar("fetch_value");
auto& fetch_outputs =
*(g_fetch_value->GetMutable<std::vector<paddle::framework::Tensor>>());
size_t size = fetch_outputs.size();
std::vector<std::vector<T>> result;
result.reserve(size);
for (size_t i = 0; i < size; i++) {
std::vector<T> tmp;
tmp.resize(fetch_outputs[i].numel());
memcpy(tmp.data(), fetch_outputs[i].data<T>(),
fetch_outputs[i].numel() * sizeof(T));
result.push_back(tmp);
}
return result;
}
class ExecutorTesterRandom : public ::testing::Test {
public:
virtual void SetUp() override {
int input_dim = 3, batch_size = 2, embed_dim = 5;
auto temp_init_root_block = init_pdesc_.add_blocks();
temp_init_root_block->set_idx(0);
temp_init_root_block->set_parent_idx(-1);
paddle::framework::ProgramDescBind& init_program =
paddle::framework::ProgramDescBind::Instance(&init_pdesc_);
paddle::framework::BlockDescBind* init_root_block = init_program.Block(0);
AddOp("gaussian_random", {}, {{"Out", {"w1"}}},
{{"dims", std::vector<int>{input_dim, embed_dim}}}, init_root_block);
AddOp("gaussian_random", {}, {{"Out", {"w2"}}},
{{"dims", std::vector<int>{embed_dim, input_dim}}}, init_root_block);
AddOp("fetch", {{"Input", {"w1"}}}, {}, {{"col", 0}}, init_root_block);
AddOp("fetch", {{"Input", {"w2"}}}, {}, {{"col", 1}}, init_root_block);
// flush
init_program.Proto();
// run block
auto temp_root_block = pdesc_.add_blocks();
temp_root_block->set_idx(0);
temp_root_block->set_parent_idx(-1);
paddle::framework::ProgramDescBind& program =
paddle::framework::ProgramDescBind::Instance(&pdesc_);
paddle::framework::BlockDescBind* root_block = program.Block(0);
// feed data
inputs_.push_back({1.0, 1.0, 1.0, 1.0, 1.0, 1.0});
dims_.push_back({batch_size, input_dim});
AddOp("feed", {}, {{"Out", {"a"}}},
{{"dims", std::vector<int>{batch_size, input_dim}}, {"col", 0}},
root_block);
// forward
AddOp("mul", {{"X", {"a"}}, {"Y", {"w1"}}}, {{"Out", {"b"}}}, {},
root_block);
AddOp("mul", {{"X", {"b"}}, {"Y", {"w2"}}}, {{"Out", {"a_out"}}}, {},
root_block);
AddOp("squared_l2_distance", {{"X", {"a"}}, {"Y", {"a_out"}}},
{{"Out", {"l2_distance"}}, {"sub_result", {"l2_distance_sub"}}}, {},
root_block);
// backward
AddOp("fill_constant", {}, {{"Out", {"l2_distance@GRAD"}}},
{{"shape", std::vector<int>{batch_size, 1}}, {"value", float(1.0)}},
root_block);
AppendBackward(program, {});
// update
AddOp("fill_constant", {}, {{"Out", {"learning_rate"}}},
{{"shape", std::vector<int>{1}}, {"value", float(0.001)}},
root_block);
AddOp("sgd", {{"Param", {"w1"}},
{"LearningRate", {"learning_rate"}},
{"Grad", {"w1@GRAD"}}},
{{"ParamOut", {"w1"}}}, {}, root_block);
AddOp("sgd", {{"Param", {"w2"}},
{"LearningRate", {"learning_rate"}},
{"Grad", {"w2@GRAD"}}},
{{"ParamOut", {"w2"}}}, {}, root_block);
AddOp("fetch", {{"Input", {"w1"}}}, {}, {{"col", 0}}, root_block);
AddOp("fetch", {{"Input", {"w2"}}}, {}, {{"col", 1}}, root_block);
AddOp("fetch", {{"Input", {"l2_distance"}}}, {}, {{"col", 0}}, root_block);
// flush
program.Proto();
}
protected:
ProgramDesc init_pdesc_;
ProgramDesc pdesc_;
std::vector<std::vector<float>> inputs_;
std::vector<std::vector<int64_t>> dims_;
};
class ExecutorTesterFeedAndFetch : public ::testing::Test {
public:
virtual void SetUp() override {
auto temp_root_block = pdesc_.add_blocks();
temp_root_block->set_idx(0);
temp_root_block->set_parent_idx(-1);
// wrap to BlockDescBind
paddle::framework::ProgramDescBind& program =
paddle::framework::ProgramDescBind::Instance(&pdesc_);
paddle::framework::BlockDescBind* root_block = program.Block(0);
std::vector<int> dim{6};
AddOp("feed", {}, {{"Out", {"a"}}}, {{"dims", dim}, {"col", 0}},
root_block);
AddOp("feed", {}, {{"Out", {"b"}}}, {{"dims", dim}, {"col", 1}},
root_block);
AddOp("fetch", {{"Input", {"a"}}}, {}, {{"col", 0}}, root_block);
AddOp("fetch", {{"Input", {"b"}}}, {}, {{"col", 1}}, root_block);
// flush
program.Proto();
