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PaddleDetection
提交 49697d9d 编写于 作者: Y Yu Yang
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Merge branch 'develop' of github.com:baidu/Paddle into feature/make_python_catch_enforce_not_met

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benchmark/paddle/image/run_mkldnn.sh
浏览文件 @ 49697d9d
......@@ -9,11 +9,9 @@ function train() {
bs=$2
use_mkldnn=$3
if [ $3 == "True" ]; then
use_mkldnn=$3
thread=1
log="logs/${topology}-mkldnn-${bs}.log"
elif [ $3 == "False" ]; then
use_mkldnn=$3
thread=`nproc`
log="logs/${topology}-${thread}mklml-${bs}.log"
else
......@@ -39,8 +37,7 @@ if [ ! -d "logs" ]; then
mkdir logs
fi
#========= mkldnn =========#
# vgg
#========== mkldnn ==========#
train vgg 64 True
train vgg 128 True
train vgg 256 True
......
doc/design/refactor/distributed_architecture.md 0 → 100644
浏览文件 @ 49697d9d
# Design Doc: Distributed Training Architecture
## Abstract
PaddlePaddle v0.10.0 uses the "trainer-parameter server"
architecture. We run multiple replicated instances of trainers (runs
the same code written by the user) and parameter servers for
distributed training. This architecture served us well, but has some
limitations:
1. Need to write special code to handle tasks which should only be run
by a single trainer. E.g., initializing model and saving model.
2. Model parallelism is hard: need to write if-else branches conditioned
on the trainer ID to partition model onto each trainer, and manually
write the inter-model-shard communication code.
3. The user can not directly specify the parameter update rule: need
to modify the parameter server C++ code and compile a new
binary. This adds complication for researchers: A lot of extra
effort is required. Besides, the training job submission program
may not allow running arbitrary binaries.
This design doc discusses PaddlePaddle's new distributed training
architecture that addresses the above limitations.
## Analysis
We will assume the user writes the trainer program by Python, the same
analysis holds if the trainer program is written in C++.
### Limitation 1
If we look at the Python code that the user writes, there are two
kinds of functionalities:
- The training logic such as load / save model and print log.
- The neural network definition such as the definition of the data
layer, the fully connected layer, the cost function and the
optimizer.
When we training with PaddlePaddle v0.10.0 distributedly, multiple
replicated Python instances are running on different nodes: both the
training logic and the neural network computation is replicated.
The tasks that should only run once all belong to the training logic,
if we only replicate the neural network computation, but do **not**
replicate the training logic, the limitation could be solved.
### Limitation 2
Model parallelism means running a single model on multiple nodes by
partitioning the model onto different nodes and managing the
inter-model-shard communications.
PaddlePaddle should be able to modify the nerual network computation
definition to support model parallelism automatically. However, the
computation is only specified in Python code, and PaddlePaddle can not
modify Python code.
Just like compiler uses a intermediate representation (IR) so that
programmer does not need to manually optimize their code in most of
the cases - the compiler will optimize the IR:
<img src="src/compiler.png"/>
We can have our own IR too: PaddlePaddle can support model parallel by
converting the IR so the user no longer need to manually do it in
Python:
<img src="src/paddle-compile.png"/>
The IR for PaddlePaddle after refactor is called `Block`, it specifies
the computation dependency graph and the variables used in the
computation.
### Limitation 3
The user can not directly specify the parameter update rule for the
parameter server because the parameter server does not use the same
computation definition as the trainer. Instead, the update rule is
baked in the parameter server. The user can not specify the update
rule in the same way of specifying the trainer computation.
This could be fixed by making the parameter server run the same
computation definition as the trainer. For a detailed explanation,
please
see
[Design Doc: Operation Graph Based Parameter Server](./dist_train.md)
## Distributed Training Architecture
The new distributed training architecture can address the above
limitations. Below is the illustration:
<img src="src/distributed_architecture.png"/>
The architecture includes major components: *PaddlePaddle Python*,
*PaddlePaddle converter* and *PaddlePaddle runtime*:
### PaddlePaddle Python
PaddlePaddle Python is the Python library that user's Python trainer
invoke to build the neural network topology, start training, etc.
```Python
paddle.init()
input = paddle.op.recordIO("/home/data/mnist.recordio") # file stored on the cluster
img, label = input[0], input[1]
hidden = paddle.layer.fc(input=img, size=200, act=paddle.activation.Tanh())
prediction = paddle.layer.fc(input=img, size=10, act=paddle.activation.Softmax())
cost = paddle.layer.classification_cost(input=prediction, label=label)
optimizer = paddle.optimizer.SGD(cost, learning_rate=0.01)
session = paddle.session.NewRemote(num_trainer=3, num_ps=2, GPU_per_trainer=1)
for i in range(1000):
_, cost_val = session.eval(targets=[cost, optimizer])
print cost_val
```
The code above is a typical Python trainer code, the neural network
topology is built using helper functions such as
`paddle.layer.fc`. The training is done by calling `session.eval`
iteratively.
#### session.eval
As shown in the graph, `session.eval` sends the IR and the evaluation
inputs/targets to the PaddlePaddle cluster for evaluation. The
targets can be any variable in the computation graph. When the target
is the `optimizer` variable, the neural network will be optimized
once. When the target is the `cost` variable, `session.eval` returns
the cost value.
The Python `session` is a wrapper of the C++ `Session` class. For more
information about `Session`, please
see [Design Doc: Session](./session.md).
### PaddlePaddle Converter
PaddlePaddle converter automatically converts the IR in the request
(IR and evaluation inputs/targets) from PaddlePaddle Python to new
partitioned IRs and dispatch the new IRs and evaluation inputs/targets
to different PaddlePaddle runtimes. Below are the steps:
1. Add `feed` OP that feeds the eval inputs, and `fetch` OP that
fetches the eval targets to the IR.
1. Extract a new computation (sub)graph with `feed` and `fetch` OP as
the boundary. The runtime does not need to run the OP that is not
dependent by the `fetch` OP.
1. Optimizes the computation graph.
1. Place the OPs in the graph onto different devices on different
PaddlePaddle runtime according to a placement algorithm and device
constraint specified by the user.
1. Partition the graph according to runtime boundaries and add `send` /
`recv` OP pair on the runtime boundaries.
1. Dispatch the partitioned graph to different PaddlePaddle runtimes.
1. PaddlePaddle runtimes with the `fetch` OP reports evaluation
results back to the converter, the convert reports the evaluation
results back to the PaddlePaddle Python.
The output IRs will be cached to optimize the conversion latency.
#### Placement Algorithm
Our first implementation will only support "trainer-parameter server"
placement: the parameters, initializers, and optimizers are placed on
the PaddlePaddle runtimes with the parameter server role. And
everything else will be placed on the PaddlePaddle runtimes with the
trainer role. This has the same functionality of our
"trainer-parameter server" architecture of PaddlePaddle v0.10.0, but
is more general and flexible.
In the future, we will implement the general placement algorithm,
which makes placements according to the input IR, and a model of
device computation time and device communication time. Model
parallelism requires the general placement algorithm.
### PaddlePaddle Runtime
The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and
runs the IR. The runtime does not need to do OP placement since it's
already done by the converter.
### Local Training Architecture
The local training architecture will be the same as the distributed
training architecture, the differences are everything runs locally,
and there is just one PaddlePaddle runtime:
<img src="src/local_architecture.png"/>
### Training Data
In PaddlePaddle v0.10.0, training data is typically read
with [data reader](../reader/README.md) from Python. This approach is
no longer efficient when training distributedly since the Python
process no longer runs on the same node with the trainer processes,
the Python reader will need to read from the distributed filesystem
(assuming it has the access) and send to the trainers, doubling the
network traffic.
When doing distributed training, the user can still use Python data
reader: the training data are sent with `session.eval`. However should
be used for debugging purpose only. The users are encouraged to use
the read data OPs.