std::vector<float> vec1 = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
std::vector<float> vec2 = {4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
inputs_.push_back(vec1);
inputs_.push_back(vec2);
dims_.push_back({static_cast<int64_t>(vec1.size())});
dims_.push_back({static_cast<int64_t>(vec2.size())});
}
protected:
ProgramDesc pdesc_;
std::vector<std::vector<float>> inputs_;
std::vector<std::vector<int64_t>> dims_;
};
#ifndef PADDLE_WITH_CUDA
TEST_F(ExecutorTesterRandom, CPU) {
std::vector<Place> places;
CPUPlace cpu_place;
places.push_back(cpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
paddle::memory::Used(cpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
executor->Run(init_pdesc_, &GetGlobalScope(), 0);
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
std::vector<std::vector<float>> result = GetFetchVariable<float>();
}
TEST_F(ExecutorTesterFeedAndFetch, CPU) {
std::vector<Place> places;
CPUPlace cpu_place;
places.push_back(cpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
paddle::memory::Used(cpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
for (int batch_id = 0; batch_id < 3; batch_id++) {
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
std::vector<std::vector<float>> result = GetFetchVariable<float>();
PADDLE_ENFORCE_EQ(result.size(), inputs_.size());
for (size_t i = 0; i < result.size(); ++i) {
PADDLE_ENFORCE_EQ(result[i].size(), inputs_[i].size());
for (size_t j = 0; j < result[i].size(); ++j) {
PADDLE_ENFORCE_EQ(result[i][j], inputs_[i][j]);
}
}
}
}
#else
TEST_F(ExecutorTesterRandom, GPU) {
std::vector<Place> places;
GPUPlace gpu_place(0);
places.push_back(gpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
// If paddle is compiled with GPU, both CPU and GPU BuddyAllocator
// need to be used at first.
paddle::memory::Used(CPUPlace());
paddle::memory::Used(gpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
executor->Run(init_pdesc_, &GetGlobalScope(), 0);
for (int batch_id = 0; batch_id < 3; batch_id++) {
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
}
}
TEST_F(ExecutorTesterFeedAndFetch, GPU) {
std::vector<Place> places;
GPUPlace gpu_place(0);
places.push_back(gpu_place);
// We have a global Scope and BuddyAllocator, and we must ensure
// global BuddyAllocator is initialized before global Scope. Thus,
// global Scope will deconstruct before BuddyAllocator. Otherwise,
// "pointer being freed was not allocated" error will appear.
// If paddle is compiled with GPU, both CPU and GPU BuddyAllocator
// need to be used at first.
paddle::memory::Used(CPUPlace());
paddle::memory::Used(gpu_place);
std::unique_ptr<Executor> executor(new Executor(places));
for (int batch_id = 0; batch_id < 3; batch_id++) {
SetFeedVariable<float>(inputs_, dims_);
executor->Run(pdesc_, &GetGlobalScope(), 0);
std::vector<std::vector<float>> result = GetFetchVariable<float>();
PADDLE_ENFORCE_EQ(result.size(), inputs_.size());
for (size_t i = 0; i < result.size(); ++i) {
PADDLE_ENFORCE_EQ(result[i].size(), inputs_[i].size());
for (size_t j = 0; j < result[i].size(); ++j) {
PADDLE_ENFORCE_EQ(result[i][j], inputs_[i][j]);
}
}
}
}
#endif
paddle/framework/framework.proto
浏览文件 @ 240adef1
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
syntax = "proto2";
option optimize_for = LITE_RUNTIME;
package paddle.framework;
enum AttrType {
......@@ -66,7 +67,6 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool not_in_gradient = 5 [ default = false ];
}
// AttrProto describes the C++ type Attribute.
......@@ -106,6 +106,7 @@ message LoDTensorDesc {
message VarDesc {
required string name = 1;
optional LoDTensorDesc lod_tensor = 2;
optional bool persistable = 3 [ default = false ];
}
message BlockDesc {
......@@ -115,4 +116,7 @@ message BlockDesc {
repeated OpDesc ops = 4;
}
// Please refer to
// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md
// for more details.
message ProgramDesc { repeated BlockDesc blocks = 1; }
paddle/framework/grad_op_builder.cc 已删除 100644 → 0
浏览文件 @ 4db50fbc
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either
express or implied. See the License for the specific language governing
permissions and limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
enum class OpArgType { IN, OUT };
static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type,
bool is_grad, VariableNameMap* vars) {
const auto& src_inout =
src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs();
auto& dst_inout = *vars;
auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto();
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
if (arg.not_in_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_inout[dst_name].reserve(src_inout.at(src_name).size());
for (auto& var_name : src_inout.at(src_name)) {
std::string s = is_grad ? GradVarName(var_name) : var_name;
dst_inout[dst_name].emplace_back(s);
}
}
}
OperatorBase* BuildGradOp(const OperatorBase* op) {
auto& info = OpInfoMap::Instance().Get(op->Type());
PADDLE_ENFORCE(info.HasGradientOp());
VariableNameMap inputs;
VariableNameMap outputs;
TransOpArg(op, OpArgType::IN, false, &inputs); // I
TransOpArg(op, OpArgType::OUT, false, &inputs); // O
TransOpArg(op, OpArgType::OUT, true, &inputs); // OG
TransOpArg(op, OpArgType::IN, true, &outputs); // IG
auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_);
return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs());
}
} // namespace framework
} // namespace paddle
paddle/framework/grad_op_builder_test.cc 已删除 100644 → 0
浏览文件 @ 4db50fbc
#include "paddle/framework/grad_op_builder.h"
#include <gtest/gtest.h>
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
USE_OP(add);
namespace paddle {
namespace framework {
class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
public:
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").AsDuplicable();
AddInput("In3", "another single input");
AddOutput("Out1", "a single output");
AddOutput("Out2_mult", "a multiple output").AsDuplicable();
AddComment("test op with multiple inputs and outputs");
}
};
class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
public:
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient();
AddInput("In3_mult", "another multiple input").AsDuplicable();
AddOutput("Out1_mult", "a multiple output").AsDuplicable();
AddOutput("Out2", "a single output").NotInGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
"add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
EXPECT_EQ(grad_add_op->Outputs().size(), 2UL);
EXPECT_EQ(grad_add_op->Input("X"), "x");
EXPECT_EQ(grad_add_op->Input("Y"), "y");
EXPECT_EQ(grad_add_op->Input("Out"), "out");
EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"mult_io", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2", "in2_3"}},
{"In3", {"in3"}}},
{{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
EXPECT_EQ(grad_test_op->Input("In3"), "in3");
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")),
f::GradVarName("out1"));
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")),
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
f::GradVarName("in2_2"),
f::GradVarName("in2_3")}));
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3"));
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"io_ignored", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2"}},
{"In3_mult", {"in3_1", "in3_2"}}},
{{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
std::vector<std::string>(
{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
f::GradVarName("out2"));
ASSERT_EQ(grad_test_op->Outputs().size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>(
{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")),
std::vector<std::string>(
{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
}
paddle/framework/grad_op_desc_maker.h 0 → 100644
浏览文件 @ 240adef1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_desc.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
class GradOpDescMakerBase {
public:
explicit GradOpDescMakerBase(const OpDescBind& fwd_op) : fwd_op_(fwd_op) {}
virtual ~GradOpDescMakerBase() = default;
virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
protected:
static std::vector<std::string> ToGradNames(
const std::vector<std::string>& var_names) {
std::vector<std::string> ret_val;
ret_val.reserve(var_names.size());
std::transform(var_names.begin(), var_names.end(),
std::back_inserter(ret_val), GradVarName);
return ret_val;
}
std::vector<std::string> InputGrad(const std::string& name) const {
return ToGradNames(fwd_op_.Input(name));
}
std::vector<std::string> OutputGrad(const std::string& name) const {
return ToGradNames(fwd_op_.Output(name));
}
std::vector<std::string> InputNames() const {
return this->fwd_op_.InputNames();
}
std::vector<std::string> OutputNames() const {
return this->fwd_op_.OutputNames();
}
std::vector<std::string> Input(const std::string& name) const {
return fwd_op_.Input(name);
}
std::vector<std::string> Output(const std::string& name) const {
return fwd_op_.Output(name);
}
const std::unordered_map<std::string, Attribute>& Attrs() const {
return fwd_op_.GetAttrMap();
}
const Attribute& GetAttr(const std::string& name) const {
auto& map = fwd_op_.GetAttrMap();
auto it = map.find(name);
PADDLE_ENFORCE(it != map.end(), "Cannot find attribute %s", name);
return it->second;
}
std::string ForwardOpType() const { return this->fwd_op_.Type(); }
private:
const OpDescBind& fwd_op_;
};
class SingleGradOpDescMaker : public GradOpDescMakerBase {
public:
using GradOpDescMakerBase::GradOpDescMakerBase;
std::vector<std::unique_ptr<OpDescBind>> operator()() const {
std::vector<std::unique_ptr<OpDescBind>> retv;
retv.emplace_back(this->Apply());
return retv;
}
protected:
virtual std::unique_ptr<OpDescBind> Apply() const = 0;
};
class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
public:
using SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
virtual std::unique_ptr<OpDescBind> Apply() const {
auto* grad = new OpDescBind();
grad->SetType(this->GradOpType());
for (auto& input_param : this->InputNames()) {
grad->SetInput(input_param, this->Input(input_param));
grad->SetOutput(GradVarName(input_param), this->InputGrad(input_param));
}
for (auto& output_param : this->OutputNames()) {
grad->SetInput(output_param, this->Output(output_param));
grad->SetInput(GradVarName(output_param), this->OutputGrad(output_param));
}
grad->SetAttrMap(this->Attrs());
return std::unique_ptr<OpDescBind>(grad);
}
virtual std::string GradOpType() const {
return this->ForwardOpType() + "_grad";
}
};
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor.h
浏览文件 @ 240adef1
......@@ -15,7 +15,7 @@
#pragma once
#include <memory>
#ifndef PADDLE_ONLY_CPU
#ifdef PADDLE_WITH_CUDA
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/system/cuda/experimental/pinned_allocator.h>
......@@ -29,7 +29,7 @@
namespace paddle {
namespace framework {
#ifdef PADDLE_ONLY_CPU
#ifndef PADDLE_WITH_CUDA
template <typename T>
using Vector = std::vector<T>;
#else
......