## References:
[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
[2] [TensorFlow: A System for Large-Scale Machine Learning](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf)
doc/faq/index_cn.rst
浏览文件 @ 49697d9d
......@@ -251,7 +251,7 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字
那么用户需要拉取所有的远程分支到本机，命令为 :code:`git fetch upstream`，然后重新cmake即可。
12. A protocol message was rejected because it was too big
----------------------------------------------------------
------------------------------------------------------------
如果在训练NLP相关模型时，出现以下错误：
......@@ -316,10 +316,42 @@ Paddle二进制在运行时捕获了浮点数异常，只要出现浮点数异
* 模型一直不收敛，发散到了一个数值特别大的地方。
* 训练数据有问题，导致参数收敛到了一些奇异的情况。或者输入数据尺度过大，有些特征的取值达到数百万，这时进行矩阵乘法运算就可能导致浮点数溢出。
主要的解决办法是减小学习率或者对数据进行归一化处理。
这里有两种有效的解决方法：
1. 设置 :code:`gradient_clipping_threshold` 参数，示例代码如下：
.. code-block:: python
optimizer = paddle.optimizer.RMSProp(
learning_rate=1e-3,
gradient_clipping_threshold=10.0,
regularization=paddle.optimizer.L2Regularization(rate=8e-4))
具体可以参考 `nmt_without_attention <https://github.com/PaddlePaddle/models/blob/develop/nmt_without_attention/train.py#L35>`_ 示例。
2. 设置 :code:`error_clipping_threshold` 参数，示例代码如下：
.. code-block:: python
decoder_inputs = paddle.layer.fc(
act=paddle.activation.Linear(),
size=decoder_size * 3,
bias_attr=False,
input=[context, current_word],
layer_attr=paddle.attr.ExtraLayerAttribute(
error_clipping_threshold=100.0))
完整代码可以参考示例 `machine translation <https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/train.py#L66>`_ 。
两种方法的区别：
1. 两者都是对梯度的截断，但截断时机不同，前者在 :code:`optimzier` 更新网络参数时应用；后者在激活函数反向计算时被调用；
2. 截断对象不同：前者截断可学习参数的梯度，后者截断回传给前层的梯度;
除此之外，还可以通过减小学习律或者对数据进行归一化处理来解决这类问题。
15. 编译安装后执行 import paddle.v2 as paddle 报ImportError: No module named v2
------------------------------------------------------------------------
------------------------------------------------------------------------------------------
先查看一下是否曾经安装过paddle v1版本，有的话需要先卸载：
pip uninstall py_paddle paddle
......@@ -329,7 +361,7 @@ pip uninstall py_paddle paddle
pip install python/dist/paddle*.whl && pip install ../paddle/dist/py_paddle*.whl
16. PaddlePaddle存储的参数格式是什么，如何和明文进行相互转化
---------------------------------------------------------
---------------------------------------------------------------------
PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数两部分组成。头信息中，1~4字节表示PaddlePaddle版本信息，请直接填充0；5~8字节表示每个参数占用的字节数，当保存的网络参数为float类型时为4，double类型时为8；9~16字节表示保存的参数总个数。
......@@ -381,7 +413,7 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
parameters.set('emb', load_parameter(emb_param_file, 30000, 256))
18. 集群多节点训练，日志中保存均为网络通信类错误
------------------------------
-----------------------------------------------------------
集群多节点训练，日志报错为网络通信类错误，比如 :code:`Connection reset by peer` 等。
此类报错通常是由于某一个节点的错误导致这个节点的训练进程退出，从而引发其他节点无法连接导致，可以参考下面的步骤排查：
......@@ -392,8 +424,8 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
* 如果当前MPI集群并不支持任务独占模式，可以联系OP是否可以更换集群或升级当前集群。
19. PaddlePaddle如何输出多个层
------------------------------
19. 如何调用 infer 接口输出多个layer的预测结果
-----------------------------------------------------------
* 将需要输出的层作为 :code:`paddle.inference.Inference()` 接口的 :code:`output_layer` 参数输入，代码如下：
......@@ -405,9 +437,28 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
.. code-block:: python
out = inferer.infer(input=data_batch, flatten_result=False, field=["value"])
out = inferer.infer(input=data_batch, field=["value"])
需要注意的是：
* 如果指定了2个layer作为输出层，实际上需要的输出结果是两个矩阵；
* 假设第一个layer的输出A是一个 N1 * M1 的矩阵，第二个 Layer 的输出B是一个 N2 * M2 的矩阵；
* paddle.v2 默认会将A和B 横向拼接，当N1 和 N2 大小不一样时，会报如下的错误：
.. code-block:: python
ValueError: all the input array dimensions except for the concatenation axis must match exactly
多个层的输出矩阵的高度不一致导致拼接失败，这种情况常常发生在：
这里设置 :code:`flatten_result=False`，得到的输出结果是元素个数等于输出字段数的 :code:`list`，该 :code:`list` 的每个元素是由所有输出层相应字段结果组成的 :code:`list`，每个字段结果的类型是 :code:`numpy.array`。:code:`flatten_result` 的默认值为 :code:`True`，该情况下，PaddlePaddle会分别对每个字段将所有输出层的结果按行进行拼接，如果各输出层该字段 :code:`numpy.array` 结果的相应维数不匹配，程序将不能正常运行。
* 同时输出序列层和非序列层；
* 多个输出层处理多个不同长度的序列;
此时可以在调用infer接口时通过设置 :code:`flatten_result=False` , 跳过“拼接”步骤，来解决上面的问题。这时，infer接口的返回值是一个python list:
* list 中元素的个数等于网络中输出层的个数；
* list 中每个元素是一个layer的输出结果矩阵，类型是numpy的ndarray；
* 每一个layer输出矩阵的高度，在非序列输入时：等于样本数；序列输入时等于：输入序列中元素的总数；宽度等于配置中layer的size；
20. :code:`paddle.layer.memory` 的参数 :code:`name` 如何使用
-------------------------------------------------------------
......@@ -416,8 +467,8 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
* PaddlePaddle的所有layer都有唯一的name，用户通过参数 :code:`name` 设定，当用户没有显式设定时，PaddlePaddle会自动设定。而 :code:`paddle.layer.memory` 不是真正的layer，其name由参数 :code:`memory_name` 设定，当用户没有显式设定时，PaddlePaddle会自动设定。:code:`paddle.layer.memory` 的参数 :code:`name` 用于指定其要关联的layer，需要用户显式设定。
21. dropout 使用
-----------------
21. 两种使用 drop_out 的方法有何区别？
-----------------------------------------------------
* 在PaddlePaddle中使用dropout有两种方式
......@@ -512,3 +563,30 @@ PaddlePaddle目前支持8种learning_rate_schedule，这8种learning_rate_schedu
出现该错误的原因一般是用户对不同layer的参数 :code:`name` 设置了相同的取值。遇到该错误时，先找出参数 :code:`name` 取值相同的layer，然后将这些layer的参数 :code:`name` 设置为不同的值。
24. PaddlePaddle 中不同的 recurrent layer 的区别
--------------------------------------------------
以LSTM为例，在PaddlePaddle中包含以下 recurrent layer：
* :code:`paddle.layer.lstmemory`
* :code:`paddle.networks.simple_lstm`
* :code:`paddle.networks.lstmemory_group`
* :code:`paddle.networks.bidirectional_lstm`
按照具体实现方式可以归纳为2类：
1. 由 recurrent_group 实现的 recurrent layer：
* 用户在使用这一类recurrent layer时，可以访问由recurrent unit在一个时间步内计算得到的中间值（例如：hidden states, memory cells等）；
* 上述的 :code:`paddle.networks.lstmemory_group` 是这一类的 recurrent layer ；
2. 将recurrent layer作为一个整体来实现：
* 用户在使用这一类recurrent layer，只能访问它们的输出值；
* 上述的 :code:`paddle.networks.lstmemory_group` 、 :code:`paddle.networks.simple_lstm` 和 :code:`paddle.networks.bidirectional_lstm` 属于这一类的实现；
将recurrent layer作为一个整体来实现， 能够针对CPU和GPU的计算做更多优化， 所以相比于recurrent group的实现方式， 第二类 recurrent layer 计算效率更高。 在实际应用中，如果用户不需要访问LSTM的中间变量，而只需要获得recurrent layer计算的输出，我们建议使用第二类实现。
此外，关于LSTM, PaddlePaddle中还包含 :code:`paddle.networks.lstmemory_unit` 这一计算单元：
* 不同于上述介绍的recurrent layer , :code:`paddle.networks.lstmemory_unit` 定义了LSTM单元在一个时间步内的计算过程，它并不是一个完整的recurrent layer，也不能接收序列数据作为输入；
* :code:`paddle.networks.lstmemory_unit` 只能在recurrent_group中作为step function使用；
doc/getstarted/build_and_install/docker_install_cn.rst
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......@@ -20,7 +20,7 @@ Docker使用入门
docker pull paddlepaddle/paddle:0.10.0
来下载Docker镜像，paddlepaddle/paddle是从官方镜像源Dockerhub.com下载的，推荐国内用户使用ocker.paddlepaddle.org/paddle下载。
来下载Docker镜像，paddlepaddle/paddle是从官方镜像源Dockerhub.com下载的，推荐国内用户使用docker.paddlepaddle.org/paddle下载。
- *容器*： 如果说一个Docker镜像就是一个程序，那容器就是这个程序运行时产生的“进程”。
实际上，一个容器就是一个操作系统的进程，但是是运行在独立的进程空间，文件系统以及网络之上。
......
doc/howto/dev/new_op_en.md
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# How to write a new operator
- [Background](#Background)
- [Implementing C++ Types](#Implementing_C++_Types)
- [Defining ProtoMaker](#Defining_ProtoMaker)
- [Defining Operator](#Defining_Operator)
- [Registering Operator](#Registering_Operator)
- [Compilation](#Compilation)
- [Python Binding](#Python_Binding)
- [Unit Tests](#Unit_Tests)
- [Background](#background)
- [Implementing C++ Types](#implementing-c++-types)
- [Defining ProtoMaker](#defining-protoMaker)
- [Defining Operator](#defining-operator)
- [Registering Operator](#registering-operator)
- [Compilation](#compilation)
- [Python Binding](#python-binding)
- [Unit Tests](#unit-tests)
- [Testing Forward Operators](#testing-forward-operators)
- [Testing Backward Operators](#testing-backward-operators)
- [Compiling and Running](#compiling-and-running)
- [Remarks](#remarks)
## Background
Here are the base types needed. For details, please refer to the design docs.
......@@ -232,4 +235,122 @@ The system will automatically bind to Python and link it to a generated library.
## Unit Tests
Unit tests include comparing a forward operator's implementations on different devices, comparing a backward operator's implementation on different devices, and a scaling test for the backward operator. Here, we introduce the [unit tests for `MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py).
Unit tests for an operator include
1. comparing a forward operator's implementations on different devices,
2. comparing a backward operator's implementation on different devices, and
3. a scaling test for the backward operator.
Here, we introduce the [unit tests for `MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py).
### Testing Forward Operators
A forward operator unit test inherits `unittest.TestCase` and defines metaclass `__metaclass__ = OpTestMeta`. More concrete tests are performed in `OpTestMeta`. Testing a forward operator requires the following:
1. Defining input, output and relevant attributes in `setUp` method.
2. Generating random input data.
3. Implementing the same computation logic in a Python script:
```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
class TestMulOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
```
Get its output, and compare it with the forward operator's own output.
The code above first loads required packages. In addition, we have
- `self.type = "mul" ` defines the type that is identical to what the operator's registered type.
- `self.inputs` defines input, with type `numpy.array` and initializes it.
- `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script.
### Testing Backward Operators
A backward operator unit test inherits `GradientChecker`, which inherits `unittest.TestCase`. As a result, **a backward operator unit test needs to be have the prefix `test_`**.
```python
class TestMulGradOp(GradientChecker):
def setUp(self):
self.op = create_op("mul")
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'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):
# mul op will enlarge the relative error
self.check_grad(
self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
def test_ignore_x(self):
self.check_grad(
self.op,
self.inputs, ["Y"],
"Out",
max_relative_error=0.5,
no_grad_set={"X"})
def test_ignore_y(self):
self.check_grad(
self.op,
self.inputs, ["X"],
"Out",
max_relative_error=0.5,
no_grad_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.
### Compiling and Running
Any new unit testing file of the format `test_*.py` added to the director `python/paddle/v2/framework/tests` is automatically added to the project to compile.
Note that **unlike the compile test for Ops, running unit tests requires compiling the entire project** and requires compiling with flag `WITH_TESTING` on i.e. `cmake paddle_dir -DWITH_TESTING=ON`.
After successfully compiling the project, run the following command to run unit tests:
```bash
make test ARGS="-R test_mul_op -V"
```
Or,
```bash
ctest -R test_mul_op
```
## Remarks
- Every `*_op.h` (if applicable), `*_op.cc`, and `*_op.cu` (if applicable) must be created for a unique Op. Compiling will fail if multiple operators are included per file.
- The type with which an operator is registered needs to be identical to the Op's name. Registering `REGISTER_OP(B, ...)` in `A_op.cc` will cause unit testing failures.
- If the operator does not implement a GPU kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail.
- If multiple operators rely on some shared methods, a file NOT named `*_op.*` can be created to store them, such as `gather.h`.
paddle/framework/attribute.h
浏览文件 @ 49697d9d
......@@ -45,6 +45,21 @@ inline AttrType AttrTypeID() {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
class AttrReader {
public:
explicit AttrReader(const AttributeMap& attrs) : attrs_(attrs) {}
template <typename T>
inline const T& Get(const std::string& name) const {
PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
name);
return boost::get<T>(attrs_.at(name));
}
private:
const AttributeMap& attrs_;
};
// check whether a value(attribute) fit a certain limit
template <typename T>
class GreaterThanChecker {
......
paddle/framework/backward.md
浏览文件 @ 49697d9d
......@@ -2,7 +2,7 @@
## Motivation
In Neural Network, many model is solved by the the backpropagation algorithm(known as BP) at present. Technically it caculates the gradient of the loss function, then distributed back through the networks. Follows the chain rule, so we need a module chains the gradient operators/expressions together with to construct the backward pass. Every forward network needs a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass.
In Neural Network, most models are solved by the backpropagation algorithm(known as **BP**) at present. Technically, BP calculates the gradient of the loss function, then propagates it back through the networks following the chain rule. Hence we need a module that chains the gradient operators/expressions together to construct the backward pass. Every forward network needs a backward network to construct the full computation graph. The operator/expression's backward pass will be generated with respect to the forward pass.
## Implementation
......@@ -24,9 +24,9 @@ A backward network is built up with several backward operators. Backward operato
| **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients |
| **Operator::outputs_** | Outputs | InputGradients |
In most cases, there is a one-to-one correspondence between the forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced.
In most cases, there is a one-to-one relation between the forward and backward operators. These relations are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and to make operators pluggable, the registry mechanism is introduced.
For example, we have got a `mul_op`, and we can register its information and corresponding backward operator by the following macro:
For example, we have `mul_op`, and we can register its information and corresponding backward operator by the following macro:
```cpp
REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
......@@ -48,7 +48,7 @@ The function `BuildGradOp` will sequentially execute following processes:
1. Get the `type_` of given forward operator, and then get the corresponding backward operator's type by looking up the `OpInfoMap`.
2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these, are not necessary for gradient computing.
2. Build two maps named `inputs` and `outputs` to temporarily store backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these, are not necessary for gradient computing.
3. Add forward inputs' gradient variables into map `output`, adding forward outputs' gradient variables into map `input`.
......@@ -56,11 +56,11 @@ The function `BuildGradOp` will sequentially execute following processes:
### Backward Network Building
A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and append them together one by one. There is some corner case need to process specially.
A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and appending them together one by one. There are some corner cases that need special processing.
1. Op
When the input forward network is an Op, return its gradient Operator Immediately. If all of its outputs are in no gradient set, then return a special `NOP`.
When the input forward network is an Op, return its gradient Operator immediately. If all of its outputs are in no gradient set, then return a special `NOP`.
2. NetOp
......@@ -68,33 +68,33 @@ A backward network is a series of backward operators. The main idea of building
3. RnnOp
RnnOp is a nested stepnet operator. Backward module need to recusively call `Backward` for every stepnet.
RnnOp is a nested stepnet operator. Backward module needs to recusively call `Backward` for every stepnet.
4. Sharing Variables
**sharing variables**. As illustrated in the pictures, two operator's share the same variable name of W@GRAD, which will overwrite their sharing input variable.
As illustrated in the figure 1 and figure 2, two operators share the same variable name **W@GRAD**, which will overwrite their shared input variable.
<p align="center">
<img src="./images/duplicate_op.png" width="50%" ><br/>
​ pic 1. Sharing variables in operators.
​ Figure 1. Sharing variables in operators.
</p>
​ Sharing variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator to replace the overwrite links.
​ Sharing variable between operators or same input variable used in multiple operators can lead to duplicate gradient variables. As illustrated in figure 2, we need to rename the gradient names recursively and add a generic add operator to prevent overwriting.
<p align="center">
<img src="images/duplicate_op2.png" width="40%" ><br/>
​ pic 2. Replace sharing variable's gradient with `Add` operator.
​ Figure 2. Replace sharing variable's gradient with `Add` operator.
</p>
​ Because our framework finds variables accord to their names, we need to rename the output links. We add a suffix of number to represent its position in clockwise.
​ Because the framework finds variables according to their names, we need to rename the output links. We add an integer suffix to represent its position in the clockwise direction.
5. Part of Gradient is Zero.
5. Part of the Gradient is Zero.
In the whole graph, there is some case of that one operator's gradient is not needed, but its input's gradient is a dependency link of other operator, we need to fill a same shape gradient matrix in the position. In our implement, we insert a special `fillZeroLike` operator.
In the whole graph, there is some case of that one operator's gradient is not needed, but its input's gradient is a dependency link of other operator, we need to fill a same shape gradient matrix in the position. In our implementation, we insert a special `fillZeroLike` operator.
Follow these rules above, then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it.
paddle/framework/operator.cc
浏览文件 @ 49697d9d
......@@ -14,7 +14,6 @@ limitations under the License. */
#include "paddle/framework/operator.h"
#include <algorithm>
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
......@@ -33,6 +32,24 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
const Tensor* GetTensorFromVar(const Variable* var) {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
Tensor* GetTensorFromVar(Variable* var) {
if (var->IsType<LoDTensor>()) {
return var->GetMutable<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return var->GetMutable<Tensor>();
}
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
......
paddle/framework/operator.h
浏览文件 @ 49697d9d
......@@ -24,6 +24,7 @@ limitations under the License. */
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/shape_inference.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/place.h"
......@@ -56,6 +57,9 @@ class OperatorBase;
class InferShapeContext;
class ExecutionContext;
extern const Tensor* GetTensorFromVar(const Variable* var);
extern Tensor* GetTensorFromVar(Variable* var);
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -262,15 +266,6 @@ class InferShapeContext {
return res;
}
const Tensor* GetTensorFromVar(const Variable* var) const {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input(%s) must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) const {
PADDLE_ENFORCE_LT(i, InputSize(in));
......@@ -340,6 +335,78 @@ class ExecutionContext : public InferShapeContext {
const platform::DeviceContext& device_context_;
};
class RuntimeInferShapeContext : public InferShapeContextBase {
public:
RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
: op_(op), scope_(scope) {}
bool HasInput(const std::string& name) const {
auto ipt = op_.Input(name);
auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
return var != nullptr;
}
bool HasOutput(const std::string& name) const {
auto ipt = op_.Output(name);
auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
return var != nullptr;
}
DDim GetInputDim(const std::string& name) const {
return GetDim(op_.Input(name));
}
void SetInputDim(const std::string& name, const DDim& dim) {
SetDim(op_.Input(name), dim);
}
DDim GetOutputDim(const std::string& name) const {
return GetDim(op_.Output(name));
}
void SetOutputDim(const std::string& name, const DDim& dim) {
SetDim(op_.Output(name), dim);
}
AttrReader Attrs() const { return AttrReader(op_.Attrs()); }
const std::vector<std::string>& Inputs(const std::string& name) const {
return op_.Inputs(name);
}
const std::vector<std::string>& Outputs(const std::string& name) const {
return op_.Outputs(name);
}
private:
template <bool Allocate>
Tensor* GetTensor(const std::string& name) const {
Tensor* t = nullptr;
auto* var = scope_.FindVar(name);
if (!var->IsType<LoDTensor>() && !var->IsType<Tensor>()) {
if (Allocate) {
t = var->GetMutable<LoDTensor>();
} else {
PADDLE_THROW("Variable(%s) should be tensor", name);
}
} else {
t = GetTensorFromVar(scope_.FindVar(name));
}
return t;
}
DDim GetDim(const std::string& name) const {
return GetTensor<false>(name)->dims();
}
void SetDim(const std::string& name, const DDim& dim) {
GetTensor<true>(name)->Resize(dim);
}
const OperatorBase& op_;
const Scope& scope_;
};
class OpKernel {
public:
/**
......@@ -383,8 +450,10 @@ class OperatorWithKernel : public OperatorBase {
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
// runtime infershape
void InferShape(const Scope& scope) const override {
InferShape(InferShapeContext(*this, scope));
auto c = RuntimeInferShapeContext(*this, scope);
InferShape(&c);
}
void Run(const Scope& scope,
......@@ -406,7 +475,7 @@ class OperatorWithKernel : public OperatorBase {
}
protected:
virtual void InferShape(const InferShapeContext& ctx) const = 0;
virtual void InferShape(InferShapeContextBase* ctx) const = 0;
};
} // namespace framework
......
paddle/framework/operator_test.cc
浏览文件 @ 49697d9d
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/framework/operator.h"
#include "gtest/gtest.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
......@@ -114,7 +115,7 @@ class OpWithKernelTest : public OperatorWithKernel {
using OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {}
void InferShape(framework::InferShapeContextBase* ctx) const override {}
};
template <typename T1, typename T2>
......
paddle/framework/shape_inference.h 0 → 100644
浏览文件 @ 49697d9d
/* 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/ddim.h"
namespace paddle {
namespace framework {
class InferShapeContextBase {
public:
virtual ~InferShapeContextBase() {}
virtual bool HasInput(const std::string &name) const = 0;
virtual bool HasOutput(const std::string &name) const = 0;
virtual framework::DDim GetInputDim(const std::string &name) const = 0;
std::vector<framework::DDim> GetInputsDim(const std::string &name) const {
const std::vector<std::string> &names = Inputs(name);
return GetDims(names);
}
virtual void SetInputDim(const std::string &name,
const framework::DDim &dim) = 0;
void SetInputsDim(const std::string &name,
const std::vector<framework::DDim> &dims) {
auto &names = Inputs(name);
SetDims(names, dims);
}
virtual framework::DDim GetOutputDim(const std::string &name) const = 0;
std::vector<framework::DDim> GetOutputsDim(const std::string &name) const {
const std::vector<std::string> &names = Outputs(name);
return GetDims(names);
}
virtual void SetOutputDim(const std::string &name, const DDim &dim) = 0;
void SetOutputsDim(const std::string &name,
const std::vector<framework::DDim> &dims) {
auto &names = Outputs(name);
SetDims(names, dims);
}
virtual AttrReader Attrs() const = 0;
virtual const std::vector<std::string> &Inputs(
const std::string &name) const = 0;
virtual const std::vector<std::string> &Outputs(
const std::string &name) const = 0;
// TODO(qiao) implement this function
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const {}
protected:
virtual framework::DDim GetDim(const std::string &name) const = 0;
virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
std::vector<framework::DDim> GetDims(
const std::vector<std::string> &names) const {
std::vector<framework::DDim> ret;
ret.reserve(names.size());
std::transform(
names.begin(), names.end(), std::back_inserter(ret),
[this](const std::string &name) { return this->GetDim(name); });
return ret;
}
void SetDims(const std::vector<std::string> &names,
const std::vector<framework::DDim> &dims) {
size_t length = names.size();
PADDLE_ENFORCE_EQ(length, dims.size());
for (size_t i = 0; i < length; ++i) {
SetDim(names[i], dims[i]);
}
}
};
} // namespace framework
} // namespace paddle
paddle/framework/variable.md
浏览文件 @ 49697d9d
......@@ -7,7 +7,7 @@ Variable is also known as *blob* in MxNet and Caffe2. It is the input and outpu
For the flexibility of a DL system, a variable should be able to contain any typed value -- a tensor in most cases, but could also be some integer IDs or a scope of other variables in the case of RNN.