paddle/framework/lod_tensor.md
浏览文件 @ 240adef1
# Design Doc: LoD (Level-of-Detail) Tensor
PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor.
Like other deep learning systems, PaddlePaddle supports training models from sequence data. Also, like other systems, PaddlePaddle represent a mini-batch of sequences as a Tensor. What is different is that PaddlePaddle doesn't require all sequences in a mini-batch to be of the same length. Thus no need for padding zeros.
## Challenge of Variable-length Inputs
| | TensorFlow | PaddlePaddle |
|-----------------------|------------|--------------|
| RNN | Support | Support |
| recursive RNN | Support | Support |
| padding zeros | Must | No need |
| blob data type | Tensor | LoDTensor |
People usually represent a mini-batch by a Tensor. For example, a mini-batch of 10 images, each of size 32x32, is a 10x32x32 Tensor. So a transformation, T, of all images can be a matrix multiplication of the 10xOx32-dimensional tensor T and the 10x32x32 Tensor.
PaddlePaddle achieves this flexibility by passing through a new data type, *LoD Tensor*, which is a Tensor attached with segmentation index known as *LoD*, between operators. The LoD index doesn't only segment a tensor, but also recursively segments sub-sequences. This document presents the design of LoD and LoDTensor.
Another example is that each mini-batch contains 32 sentences, where each word is a D-dimensional one-hot vector. If all sentences have the same length L, we can represent this mini-batch by a 32xLxD tensor. However, in most cases, sentences have variable lengths, and we will need an index data structure to record these variable lengths.
## LoD as a Solution
## The Challenge: Variable-length Sequences
### Mini-Batch of variable-length sentences
Most deep learning systems represent a mini-batch as a Tensor. For example, a mini-batch of 10 images, each of size 32x32, is a 10x32x32 Tensor. Another example is that each mini-batch contains N sentences, where each word is a D-dimensional one-hot vector. Suppose that all sentences have the same length L, we can represent this mini-batch by a NxLxD tensor.
Let's imagine a mini-batch of 3 variable lengths sentences, containing 3, 1, and 2 words respectively. We can represent it by a (3+1+2)xD tensor plus some index information:
Both examples show that the elements of sequences are usually of the same size. In the first example, all images are 32x32, and in the second one, all words are D-dimensional vectors. It doesn't make sense to allow variable-sized images, as that would require transformations like convolution to handle variable-sized Tensors.
The real challenge is that in most cases, sentences have variable lengths, and we will need an index data structure to segment the tensor into sequences. Also, sequences might consist of sub-sequences.
## A Solution: The LoD Index
To understand our solution, it is best to look at some examples.
### A Mini-Batch of Sentences
Let's imagine a mini-batch of 3 variable lengths sentences composed of 3, 1, and 2 words, respectively. We can represent the mini-batch by a (3+1+2)xD tensor plus some index information:
```
3
3 1 2
||| | ||
```
Each `|` represents a D-dimensional word vectors. The number 3 on top indicate 3 sentences, and numbers 3, 1, and 2 on the second level represent the number of words in each sentence.
where each `|` represents a D-dimensional word vector. The numbers, 3, 1, and 2, form a 1-level LoD.
### Recursive Sequences
Let check another example of a 2-level LoD Tensor. Consider a mini-batch of three articles with 3, 1, and 2 sentences, and each sentence consists of a variable number of words:
```
3 1 2
3 2 4 1 2 3
||| || |||| | || |||
```
### Mini-Batch of variable-length videos
### A Mini-Batch of Videos
This approach generalizes to the case where elements are not words, but higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. If a mini-batch contains 3 videos of 3, 1, and 2 frames respectively. The underlying tensor is of size (3+1+2)x640x480. The index information illustrates as:
LoD tensors generalize to the case where elements are higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. Here is a mini-batch of 3 videos with 3, 1, and 2 frames, respectively.
```
3
3 1 2
口口口 口 口口
```
where each `口` represents an image.