To use the minimum amount of memory, we'd like that a variable to allocate memory when it has to, or, lazy memory allocation. Let's take the following example:
To use the minimum amount of memory, we would like that a variable allocates memory only when it has to, or, lazy memory allocation. Let's take the following example:
```cpp
Variable vr, v1, v2;
......@@ -38,7 +38,7 @@ This syntax for lazy memory allocation when we call `Randomize` and `Mult`, thos
To make memory allocation lazy, we cannot assume that we know the type held by a variable at definition time. In other words, `class Variable` cannot be a template `template <T> class Variable`.
Because we don't know the type `T`, we cannot save a `T*` as `Variable's` data member. Instead, we save an interface object `Placeholder`, who can return the pointer to the saved object via `Placeholder::Ptr()` as `void*`.
Because we don't know the type `T`, we cannot save a `T*` as `Variable's` data member. Instead, we save an interface object `Placeholder`, which can return the pointer to the saved object via `Placeholder::Ptr()` as `void*`.
But anyway, Variable needs to know `T` so could it `delete<T>(ptr)` and so could `Variable::Get` checks the expected type and the saved object's type.
......@@ -49,4 +49,4 @@ Because `PlaceholderImpl` knows `T`, it can save and return `typeid(T)` for the
## Conclusion
The technique type hiding utilizes C++ class templates, interface and derivation, and C++ RTTI (typeid). This combination saves us from definition something like `caffe2::TypeMata`, which takes hundreds of lines of C++ code.
The technique type hiding utilizes C++ class templates, interface and derivation, and C++ RTTI (typeid). This combination saves us from defining something like `caffe2::TypeMeta`, which takes hundreds of lines of C++ code.
paddle/gserver/activations/MKLDNNActivation.cpp
浏览文件 @ 49697d9d
......@@ -27,31 +27,53 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
#define MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) mkldnn_##ACT_TYPE##Activation
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
* @def BEGIN_MKLDNN_ACTIVATION
*/
#define BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) : public BASE_CLASS {
/**
* @def END_MKLDNN_ACTIVATION
*/
#define DEFINE_MKLDNN_ELTWISE_ACTIVATION(ACT_TYPE, ALPHA, BWD_ALPHA) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) \
: public MKLDNNEltwiseActivation { \
private: \
#define END_MKLDNN_ACTIVATION(ACT_TYPE) \
private: \
static const std::string name; \
static const float alpha; \
static const float bwdAlpha; \
\
public: \
public: \
const std::string& getName() const { return name; } \
float getAlpha() const { return alpha; } \
float getBwdAlpha() const { return bwdAlpha; } \
}; \
} \
; \
const std::string MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::name = \
"mkldnn_" #ACT_TYPE; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::alpha = ALPHA; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::bwdAlpha = BWD_ALPHA; \
static InitFunction __reg_activation__mkldnn_##ACT_TYPE([] { \
gMKLDNNActivationRegistrar \
.registerClass<MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)>( \
"mkldnn_" #ACT_TYPE); \
});
/**
* @def DEFINE_MKLDNN_ACTIVATION
*/
#define DEFINE_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
END_MKLDNN_ACTIVATION(ACT_TYPE)
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
*/
#define DEFINE_MKLDNN_ELTWISE_ACTIVATION( \
ACT_TYPE, BASE_CLASS, ALPHA, BWD_ALPHA) \
BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS) \
private: \
static const float alpha; \
static const float bwdAlpha; \
\
public: \
float getAlpha() const { return alpha; } \
float getBwdAlpha() const { return bwdAlpha; } \
END_MKLDNN_ACTIVATION(ACT_TYPE) \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::alpha = ALPHA; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::bwdAlpha = BWD_ALPHA;
/**
* @brief MKLDNN Relu Activation.
* Actually mkldnn_relu is Leaky Relu.
......@@ -59,19 +81,129 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
* f(x) = negative_slope * x (x < 0)
* @note the negative_slope should be -0.f in forward
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(relu, -0.f, 0.f)
DEFINE_MKLDNN_ELTWISE_ACTIVATION(relu, MKLDNNEltwiseActivation, -0.f, 0.f)
/**
* @brief MKLDNN Tanh Activation.
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(tanh, 0.f, 0.f)
DEFINE_MKLDNN_ELTWISE_ACTIVATION(tanh, MKLDNNEltwiseActivation, 0.f, 0.f)
/**
* @brief MKLDNN ELU(Exponential Linear Unit) Activation.
* f(x) = x (x >= 0)
* f(x) = negative_slope * (exp(x) - 1) (x < 0)
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(elu, 0.f, 0.f)
DEFINE_MKLDNN_ELTWISE_ACTIVATION(elu, MKLDNNEltwiseActivation, 0.f, 0.f)
mkldnn::algorithm MKLDNNEltwiseActivation::getAlgo(std::string type) const {
const std::map<std::string, mkldnn::algorithm> algoMap = {
{"relu", algorithm::eltwise_relu},
{"tanh", algorithm::eltwise_tanh},
{"elu", algorithm::eltwise_elu}};
type.erase(0, 7); // remove mkldnn_
algorithm algo = (algorithm)0;
mapGet(type, algoMap, &algo);
return algo;
}
void MKLDNNEltwiseActivation::resetFwd(Argument& act) {
if (cnt_ == act.value->getElementCnt()) {
return;
}
MKLDNNActivation::resetFwd(act);
// note: alpha represents the NegativeSlope when used in relu.
float alpha = getAlpha();
float beta = getBeta();
algorithm algo = getAlgo(this->getName());
auto fwdDesc = eltwise_fwd::desc(mkldnn::prop_kind::forward_training,
algo,
val_->getMemoryDesc(),
alpha,
beta);
fwdPD_.reset(new eltwise_fwd::primitive_desc(fwdDesc, *engine_));
// use inplace for forward but save input value before submit
inVal_ = val_;
copyInVal_ = nullptr;
if (act.grad && algo == algorithm::eltwise_tanh) {
// tanh need save src input for backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";
pipelineFwd_.push_back(*copyInVal_);
}
fwd_.reset(new eltwise_fwd(*fwdPD_, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
needResetBwd_ = true;
}
void MKLDNNEltwiseActivation::resetBwd(Argument& act) {
if (!needResetBwd_) {
return;
}
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
needResetBwd_ = false;
algorithm algo = getAlgo(this->getName());
float alpha = getBwdAlpha();
float beta = getBeta();
grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
auto eng = CPUEngine::Instance().getEngine();
auto bwdDesc = eltwise_bwd::desc(
algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
auto bwdPD = eltwise_bwd::primitive_desc(bwdDesc, eng, *fwdPD_);
CHECK(inVal_);
bwd_.reset(new eltwise_bwd(bwdPD, *inVal_, *grad_, *grad_));
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwd_);
}
/**
* @brief MKLDNN Softmax Activation
*/
DEFINE_MKLDNN_ACTIVATION(softmax, MKLDNNSoftmaxActivation)
void MKLDNNSoftmaxActivation::resetFwd(Argument& act) {
if (cnt_ == act.value->getElementCnt()) {
return;
}
MKLDNNActivation::resetFwd(act);
int axis = 1;
auto fwdDesc = softmax_fwd::desc(
mkldnn::prop_kind::forward_scoring, val_->getMemoryDesc(), axis);
auto fwdPD = softmax_fwd::primitive_desc(fwdDesc, *engine_);
fwd_.reset(new softmax_fwd(fwdPD, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
}
Error __must_check MKLDNNSoftmaxActivation::forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
real* v = act.value->getData();
real threshold = exp(-64);
#pragma omp parallel for
for (size_t i = 0; i < act.value->getElementCnt(); ++i) {
v[i] = v[i] < threshold ? threshold : v[i];
}
return Error();
}
Error __must_check MKLDNNSoftmaxActivation::backward(Argument& act) {
MatrixPtr outputV = act.value;
MatrixPtr outputG = act.grad;
Matrix::resizeOrCreate(sftMaxDot_,
outputG->getHeight(),
outputG->getWidth(),
/* trans */ false,
/* useGpu */ false);
Matrix::resizeOrCreate(sftMaxSum_,
outputG->getHeight(),
1,
/* trans */ false,
/* useGpu */ false);
sftMaxDot_->dotMul(*outputG, *outputV);
sftMaxSum_->colMerge(*sftMaxDot_);
act.grad->softmaxDerivative(*act.value, *sftMaxSum_);
return Error();
}
ActivationFunction* MKLDNNActivation::create(const std::string& type) {
return gMKLDNNActivationRegistrar.createByType(type);
......@@ -84,4 +216,34 @@ std::vector<std::string> MKLDNNActivation::getAllRegisteredTypes() {
return types;
}
void MKLDNNActivation::resetFwd(Argument& act) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
cnt_ = act.value->getElementCnt();
pipelineFwd_.clear();
stream_.reset(new MKLDNNStream());
engine_.reset(new mkldnn::engine(mkldnn::engine::cpu, 0));
val_ = std::dynamic_pointer_cast<MKLDNNMatrix>(act.value);
if (val_ == nullptr) {
int bs = act.getBatchSize();
int ih = act.getFrameHeight() > 0 ? act.getFrameHeight() : 1;
int iw = act.getFrameWidth() > 0 ? act.getFrameWidth() : 1;
int ic = cnt_ / bs / ih / iw;
CHECK_EQ(cnt_, (size_t)bs * ic * ih * iw);
val_ = MKLDNNMatrix::create(
act.value, {bs, ic, ih, iw}, mkldnn::memory::format::nchw, *engine_);
CHECK(val_);
val_->downSpatial();
}
}
Error __must_check MKLDNNActivation::forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
return Error();
}
Error __must_check MKLDNNActivation::backward(Argument& act) {
resetBwd(act);
stream_->submit(pipelineBwd_);
return Error();
}
} // namespace paddle
paddle/gserver/activations/MKLDNNActivation.h
浏览文件 @ 49697d9d
......@@ -36,6 +36,7 @@ protected:
// mkldnn matrix, primitive, stream and pipeline
MKLDNNMatrixPtr val_;
MKLDNNMatrixPtr grad_;
std::shared_ptr<mkldnn::engine> engine_;
std::shared_ptr<MKLDNNStream> stream_;
std::shared_ptr<mkldnn::primitive> fwd_;
std::shared_ptr<mkldnn::primitive> bwd_;
......@@ -48,8 +49,18 @@ public:
static ActivationFunction* create(const std::string& type);
static std::vector<std::string> getAllRegisteredTypes();
virtual const std::string& getName() const = 0;
virtual Error __must_check forward(Argument& act) = 0;
virtual Error __must_check backward(Argument& act) = 0;
/**
* reset the forward primitives
*/
virtual void resetFwd(Argument& act);
/**
* reset the backward primitives,
* can not merge this functions into resetFwd as the grad data
* would be changing before backward.