The underlying tensor is of size (3+1+2)x640x480, and each `口` represents a 640x480 image.
### Mini-Batch of fixed-size images
### A Mini-Batch of Images
Let's get back to a typical example, image classification, where each mini-batch has M fixed-sized images. The LoD Tensor representation is
In traditional cases like a mini-batch with N fixed-sized images, the LoD Tensor representation is as
```
M
1 1 1 1 1
口口口口 ... 口
```
The many 1's on the second level seem duplicated. For this particular case of 2 levels and the second level always have length 1, we can ignore the LoD index.
### Design and summarization
In this case, we don't lose any information by ignoring the many 1's in the index and simply considering this LoD Tensor as a usual Tensor:
In summary, as long as that the essential elements (words or images) have the same size, we can represent mini-batches by a LoD Tensor:
```
口口口口 ... 口
```
- The underlying tensor has size LxD1xD2x..., where D1xD2... is the size of the essential elements, and
- The first dimension size L has an additonal property -- a LoD index as a nested vector:
### Model Parameters
```c++
typedef std::vector<std::<vector>> LoD;
```
A model parameter is just a usual Tensor, which, just like the above example, is a **0-level LoD Tensor**.
- The LoD index is not necessary when there are only two levels and all elements of the second level have length 1.
## Slicing of LoD Tensor
## The LoD Tensor
Consider that we have a network with three levels of RNN: the top level one handles articles, the second level one handles sentences, and the basic level one handles words. This network requires that mini-batches represented by 3 level LoD Tensor, for example,
Let us revisit above example of the 2-level LoD Tensor
```
3
3 1 2
3 2 4 1 2 3
||| || |||| | || |||
```
To allow each level of RNN to handle its input, we define **the slicing of a LoD Tensor is defined as getting the j-th sequence on level i, or the <i,j>-slice**
It is indeed a tree, where leaves are elementary sequences identified by **branches**.
For example, the third sentence in above example is identified by branch <0,2>, where 0 indicates the first article with length 3, and 2 indicates the third sentence in this article with length 4.
### The LoD Index
For example, the <2,1>-slice of above slice is
We can save the LoD index in the above example
```
2
||
3 1 2
3 2 4 1 2 3
```
and the <1,2>-slice of above example is
in a not-full 2D matrix:
```c++
typedef std::vector<std::vector<int> > LoD;
```
2
2 3
|| |||
```
Let's go on slicing this slice. Its <1,1>-slice is
where
- `LoD.size()` is the number of levels, or the maximum length of branches,
- `LoD[i][j]` is the length of the j-th segment at the i-th level.
## The Offset Representation
To quickly access elementary sequences, we adopt an offset representation -- instead of saving the lengths, we save the beginning and ending elements of sequences.
In the above example, we accumulate the length of elementary sequences:
```
1
1
|
3 2 4 1 2 3
```
### The Slicing Algorithm
into offsets
The algorithm, with over-simplified data structure, is defined as
```
0 3 5 9 10 12 15
= = = = = =
3 2+3 4+5 1+9 2+10 3+12
```
```c++
typedef std::vector<std::vector<int>> LoD;
so we know that the first sentence is from word 0 to word 3, and the second sentence from work 3 to word 5.
struct LoDTensor {
LoD lod_;
float* tensor_;
};
Similarly, the lengths in the top level LoD
LoDTensor Slice(const LoDTensor& lodt, int level, int sequence);
```
3 1 2
```
Let us revisit the example above
are transformed into offsets of elements/words as follows:
```
3
3 1 2
3 2 4 1 2 3
||| || |||| | || |||
0 9 10 15
= = =
3+2+4 1+9 2+3+10
```
Suppose that we want to retrieve the <1,2>-slice
so we can tell that the first article is from word 0 to word 9, and the second article is from word 9 to word 10.
The complete offset representation is as follows:
```
2
2 3
|| |||
0 9 10 15
0 3 5 9 10 12 15
||| || |||| | || |||
```
we will need to find out the starting position of this slice by summing over all leaf nodes in `LoD` to the left of the slice, i.e., 3 + 2 + 4 + 1 = 10.
## Slicing of LoD Tensors
When we use the above 2-level LoD Tensor as the input to a nested-RNN, we need to retrieve certain sequences. Here we define the sequence identified by branch <i,j,...> as the **<i,j,...>-slice**.
To avoid the traversal of the LoD tree at slicing time, we can do it at the construction time -- instead of saving the lengths of the next level in the LoD tree, we can save the starting offset of the next level. For example, above LoD Tensor can be transformed into
For example, the <2>-slice of above example is
```
0
0 9 10
0 3 5 9 10 12
||| || |||| | || |||
10 15
10 12 15
|| |||
```
We don't really need the 0 on top, so the LoD Tensor could be
and the <2,0>-slice of above slice is
```
0 9 10
0 3 5 9 10 12
||| || |||| | || |||
10 12
||
```
paddle/framework/op_desc.cc
浏览文件 @ 240adef1
......@@ -13,11 +13,24 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/op_desc.h"
#include <functional>
#include <unordered_map>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs) {
op_desc_.set_type(type);
inputs_ = inputs;
outputs_ = outputs;
attrs_ = attrs;
need_update_ = true;
}
OpDesc *OpDescBind::Proto() {
Sync();
return &op_desc_;
......@@ -31,11 +44,10 @@ const std::vector<std::string> &OpDescBind::Input(
return it->second;
}
std::vector<std::string> OpDescBind::InputNames() const {
std::vector<std::string> OpDescBind::InputArgumentNames() const {
std::vector<std::string> retv;
retv.reserve(this->inputs_.size());
for (auto &ipt : this->inputs_) {
retv.push_back(ipt.first);
retv.insert(retv.end(), ipt.second.begin(), ipt.second.end());
}
return retv;
}
......@@ -54,11 +66,10 @@ const std::vector<std::string> &OpDescBind::Output(
return it->second;
}
std::vector<std::string> OpDescBind::OutputNames() const {
std::vector<std::string> OpDescBind::OutputArgumentNames() const {
std::vector<std::string> retv;
retv.reserve(this->outputs_.size());
for (auto &ipt : this->outputs_) {
retv.push_back(ipt.first);
retv.insert(retv.end(), ipt.second.begin(), ipt.second.end());
}
return retv;
}
......@@ -89,6 +100,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
need_update_ = true;
}
void OpDescBind::SetAttrMap(
const std::unordered_map<std::string, Attribute> &attr_map) {
attrs_ = attr_map;
need_update_ = true;
}
Attribute OpDescBind::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
......@@ -101,6 +118,47 @@ int OpDescBind::GetBlockAttr(const std::string &name) const {
return boost::get<BlockDesc *>(it->second)->idx();
}
const std::unordered_map<std::string, Attribute> &OpDescBind::GetAttrMap()
const {
return attrs_;
}
void OpDescBind::Rename(const std::string &old_name,
const std::string &new_name) {
for (auto &input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
for (auto &output : outputs_) {
std::replace(output.second.begin(), output.second.end(), old_name,
new_name);
}
need_update_ = true;
}
struct SetAttrDescVisitor : public boost::static_visitor<void> {
explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
mutable OpDesc::Attr *attr_;
void operator()(int v) const { attr_->set_i(v); }
void operator()(float v) const { attr_->set_f(v); }
void operator()(const std::string &v) const { attr_->set_s(v); }
void operator()(bool b) const { attr_->set_b(b); }
void operator()(const std::vector<int> &v) const {
VectorToRepeated(v, attr_->mutable_ints());
}
void operator()(const std::vector<float> &v) const {
VectorToRepeated(v, attr_->mutable_floats());
}
void operator()(const std::vector<std::string> &v) const {
VectorToRepeated(v, attr_->mutable_strings());
}
void operator()(const std::vector<bool> &v) const {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->idx()); }
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
};
void OpDescBind::Sync() {
if (need_update_) {
this->op_desc_.mutable_inputs()->Clear();
......@@ -123,11 +181,45 @@ void OpDescBind::Sync() {
attr_desc->set_name(attr.first);
attr_desc->set_type(
static_cast<framework::AttrType>(attr.second.which() - 1));
boost::apply_visitor(SetAttrDescVisitor(attr_desc), attr.second);
SetAttrDescVisitor visitor(attr_desc);
boost::apply_visitor(visitor, attr.second);
}
need_update_ = false;
}
}
using InferShapeFuncMap =
std::unordered_map<std::string /*op_type*/,
std::function<void(InferShapeContext *)>>;
static InferShapeFuncMap &InferShapeFuncs() {
static InferShapeFuncMap *g_map = nullptr;
if (g_map == nullptr) {
g_map = new InferShapeFuncMap();
auto &info_map = OpInfoMap::Instance();
// all registered kernels
for (auto &pair : OperatorWithKernel::AllOpKernels()) {
auto &info = info_map.Get(pair.first);
// use empty type here to avoid runtime checks.