*/
virtual void resetBwd(Argument& act) {}
virtual Error __must_check forward(Argument& act);
virtual Error __must_check backward(Argument& act);
};
/**
......@@ -59,6 +70,7 @@ public:
class MKLDNNEltwiseActivation : public MKLDNNActivation {
typedef mkldnn::eltwise_forward eltwise_fwd;
typedef mkldnn::eltwise_backward eltwise_bwd;
typedef mkldnn::algorithm algorithm;
protected:
// save the forward primitive desc, which can be used backward
......@@ -70,9 +82,7 @@ protected:
public:
MKLDNNEltwiseActivation() {}
~MKLDNNEltwiseActivation() {}
virtual const std::string& getName() const = 0;
// in common, the alpha of forward and backward should be equal.
......@@ -80,105 +90,30 @@ public:
virtual float getAlpha() const = 0;
virtual float getBwdAlpha() const = 0;
virtual float getBeta() const { return 0.f; }
virtual mkldnn::algorithm getAlgo(const std::string& type) const {
if (type == "mkldnn_relu") {
return mkldnn::algorithm::eltwise_relu;
} else if (type == "mkldnn_tanh") {
return mkldnn::algorithm::eltwise_tanh;
} else if (type == "mkldnn_elu") {
return mkldnn::algorithm::eltwise_elu;
} else {
LOG(FATAL) << "Unkown eltwise activation type: " << type;
}
return (mkldnn::algorithm)0;
}
/**
* reshape and reset the forward primitives
*/
void resetFwd(Argument& act) {
if (cnt_ == act.value->getElementCnt()) {
return;
}
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
cnt_ = act.value->getElementCnt();
stream_.reset(new MKLDNNStream());
auto eng = CPUEngine::Instance().getEngine();
// get algo setting
mkldnn::algorithm algo = getAlgo(this->getName());
// note: alpha represents the NegativeSlope when used in relu.
float alpha = getAlpha();
float beta = getBeta();
pipelineFwd_.clear();
val_ = std::dynamic_pointer_cast<MKLDNNMatrix>(act.value);
if (val_ == nullptr) {
int bs = act.getBatchSize();
int ih = act.getFrameHeight() > 0 ? act.getFrameHeight() : 1;
int iw = act.getFrameWidth() > 0 ? act.getFrameWidth() : 1;
int ic = cnt_ / bs / ih / iw;
CHECK_EQ(cnt_, (size_t)bs * ic * ih * iw);
val_ = MKLDNNMatrix::create(
act.value, {bs, ic, ih, iw}, mkldnn::memory::format::nchw, eng);
CHECK(val_);
}
auto fwdDesc = eltwise_fwd::desc(mkldnn::prop_kind::forward_training,
algo,
val_->getMemoryDesc(),
alpha,
beta);
fwdPD_.reset(new eltwise_fwd::primitive_desc(fwdDesc, eng));
// use inplace for forward but save input value before submit
inVal_ = val_;
copyInVal_ = nullptr;
if (act.grad && algo == mkldnn::algorithm::eltwise_tanh) {
// tanh need save src input for backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";
pipelineFwd_.push_back(*copyInVal_);
}
fwd_.reset(new eltwise_fwd(*fwdPD_, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
needResetBwd_ = true;
}
virtual algorithm getAlgo(std::string type) const;
void resetFwd(Argument& act) override;
void resetBwd(Argument& act) override;
};
/**
* reset the backward primitives, can not merge into resetFwd as the grad data
* would be changing before backward.
/**
* @brief Base class of MKLDNN softmax Activation,
* only have mkldnn forward, use cpu implement for backward.
*/
void resetBwd(Argument& act) {
if (!needResetBwd_) {
return;
}
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
needResetBwd_ = false;
mkldnn::algorithm algo = getAlgo(this->getName());
float alpha = getBwdAlpha();
float beta = getBeta();
grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
auto eng = CPUEngine::Instance().getEngine();
auto bwdDesc = eltwise_bwd::desc(
algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
auto bwdPD = eltwise_bwd::primitive_desc(bwdDesc, eng, *fwdPD_);
CHECK(inVal_);
bwd_.reset(new eltwise_bwd(bwdPD, *inVal_, *grad_, *grad_));
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwd_);
}
class MKLDNNSoftmaxActivation : public MKLDNNActivation {
typedef mkldnn::softmax_forward softmax_fwd;
Error __must_check forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
return Error();
}
private:
// for backward
MatrixPtr sftMaxSum_;
MatrixPtr sftMaxDot_;
Error __must_check backward(Argument& act) {
resetBwd(act);
stream_->submit(pipelineBwd_);
return Error();
}
public:
MKLDNNSoftmaxActivation() {}
~MKLDNNSoftmaxActivation() {}
virtual const std::string& getName() const = 0;
void resetFwd(Argument& act) override;
Error __must_check forward(Argument& act) override;
Error __must_check backward(Argument& act) override;
};
} // namespace paddle
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 49697d9d
......@@ -222,8 +222,8 @@ static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) {
}
void testActivation(std::string& actType, const testActDesc& pm) {
// TODO(TJ): mkldnn_softmax not implemented, paddle do not have elu activation
if (actType == "mkldnn_softmax" || actType == "mkldnn_elu") {
// TODO(TJ): remove me when paddle support elu activation
if (actType == "mkldnn_elu") {
return;
}
const std::string compareTypes[] = {actType, actType.erase(0, 7)};
......
paddle/operators/accuracy_op.cc
浏览文件 @ 49697d9d
......@@ -22,25 +22,23 @@ class AccuracyOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Inference"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Inference"),
"Input(Inference) of AccuracyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
PADDLE_ENFORCE(ctx->HasInput("Label"),
"Input(Label) of AccuracyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Accuracy"),
PADDLE_ENFORCE(ctx->HasOutput("Accuracy"),
"Output(Accuracy) of AccuracyOp should not be null.");
auto *inference = ctx.Input<framework::Tensor>("Inference");
auto *label = ctx.Input<framework::Tensor>("Label");
auto inference_dim = ctx->GetInputDim("Inference");
auto label_dim = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
PADDLE_ENFORCE_EQ(label_dim.size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference_dim[0], label_dim[0],
"inference size must be the same as label size");
ctx.Output<framework::Tensor>("Accuracy")->Resize({1});
ctx.ShareLoD("Inference", /*->*/ "Accuracy");
ctx->SetOutputDim("Accuracy", {1});
ctx->ShareLoD("Inference", /*->*/ "Accuracy");
}
};
......
paddle/operators/accuracy_op.cu
浏览文件 @ 49697d9d
......@@ -69,8 +69,12 @@ class AccuracyOpCUDAKernel : public framework::OpKernel {
return;
}
AccuracyCudaKernel<PADDLE_CUDA_NUM_THREADS><<<1, PADDLE_CUDA_NUM_THREADS>>>(
num_samples, infer_width, inference_data, label_data, accuracy_data);
AccuracyCudaKernel<PADDLE_CUDA_NUM_THREADS><<<
1, PADDLE_CUDA_NUM_THREADS, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(num_samples, infer_width, inference_data, label_data,
accuracy_data);
}
};
......
paddle/operators/activation_op.cc
浏览文件 @ 49697d9d
......@@ -22,10 +22,9 @@ class ActivationOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>("Y")->Resize(
ctx.Input<framework::Tensor>("X")->dims());
ctx.ShareLoD("X", /*->*/ "Y");
void InferShape(framework::InferShapeContextBase *ctx) const override {
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Y");
}
};
......@@ -34,9 +33,8 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<framework::Tensor>("Y")->dims());
void InferShape(framework::InferShapeContextBase *ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Y"));
}
};
......
paddle/operators/add_op.cc
浏览文件 @ 49697d9d
......@@ -22,25 +22,23 @@ class AddOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of AddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of AddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of AddOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of AddOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of AddOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Two input of Add Op's dimension must be same.");
ctx.Output<framework::Tensor>("Out")->Resize(
ctx.Input<Tensor>("X")->dims());
ctx->SetOutputDim("Out", x_dims);
}
};
class AddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AddOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
AddOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of add op");
AddInput("Y", "The second input of add op");
......@@ -58,7 +56,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {}
void InferShape(framework::InferShapeContextBase* ctx) const override {}
};
} // namespace operators
......
paddle/operators/clip_op.cc
浏览文件 @ 49697d9d
......@@ -22,24 +22,24 @@ class ClipOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ClipOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ClipOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto max = Attr<float>("max");
auto min = Attr<float>("min");
auto x_dims = ctx->GetInputDim("X");
auto max = ctx->Attrs().Get<float>("max");
auto min = ctx->Attrs().Get<float>("min");
PADDLE_ENFORCE_LT(min, max, "max should be greater than min.");
ctx.Output<Tensor>("Out")->Resize(x_dims);
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
template <typename AttrType>
class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ClipOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ClipOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor)The input of clip op."
......@@ -61,14 +61,13 @@ class ClipOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
if (x_grad != nullptr) {
x_grad->Resize(x_dims);
auto x_dims = ctx->GetInputDim("X");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
};
......
paddle/operators/concat_op.cc
浏览文件 @ 49697d9d
......@@ -24,31 +24,30 @@ class ConcatOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ConcatOp should not be null.");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
auto ins = ctx->GetInputsDim("X");
size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
size_t n = ins.size();
PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1.");
auto out_dims = ins[0]->dims();
auto out_dims = ins[0];
size_t in_zero_dims_size = out_dims.size();
for (size_t i = 1; i < n; i++) {
for (size_t j = 0; j < in_zero_dims_size; j++) {
if (j == axis) {
out_dims[axis] += ins[i]->dims()[j];
out_dims[axis] += ins[i][j];
continue;
}
PADDLE_ENFORCE_EQ(out_dims[j], ins[i]->dims()[j],
PADDLE_ENFORCE_EQ(out_dims[j], ins[i][j],
"Input tensors should have the same "
"elements except the specify axis.")