auto op =
static_cast<OperatorWithKernel *>(info.Creator()("", {}, {}, {}));
g_map->insert(
{pair.first, [op](InferShapeContext *ctx) { op->InferShape(ctx); }});
}
}
return *g_map;
}
void OpDescBind::InferShape(const BlockDescBind &block) const {
auto &funcs = InferShapeFuncs();
auto it = funcs.find(this->Type());
if (it == funcs.end()) {
PADDLE_THROW("Operator %s has not been registered", this->Type());
}
CompileTimeInferShapeContext ctx(*this, block);
it->second(&ctx);
}
} // namespace framework
} // namespace paddle
paddle/framework/op_desc.h
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......@@ -17,6 +17,7 @@ limitations under the License. */
#include <unordered_map>
#include <vector>
#include "paddle/framework/attribute.h"
#include "paddle/framework/type_defs.h"
#include "paddle/framework/var_desc.h"
namespace paddle {
......@@ -26,6 +27,11 @@ class BlockDescBind;
class OpDescBind {
public:
OpDescBind() {}
OpDescBind(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs);
OpDesc *Proto();
std::string Type() const { return op_desc_.type(); }
......@@ -34,20 +40,18 @@ class OpDescBind {
const std::vector<std::string> &Input(const std::string &name) const;
std::vector<std::string> InputNames() const;
std::vector<std::string> InputArgumentNames() const;
void SetInput(const std::string &param_name,
const std::vector<std::string> &args);
const std::vector<std::string> &Output(const std::string &name) const;
std::vector<std::string> OutputNames() const;
std::vector<std::string> OutputArgumentNames() const;
void SetOutput(const std::string &param_name,
const std::vector<std::string> &args);
std::string DebugString() { return this->Proto()->DebugString(); }
bool HasAttr(const std::string &name) const {
return attrs_.find(name) != attrs_.end();
}
......@@ -64,39 +68,55 @@ class OpDescBind {
int GetBlockAttr(const std::string &name) const;
private:
struct SetAttrDescVisitor : public boost::static_visitor<void> {
explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
mutable OpDesc::Attr *attr_;
void operator()(int v) const { attr_->set_i(v); }
void operator()(float v) const { attr_->set_f(v); }
void operator()(const std::string &v) const { attr_->set_s(v); }
void operator()(bool b) const { attr_->set_b(b); }
void operator()(const std::vector<int> &v) const {
VectorToRepeated(v, attr_->mutable_ints());
}
void operator()(const std::vector<float> &v) const {
VectorToRepeated(v, attr_->mutable_floats());
}
void operator()(const std::vector<std::string> &v) const {
VectorToRepeated(v, attr_->mutable_strings());
}
void operator()(const std::vector<bool> &v) const {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(BlockDesc *desc) const {
attr_->set_block_idx(desc->idx());
}
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
};
void Rename(const std::string &old_name, const std::string &new_name);
// Only be used in C++
const AttributeMap &GetAttrMap() const;
// Only be used in C++
void SetAttrMap(const AttributeMap &attr_map);
std::vector<std::string> InputNames() const { return MapKeys(inputs_); }
std::vector<std::string> OutputNames() const { return MapKeys(outputs_); }
void SetInputMap(const VariableNameMap &input) {
this->inputs_ = input;
this->need_update_ = true;
}
void SetOutputMap(const VariableNameMap &output) {
this->outputs_ = output;
this->need_update_ = true;
}
void Sync();
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
AttributeMap *MutableAttrMap() {
this->need_update_ = true;
return &this->attrs_;
}
void InferShape(const BlockDescBind &block) const;
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
std::vector<typename MapType::key_type> ret_val;
ret_val.reserve(map.size());
std::transform(
map.begin(), map.end(), std::back_inserter(ret_val),
[](const typename MapType::value_type &pair) { return pair.first; });
return ret_val;
}
OpDesc op_desc_;
std::unordered_map<std::string, std::vector<std::string>> inputs_;
std::unordered_map<std::string, std::vector<std::string>> outputs_;
std::unordered_map<std::string, Attribute> attrs_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
// need_update_ indicate there some local changes not be synchronized. If
// local changes should be synchronized, need_update_ should be set to true.
......
paddle/framework/op_info.h
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......@@ -19,21 +19,18 @@
#include <unordered_map>
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/type_defs.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
class OperatorBase;
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
using OpCreator = std::function<OperatorBase*(
const std::string& /*type*/, const VariableNameMap& /*inputs*/,
const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
struct OpInfo {
OpCreator creator_;
std::string grad_op_type_;
OpProto* proto_;
OpAttrChecker* checker_;
GradOpMakerFN grad_op_maker_;
OpProto* proto_{nullptr};
OpAttrChecker* checker_{nullptr};
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
......@@ -46,30 +43,25 @@ struct OpInfo {
return *proto_;
}
const OpAttrChecker& Checker() const {
PADDLE_ENFORCE_NOT_NULL(checker_,
"Operator Checker has not been registered");
return *checker_;
}
const OpCreator& Creator() const {
PADDLE_ENFORCE_NOT_NULL(creator_,
"Operator Creator has not been registered");
return creator_;
}
bool HasGradientOp() const { return !grad_op_type_.empty(); }
const GradOpMakerFN& GradOpMaker() const {
PADDLE_ENFORCE_NOT_NULL(grad_op_maker_,
"Operator GradOpMaker has not been registered.");
return grad_op_maker_;
}
const OpAttrChecker* Checker() const { return checker_; }
};
class OpInfoMap {
public:
static OpInfoMap& Instance();
OpInfoMap(const OpInfoMap& o) = delete;
OpInfoMap(OpInfoMap&& o) = delete;
OpInfoMap& operator=(const OpInfoMap& o) = delete;
OpInfoMap& operator=(OpInfoMap&& o) = delete;
bool Has(const std::string& op_type) const {
return map_.find(op_type) != map_.end();
}
......@@ -105,6 +97,8 @@ class OpInfoMap {
private:
OpInfoMap() = default;
std::unordered_map<std::string, const OpInfo> map_;
DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
......