}
}
out->Resize(out_dims);
ctx->SetOutputDim("Out", out_dims);
}
};
......
paddle/operators/conv2d_op.cc
浏览文件 @ 49697d9d
......@@ -27,27 +27,25 @@ class Conv2DOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Input"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of Conv2DOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Filter"),
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of Conv2DOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Output"),
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of Conv2DOp should not be null.");
auto in = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto out = ctx.Output<framework::Tensor>("Output");
std::vector<int> strides = Attr<std::vector<int>>("strides");
std::vector<int> paddings = Attr<std::vector<int>>("paddings");
int groups = Attr<int>("groups");
int input_channels = in->dims()[1];
int output_channels = filter->dims()[0];
PADDLE_ENFORCE_EQ(in->dims().size(), 4, "Conv2DOp input should be 4-D.");
PADDLE_ENFORCE_EQ(filter->dims().size(), 4,
"Conv2DOp filter should be 4-D.");
PADDLE_ENFORCE_EQ(input_channels, filter->dims()[1] * groups,
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
"The number of input channels should be equal to filter "
"channels * groups.");
PADDLE_ENFORCE_EQ(
......@@ -55,17 +53,17 @@ class Conv2DOp : public framework::OperatorWithKernel {
"The number of output channels should be divided by groups.");
auto output_height =
outputSize(in->dims()[2], filter->dims()[2], paddings[0], strides[0]);
outputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
auto output_width =
outputSize(in->dims()[3], filter->dims()[3], paddings[1], strides[1]);
out->Resize(
{in->dims()[0], filter->dims()[0], output_height, output_width});
outputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
ctx->SetOutputDim(
"Output", {in_dims[0], filter_dims[0], output_height, output_width});
}
};
class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
Conv2DOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
......@@ -108,14 +106,15 @@ class Conv2DOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto in = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto d_in = ctx.Output<framework::Tensor>(framework::GradVarName("Input"));
auto d_filter =
ctx.Output<framework::Tensor>(framework::GradVarName("Filter"));
if (d_in) d_in->Resize(in->dims());
if (d_filter) d_filter->Resize(filter->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
if (ctx->HasOutput(framework::GradVarName("Input"))) {
ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
}
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
}
}
};
......
paddle/operators/cos_sim_op.cc
浏览文件 @ 49697d9d
......@@ -24,22 +24,22 @@ class CosSimOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase* ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("XNorm"),
PADDLE_ENFORCE(ctx->HasOutput("XNorm"),
"Output(XNorm) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("YNorm"),
PADDLE_ENFORCE(ctx->HasOutput("YNorm"),
"Output(YNorm) of CosSimOp should not be null.");
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
......@@ -54,16 +54,16 @@ class CosSimOp : public framework::OperatorWithKernel {
" just 1 (which will be broadcasted to match Input(X)).");
// resize tensor
ctx.Output<framework::Tensor>("Out")->Resize({x_dims[0], 1});
ctx.Output<framework::Tensor>("XNorm")->Resize({x_dims[0], 1});
ctx.Output<framework::Tensor>("YNorm")->Resize({y_dims[0], 1});
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", {x_dims[0], 1});
ctx->SetOutputDim("XNorm", {x_dims[0], 1});
ctx->SetOutputDim("YNorm", {y_dims[0], 1});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
CosSimOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The 1st input of cos_sim op.");
AddInput("Y", "The 2nd input of cos_sim op.");
......@@ -98,27 +98,23 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase* ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
"Input(XNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
"Input(YNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Out"),
"Input(Out) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("XNorm"), "Input(XNorm) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("YNorm"), "Input(YNorm) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
auto out_dims = ctx.Input<Tensor>("Out")->dims();
auto out_grad_dims =
ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto xnorm_dims = ctx->GetInputDim("XNorm");
auto ynorm_dims = ctx->GetInputDim("YNorm");
auto out_dims = ctx->GetInputDim("Out");
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
......@@ -143,10 +139,14 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
"Shape of Input(Out@Grad) must be [X.Dim(0), 1].");
// resize tensor
auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
......
paddle/operators/crop_op.cc
浏览文件 @ 49697d9d
......@@ -25,16 +25,14 @@ class CropOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of CropOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of CropOp should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto *y = ctx.Input<Tensor>("Y");
auto *out = ctx.Output<Tensor>("Out");
if (y == nullptr) {
auto shape = Attr<std::vector<int>>("shape");
auto x_dim = ctx->GetInputDim("X");
if (!ctx->HasInput("Y")) {
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE_EQ(
int64_t(shape.size()), x_dim.size(),
"Shape size should be equal to dimention size of input tensor.");
......@@ -42,19 +40,20 @@ class CropOp : public framework::OperatorWithKernel {
for (size_t i = 0; i < shape.size(); ++i) {
tensor_shape[i] = static_cast<int64_t>(shape[i]);
}
out->Resize(framework::make_ddim(tensor_shape));
ctx->SetOutputDim("Out", framework::make_ddim(tensor_shape));
} else {
PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y->dims()),
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y_dim),
"Tensor rank of both CropOp's "
"inputs must be same.");
out->Resize(y->dims());
ctx->SetOutputDim("Out", y_dim);
}
}
};
class CropOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
CropOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
......@@ -116,14 +115,14 @@ class CropOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
if (x_grad != nullptr) {
x_grad->Resize(x_dims);
auto x_dims = ctx->GetInputDim("X");
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
}
};
......
paddle/operators/cross_entropy_op.cc
浏览文件 @ 49697d9d
......@@ -22,32 +22,30 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"), "Output(Y) must not be null.");
auto x = ctx.Input<Tensor>("X");
auto label = ctx.Input<Tensor>("Label");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 2,
"Input(Label)'s rank must be 2.");
PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
"The 1st dimension of Input(X) and Input(Label) must "
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
if (ctx.Attr<bool>("soft_label")) {
PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
"If Attr(soft_label) == true, The 2nd dimension of "
"Input(X) and Input(Label) must be equal.");
if (ctx->Attrs().Get<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
"If Attr(softLabel) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label->dims()[1], 1,
"If Attr(soft_label) == false, The 2nd dimension of "
"Input(Label) must be 1.");
PADDLE_ENFORCE_EQ(label_dims[1], 1,
"If Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
ctx.Output<Tensor>("Y")->Resize({x->dims()[0], 1});
ctx.ShareLoD("X", /*->*/ "Y");
ctx->SetOutputDim("Y", {x_dims[0], 1});
ctx->ShareLoD("X", /*->*/ "Y");
}
};
......@@ -56,66 +54,79 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
"Input(Y@GRAD) must not be null.");
auto x = ctx.Input<Tensor>("X");
auto label = ctx.Input<Tensor>("Label");
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE_EQ(dy->dims().size(), 2, "Input(Y@Grad)'s rank must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 2,
"Input(Label)'s rank must be 2.");
PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
"The 1st dimension of Input(X) and Input(Label) must "
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) shoudl be not null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x->dims()[0], dy->dims()[0],
"The 1st dimension of Input(X) and Input(Y@Grad) must "
PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal.");
PADDLE_ENFORCE_EQ(dy->dims()[1], 1,
"The 2nd dimension of Input(Y@Grad) must be 1.");
if (ctx.Attr<bool>("soft_label")) {
PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
"If Attr(soft_label) == true, The 2nd dimension of "
"Input(X) and Input(Label) must be equal.");
PADDLE_ENFORCE_EQ(dy_dims[1], 1,
"The 2nd dimension of Input(Y@Grad) should be 1.");
if (ctx->Attrs().Get<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
"When Attr(softLabel) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label->dims()[1], 1,
"If Attr(soft_label) == false, The 2nd dimension of "
"Input(Label) must be 1.");
PADDLE_ENFORCE_EQ(label_dims[1], 1,
"When Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
dx->Resize(x->dims());
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CrossEntropyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
CrossEntropyOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of CrossEntropyOp");
AddInput("Label", "The second input of CrossEntropyOp");
AddOutput("Y", "The output of CrossEntropyOp");
AddAttr<bool>("soft_label", "Is soft label. Default zero.")
AddInput("X",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
"where N is the batch size and D is the number of classes. "
"This input is a probability computed by the previous operator, "
"which is almost always the result of a softmax operator.");
AddInput(
"Label",
"(Tensor, default Tensor<int>), the ground truth which is "
"a 2-D tensor. "
"When softLabel is set to false, `Label` is a Tensor<int> with shape "
"[N x 1]. "
"When softLabel is set to true, `Label` is a Tensor<float/double> "
"with shape [N x K].");
AddOutput("Y",
"(Tensor, default Tensor<float>), a 2-D tensor "
"with shape [N x 1]. The cross entropy loss.");
AddAttr<bool>(
"softLabel",
"(bool, default false), a flag to indicate whether to interpretate "
"the given labels as soft labels.")
.SetDefault(false);
AddComment(R"DOC(
CrossEntropy Operator.
It supports both standard cross-entropy and soft-label cross-entropy loss
computation.
1) One-hot cross-entropy:
soft_label = False, Label[i, 0] indicates the class index for sample i:
softLabel = false, Label[i, 0] indicates the class index for sample i:
Y[i] = -log(X[i, Label[i]])
2) Soft-label cross-entropy:
soft_label = True, Label[i, j] indicates the soft label of class j
softLabel = true, Label[i, j] indicates the soft label of class j
for sample i:
Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
......
paddle/operators/cross_entropy_op.cu
浏览文件 @ 49697d9d
......@@ -28,26 +28,49 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
Y[i] = -tolerable_value(log(X[i * D + label[i]]));
Y[i] = -TolerableValue<T>()(log(X[i * D + label[i]]));
}
}
template <typename T>
__device__ __forceinline__ T sum_single_warp(T val) {
val += __shfl_down(val, 16);
val += __shfl_down(val, 8);
val += __shfl_down(val, 4);
val += __shfl_down(val, 2);
val += __shfl_down(val, 1);
return val;
}
template <typename T>
__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
const int N, const int D) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
T sum = static_cast<T>(0);
for (int j = 0; j < D; j++) {
sum += label[i * D + j] * tolerable_value(log(X[i * D + j]));
const int class_num) {
int tid = threadIdx.x;
extern __shared__ T d_sum[];
d_sum[tid] = 0;
int cur_idx = tid;
int next_idx = blockIdx.x * class_num + tid;
while (cur_idx < class_num) {
d_sum[tid] += TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
next_idx += blockDim.x;
cur_idx += blockDim.x;
}
Y[i] = -sum;
__syncthreads();
for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
if (tid < stride) d_sum[tid] += d_sum[tid + stride];
__syncthreads();
}
T val = d_sum[tid];
val = sum_single_warp<T>(val);
if (tid == 0) Y[blockIdx.x] = -val;
}
// TODO(qingqing): make zero setting an common function.
// TODO(qingqing): make zero setting a common function.
template <typename T>
__global__ void zero(T* X, const int N) {
__global__ void Zero(T* X, const int N) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
X[i] = 0.0;
......@@ -71,13 +94,10 @@ template <typename T>
__global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
const T* label, const int N,
const int D) {
// TOOD(qingqing): optimize for this kernel
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
for (int j = 0; j < D; ++j) {
int idx = i * D + j;
dX[idx] = -label[idx] * dY[i] / X[idx];
}
int ids = blockIdx.x * blockDim.x + threadIdx.x;
if (ids < N * D) {
int row_ids = ids / D;
dX[ids] = -label[ids] * dY[row_ids] / X[ids];
}
}
......@@ -86,29 +106,36 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
"This kernel only runs on GPU device.");
auto x = ctx.Input<Tensor>("X");
auto y = ctx.Output<Tensor>("Y");
auto label = ctx.Input<Tensor>("Label");
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* label = ctx.Input<Tensor>("Label");
Tensor* y = ctx.Output<Tensor>("Y");
auto* x_data = x->data<T>();
y->mutable_data<T>(ctx.GetPlace());
auto* y_data = y->data<T>();
const T* x_data = x->data<T>();
T* y_data = y->mutable_data<T>(ctx.GetPlace());
int n = x->dims()[0];
int d = x->dims()[1];
int block = 512;
int grid = (n + block - 1) / block;
// TODO(qingqing) launch kernel on specified stream
// base on ExecutionContext.
if (ctx.Attr<bool>("soft_label")) {
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
if (ctx.Attr<bool>("softLabel")) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
SoftCrossEntropyKernel<T><<<grid, block>>>(y_data, x_data, label_data, n,
d);
int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
SoftCrossEntropyKernel<
T><<<batch_size, block, block * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(y_data, x_data, label_data, class_num);
} else {
auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
CrossEntropyKernel<T><<<grid, block>>>(y_data, x_data, label_data, n, d);
int block = 512;
int grid = (batch_size + block - 1) / block;
CrossEntropyKernel<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(y_data, x_data, label_data,
batch_size, class_num);
}
}
};
......@@ -118,33 +145,43 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
"This kernel only runs on GPU device.");
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* label = ctx.Input<Tensor>("Label");
Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto x = ctx.Input<Tensor>("X");
auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto label = ctx.Input<Tensor>("Label");
const T* dy_data =
ctx.Input<Tensor>(framework::GradVarName("Y"))->data<T>();
T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
const T* x_data = x->data<T>();
auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
auto* dy_data = dy->data<T>();
auto* x_data = x->data<T>();
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
int n = x->dims()[0];
int d = x->dims()[1];
int block = 512;
int grid = (n * d + block - 1) / block;
zero<T><<<grid, block>>>(dx_data, n * d);
grid = (n + block - 1) / block;
// TODO(qingqing): launch kernel on specified stream
// base on ExecutionContext.
if (ctx.Attr<bool>("soft_label")) {
int grid = (batch_size * class_num + block - 1) / block;
if (ctx.Attr<bool>("softLabel")) {
auto* label_data = label->data<T>();
SoftCrossEntropyGradientKernel<T><<<grid, block>>>(
dx_data, dy_data, x_data, label_data, n, d);
SoftCrossEntropyGradientKernel<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(dx_data, dy_data, x_data, label_data,
batch_size, class_num);
} else {
Zero<T><<<grid, block, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(dx_data, batch_size * class_num);
auto* label_data = label->data<int>();
CrossEntropyGradientKernel<T><<<grid, block>>>(dx_data, dy_data, x_data,
label_data, n, d);
grid = (batch_size + block - 1) / block;
CrossEntropyGradientKernel<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(dx_data, dy_data, x_data, label_data,
batch_size, class_num);
}
}
};
......
paddle/operators/cross_entropy_op.h
浏览文件 @ 49697d9d
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/hostdevice.h"
......@@ -20,53 +21,51 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
HOSTDEVICE T tolerable_value(const T x) {
struct TolerableValue {
HOSTDEVICE T operator()(const T& x) const {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
if (x == INFINITY) {
return kApproInf;
}
if (x == -INFINITY) {
return -kApproInf;
}
if (x == INFINITY) return kApproInf;
if (x == -INFINITY) return -kApproInf;
return x;
}
}
};
template <typename T>
class CrossEntropyOpKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto x = ctx.Input<Tensor>("X");
auto y = ctx.Output<Tensor>("Y");
auto* x_data = x->data<T>();
y->mutable_data<T>(ctx.GetPlace());
auto* y_data = y->data<T>();
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
if (ctx.Attr<bool>("soft_label")) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
int index = 0;
for (int i = 0; i < batch_size; ++i) {
T sum = static_cast<T>(0);
for (int j = 0; j < class_num; ++j) {
sum += label_data[index] * tolerable_value(std::log(x_data[index]));
y_data[i] = -sum;
index++;
}
}
"This kernel only runs on CPU.");
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* labels = ctx.Input<Tensor>("Label");
Tensor* y = ctx.Output<Tensor>("Y");
T* y_data = y->mutable_data<T>(ctx.GetPlace());
const int batch_size = x->dims()[0];
if (ctx.Attr<bool>("softLabel")) {
auto prob = EigenMatrix<T>::From(*x);
auto lbl_mat = EigenMatrix<T>::From(*labels);
auto loss = EigenMatrix<T>::From(*y);
loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
-((lbl_mat * prob.log().unaryExpr(TolerableValue<T>()))
.sum(Eigen::DSizes<int, 1>(1))
.reshape(Eigen::DSizes<int, 2>(batch_size, 1)));
} else {
auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
const int class_num = x->dims()[1];
const T* x_data = x->data<T>();
const int* label_data = labels->data<int>();
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
y_data[i] = -tolerable_value(std::log(x_data[index]));
y_data[i] = -TolerableValue<T>()(std::log(x_data[index]));
}
}
}
......@@ -77,33 +76,32 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto x = ctx.Input<Tensor>("X");
auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto label = ctx.Input<Tensor>("Label");
"This kernel only runs on CPU.");
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
const Tensor* label = ctx.Input<Tensor>("Label");
Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
auto* dy_data = dy->data<T>();
auto* x_data = x->data<T>();
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
// TODO(qingqing): make zero setting an common function.
if (ctx.Attr<bool>("soft_label")) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
int index = 0;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < class_num; ++j) {
dx_data[index] = -label_data[index] * dy_data[i] / x_data[index];
index++;
}
}
if (ctx.Attr<bool>("softLabel")) {
auto x_mat = EigenMatrix<T>::From(*x);
auto dy_mat = EigenMatrix<T>::From(*dy);
auto lbl_mat = EigenMatrix<T>::From(*label);
auto dx_mat = EigenMatrix<T>::From(*dx);
dx_mat.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
-(lbl_mat * dy_mat.broadcast(Eigen::DSizes<int, 2>(1, class_num)) /
x_mat);
} else {
auto* label_data = label->data<int>();
int batch_size = x->dims()[0];
const T* dy_data = dy->data<T>();
const T* x_data = x->data<T>();
const int* label_data = label->data<int>();
// TODO(qingqing): make zero setting a common function.
memset(dx_data, 0, sizeof(T) * batch_size * class_num);
for (int i = 0; i < batch_size; ++i) {
PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);
int index = i * class_num + label_data[i];
......
paddle/operators/dropout_op.cc
浏览文件 @ 49697d9d
......@@ -24,25 +24,25 @@ class DropoutOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_GE(ctx.Attr<float>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx.Attr<float>("dropout_prob"), 1);
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("Out")->Resize(dims);
if (ctx.Attr<bool>("is_training")) {
ctx.Output<Tensor>("Mask")->Resize(dims);
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_GE(ctx->Attrs().Get<float>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx->Attrs().Get<float>("dropout_prob"), 1);
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims);
if (ctx->Attrs().Get<bool>("is_training") == 1) {
ctx->SetOutputDim("Mask", x_dims);
}
ctx.ShareLoD("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
template <typename AttrType>
class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
public:
DropoutOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
DropoutOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<AttrType>("dropout_prob", "Probability of setting units to zero.")
.SetDefault(.5f);
......@@ -70,27 +70,26 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.Attr<bool>("is_training"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_training"), 1,
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Mask"), "Mask must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Mask"), "Mask must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
PADDLE_ENFORCE_GE(ctx.Attr<AttrType>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx.Attr<AttrType>("dropout_prob"), 1);
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_GE(ctx->Attrs().Get<AttrType>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx->Attrs().Get<AttrType>("dropout_prob"), 1);
auto x_dims = ctx->GetInputDim("X");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(x_dims, out_dims,
"Dimensions of Input(X) and Out@Grad must be the same.");
auto mask_dims = ctx.Input<Tensor>("Mask")->dims();
auto mask_dims = ctx->GetInputDim("Mask");
PADDLE_ENFORCE_EQ(x_dims, mask_dims,
"Dimensions of Input(X) and Mask must be the same.");
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
x_grad->Resize(x_dims);
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
......
paddle/operators/elementwise_op.h
浏览文件 @ 49697d9d
......@@ -202,21 +202,20 @@ class ElementwiseOp : public framework::OperatorWithKernel {
protected:
using Tensor = framework::Tensor;
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of elementwise op should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of elementwise op should not be null");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of elementwise op should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto y_dim = ctx.Input<Tensor>("Y")->dims();
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.")
ctx.Output<framework::Tensor>("Out")->Resize(x_dim);
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -284,27 +283,26 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
using Tensor = framework::Tensor;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Rank of first input must >= rank of second input.")
if (x_grad) {
x_grad->Resize(x_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (y_grad) {
y_grad->Resize(y_dims);
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
......