paddle/framework/op_proto_maker.h
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......@@ -44,11 +44,6 @@ class OpProtoAndCheckerMaker {
var_->set_intermediate(true);
return *this;
}
VariableBuilder& NotInGradient() {
var_->set_not_in_gradient(true);
return *this;
}
};
VariableBuilder AddInput(const std::string& name, const std::string& comment);
......
paddle/framework/op_proto_maker_test.cc
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......@@ -48,4 +48,4 @@ TEST(ProtoMaker, DuplicatedInOut) {
paddle::framework::OpAttrChecker op_checker;
auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
\ No newline at end of file
}
paddle/framework/op_registry.cc
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......@@ -23,7 +23,9 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(
const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, AttributeMap attrs) {
auto& info = OpInfoMap::Instance().Get(type);
info.Checker().Check(attrs);
if (info.Checker() != nullptr) {
info.Checker()->Check(attrs);
}
auto op = info.Creator()(type, inputs, outputs, attrs);
return std::unique_ptr<OperatorBase>(op);
}
......@@ -52,9 +54,20 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
return CreateOp(op_desc.type(), inputs, outputs, attrs);
}
std::unique_ptr<OperatorBase> OpRegistry::CreateGradOp(const OperatorBase& op) {
PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops");
return std::unique_ptr<OperatorBase>(BuildGradOp(&op));
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDescBind& op_desc) {
return CreateOp(op_desc.Type(), op_desc.Inputs(), op_desc.Outputs(),
op_desc.GetAttrMap());
}
std::vector<std::unique_ptr<OpDescBind>> OpRegistry::CreateGradOpDescs(
OpDescBind* op_desc) {
auto& info = OpInfoMap::Instance().Get(op_desc->Type());
if (info.Checker() != nullptr) {
info.Checker()->Check(*op_desc->MutableAttrMap());
}
return info.grad_op_maker_(*op_desc);
}
} // namespace framework
......
paddle/framework/op_registry.h
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paddle/framework/op_registry_test.cc
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......@@ -173,3 +173,14 @@ TEST(OpRegistry, CustomChecker) {
int test_attr = op->Attr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
}
class CosineOpComplete : public paddle::framework::CosineOp {
public:
DEFINE_OP_CONSTRUCTOR(CosineOpComplete, paddle::framework::CosineOp);
DEFINE_OP_CLONE_METHOD(CosineOpComplete);
};
TEST(OperatorRegistrar, Test) {
using namespace paddle::framework;
OperatorRegistrar<CosineOpComplete, CosineOpProtoAndCheckerMaker> reg("cos");
}
paddle/framework/operator.cc
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paddle/framework/operator.h
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paddle/framework/operator_test.cc
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......@@ -113,11 +113,14 @@ class OpWithKernelTest : public OperatorWithKernel {
using OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {}
void InferShape(framework::InferShapeContext* ctx) const override {}
DataType IndicateDataType(const ExecutionContext& ctx) const override {
return DataType::FP32;
}
};
template <typename T1, typename T2>
class CPUKernelTest : public OpKernel {
class CPUKernelTest : public OpKernel<float> {
public:
void Compute(const ExecutionContext& ctx) const {
std::cout << "this is cpu kernel" << std::endl;
......@@ -144,7 +147,7 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker
}
};
class CPUKernalMultiInputsTest : public OpKernel {
class CPUKernalMultiInputsTest : public OpKernel<float> {
public:
void Compute(const ExecutionContext& ctx) const {
auto xs = ctx.op().Inputs("xs");
......
paddle/framework/program_desc.h
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......@@ -14,8 +14,10 @@ limitations under the License. */
#pragma once
#include <memory>
#include <vector>
#include "paddle/framework/framework.pb.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
......@@ -26,15 +28,10 @@ class ProgramDescBind {
public:
static ProgramDescBind &Instance(ProgramDesc *prog);
ProgramDescBind(const ProgramDescBind &o) = delete;
ProgramDescBind &operator=(const ProgramDescBind &o) = delete;
BlockDescBind *AppendBlock(const BlockDescBind &parent);
BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); }
std::string DebugString() { return Proto()->DebugString(); }
size_t Size() const { return blocks_.size(); }
ProgramDesc *Proto();
......@@ -46,6 +43,8 @@ class ProgramDescBind {
ProgramDesc *prog_;
std::vector<std::unique_ptr<BlockDescBind>> blocks_;
DISABLE_COPY_AND_ASSIGN(ProgramDescBind);
};
} // namespace framework
} // namespace paddle
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