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 49697d9d
......@@ -22,15 +22,13 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of FillZerosLikeOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of FillZerosLikeOp should not be null.");
ctx.Output<framework::Tensor>("Y")->Resize(
ctx.Input<framework::Tensor>("X")->dims());
ctx.ShareLoD("X", /*->*/ "Y");
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Y");
}
};
......
paddle/operators/gather_op.cc
浏览文件 @ 49697d9d
......@@ -23,19 +23,19 @@ class GatherOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GatherOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Index"),
PADDLE_ENFORCE(ctx->HasInput("Index"),
"Input(Index) of GatherOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GatherOp should not be null.");
int batch_size = ctx.Input<Tensor>("Index")->dims()[0];
int batch_size = ctx->GetInputDim("Index")[0];
PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
framework::DDim output_dims(ctx.Input<Tensor>("X")->dims());
framework::DDim output_dims(ctx->GetInputDim("X"));
output_dims[0] = batch_size;
ctx.Output<framework::Tensor>("Out")->Resize(output_dims);
ctx->SetOutputDim("Out", output_dims);
}
};
......@@ -44,17 +44,14 @@ class GatherGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto X_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto X = ctx.Input<Tensor>("X");
X_grad->Resize(X->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
class GatherOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GatherOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
GatherOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The source input of gather op");
AddInput("Index", "The index input of gather op");
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ 49697d9d
......@@ -43,13 +43,10 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GaussianRandomOp should not be null.");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = Attr<std::vector<int>>("dims");
auto dims = ctx->Attrs().Get<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
......@@ -57,7 +54,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
}
PADDLE_ENFORCE(dims.size() > 0UL,
"dims can be one int or array. dims must be set.");
tensor->Resize(framework::make_ddim(temp));
ctx->SetOutputDim("Out", framework::make_ddim(temp));
}
};
......
paddle/operators/lookup_table_op.cc
浏览文件 @ 49697d9d
......@@ -22,27 +22,26 @@ class LookupTableOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("W"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("W"),
"Input(W) of LookupTableOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ids"),
PADDLE_ENFORCE(ctx->HasInput("Ids"),
"Input(Ids) of LookupTableOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of LookupTableOp should not be null.");
auto table_t = ctx.Input<Tensor>("W");
auto ids_t = ctx.Input<Tensor>("Ids");
auto output_t = ctx.Output<framework::Tensor>("Out");
auto table_dims = ctx->GetInputDim("W");
auto ids_dims = ctx->GetInputDim("Ids");
output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
ctx.ShareLoD("Ids", /*->*/ "Out");
ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
ctx->ShareLoD("Ids", /*->*/ "Out");
}
};
class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LookupTableOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LookupTableOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("W",
"An input represents embedding tensors,"
......@@ -66,11 +65,9 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &context) const override {
auto table = context.Input<Tensor>("W");
auto d_table =
context.Output<framework::Tensor>(framework::GradVarName("W"));
d_table->Resize(table->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto table_dims = ctx->GetInputDim("W");
ctx->SetOutputDim(framework::GradVarName("W"), table_dims);
}
};
......
paddle/operators/lookup_table_op.cu
浏览文件 @ 49697d9d
......@@ -77,7 +77,10 @@ class LookupTableCUDAKernel : public framework::OpKernel {
dim3 threads(128, 8);
dim3 grids(8, 1);
LookupTable<T, 128, 8, 8><<<grids, threads>>>(output, table, ids, N, K, D);
LookupTable<T, 128, 8, 8><<<
grids, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
context.device_context())
.stream()>>>(output, table, ids, N, K, D);
}
};
......@@ -102,8 +105,10 @@ class LookupTableGradCUDAKernel : public framework::OpKernel {
dim3 threads(128, 8);
dim3 grids(8, 1);
LookupTableGrad<T, 128, 8, 8><<<grids, threads>>>(d_table, d_output, ids, N,
K, D);
LookupTableGrad<T, 128, 8, 8><<<
grids, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
context.device_context())
.stream()>>>(d_table, d_output, ids, N, K, D);
}
};
......
paddle/operators/lstm_unit_op.cc
浏览文件 @ 49697d9d
......@@ -22,37 +22,36 @@ class LstmUnitOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of LSTM should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("C_prev"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("C_prev"),
"Input(C_prev) of LSTM should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("C"),
PADDLE_ENFORCE(ctx->HasOutput("C"),
"Output(C) of LSTM should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("H"),
PADDLE_ENFORCE(ctx->HasOutput("H"),
"Output(H) of LSTM should not be null.");
auto *x = ctx.Input<framework::Tensor>("X");
auto *c_prev = ctx.Input<framework::Tensor>("C_prev");
auto x_dims = ctx->GetInputDim("X");
auto c_prev_dims = ctx->GetInputDim("C_prev");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE(x->dims()[0] == c_prev->dims()[0],
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE(x_dims[0] == c_prev_dims[0],
"Batch size of inputs and states must be equal");
PADDLE_ENFORCE(x->dims()[1] == c_prev->dims()[1] * 4,
PADDLE_ENFORCE(x_dims[1] == c_prev_dims[1] * 4,
"Dimension of FC should equal to prev state * 4");
int b_size = c_prev->dims()[0]; // batch size
int s_dim = c_prev->dims()[1]; // state dim
ctx.Output<framework::LoDTensor>("C")->Resize({b_size, s_dim});
ctx.Output<framework::LoDTensor>("H")->Resize({b_size, s_dim});
int b_size = c_prev_dims[0]; // batch size
int s_dim = c_prev_dims[1]; // state dim
ctx->SetOutputDim("C", {b_size, s_dim});
ctx->SetOutputDim("H", {b_size, s_dim});
}
};
template <typename AttrType>
class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LstmUnitOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LstmUnitOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "FC input before the non-linear activation.");
AddInput(
......@@ -79,15 +78,14 @@ class LstmUnitGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("C")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("C")),
"Input(C@GRAD) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("H")),
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("H")),
"Input(H@GRAD) should not be null");
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
ctx.Output<framework::LoDTensor>(framework::GradVarName("C_prev"))
->Resize(ctx.Input<Tensor>("C_prev")->dims());
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("C_prev"),
ctx->GetInputDim("C_prev"));
}
};
......
paddle/operators/mean_op.cc
浏览文件 @ 49697d9d
......@@ -22,18 +22,18 @@ class MeanOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of MeanOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MeanOp should not be null.");
ctx.Output<framework::Tensor>("Out")->Resize({1});
ctx->SetOutputDim("Out", {1});
}
};
class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MeanOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").NotInGradient();
......@@ -47,9 +47,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
......
paddle/operators/minus_op.cc
浏览文件 @ 49697d9d
......@@ -26,22 +26,22 @@ class MinusOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of MinusOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of MinusOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MinusOp should not be null.");
auto *left_tensor = ctx.Input<framework::Tensor>("X");
auto *right_tensor = ctx.Input<framework::Tensor>("Y");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(
left_tensor->numel(), right_tensor->numel(),
x_dims, y_dims,
"Minus operator must take two tensor with same num of elements");
ctx.Output<framework::Tensor>("Out")->Resize(left_tensor->dims());
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/operators/modified_huber_loss_op.cc
浏览文件 @ 49697d9d
......@@ -22,20 +22,19 @@ class ModifiedHuberLossOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& context) const override {
PADDLE_ENFORCE_NOT_NULL(context.InputVar("X"), "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(context.InputVar("Y"), "Y must be initialized.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x->dims(), y->dims(),
"The shape of X and Y must be the same.");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "The tensor rank of X must be 2.");
PADDLE_ENFORCE_EQ(x->dims()[1], 1, "The 2nd dimension of X must be 1.");
PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "The tensor rank of X must be 2.");
PADDLE_ENFORCE_EQ(x_dims[1], 1, "The 2nd dimension of X must be 1.");
context.Output<framework::Tensor>("IntermediateVal")->Resize(x->dims());
context.Output<framework::Tensor>("Out")->Resize({x->dims()[0], 1});
ctx->SetOutputDim("IntermediateVal", x_dims);
ctx->SetOutputDim("Out", {x_dims[0], 1});
}
};
......@@ -75,27 +74,28 @@ class ModifiedHuberLossGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& context) const override {
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto* intermediate_val = context.Input<Tensor>("IntermediateVal");
auto* out_grad = context.Input<Tensor>(framework::GradVarName("Out"));
auto* x_grad =
context.Output<framework::Tensor>(framework::GradVarName("X"));
PADDLE_ENFORCE_NOT_NULL(x, "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(y, "Y must be initialized.");
PADDLE_ENFORCE_NOT_NULL(intermediate_val,
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("IntermediateVal"),
"Intermediate value must not be null.");
PADDLE_ENFORCE_NOT_NULL(out_grad, "Input(Out@Grad) must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@Grad) must not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto intermediate_dims = ctx->GetInputDim("IntermediateVal");
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(
intermediate_val->dims(), x->dims(),
intermediate_dims, x_dims,
"The shape of X and intermediate value must be the same.");
PADDLE_ENFORCE_EQ(out_grad->dims(), x->dims(),
PADDLE_ENFORCE_EQ(out_grad_dims, x_dims,
"The shape of Input(Out@Grad) and X must be the same.");
if (x_grad) x_grad->Resize(x->dims());
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
};
......
paddle/operators/mul_op.cc
浏览文件 @ 49697d9d
......@@ -24,27 +24,23 @@ class MulOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of MulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of MulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MulOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
int x_num_col_dims = Attr<int>("x_num_col_dims");
int y_num_col_dims = Attr<int>("y_num_col_dims");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");
PADDLE_ENFORCE(x_dims.size() > x_num_col_dims,
"The rank of input tensor X(%s) should be larger than "
"`mul_op`'s `x_num_col_dims`.",
ctx.op().Input("X"));
"The rank of input tensor X should be larger than "
"`mul_op`'s `x_num_col_dims`.");
PADDLE_ENFORCE(y_dims.size() > y_num_col_dims,
"The rank of input tensor Y(%s) should be larger than "
"`mul_op`'s `y_num_col_dims`.",
ctx.op().Input("Y"));
"The rank of input tensor Y should be larger than "
"`mul_op`'s `y_num_col_dims`.");
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
......@@ -52,15 +48,14 @@ class MulOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's height.");
ctx.Output<framework::Tensor>("Out")->Resize(
{x_mat_dims[0], y_mat_dims[1]});
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", {x_mat_dims[0], y_mat_dims[1]});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MulOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of mul op");
AddInput("Y", "The second input of mul op");
......@@ -100,16 +95,14 @@ class MulOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_mat_dims =
framework::flatten_to_2d(x_dims, Attr<int>("x_num_col_dims"));
......@@ -125,8 +118,15 @@ class MulOpGrad : public framework::OperatorWithKernel {
"The second dimension of Out@GRAD must equal to the second "
"dimension of the second operand.");
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
......
paddle/operators/multiplex_op.cc
浏览文件 @ 49697d9d
......@@ -18,61 +18,64 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
class MultiplexOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) shouldn't be null.");
auto ins = ctx.MultiInput<Tensor>("X");
auto *out = ctx.Output<LoDTensor>("Out");
auto num_ins = ins.size();
PADDLE_ENFORCE(num_ins > 2,
"multiplex operator should have more than 2 inputs.");
PADDLE_ENFORCE_EQ(ins[0]->dims().size(), 1,
"The first input must be a index vector.");
auto in_dim = ins[1]->dims();
for (size_t i = 2; i < num_ins; i++) {
auto dim = ins[i]->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ids"), "Input(Ids) shouldn't be null.");
PADDLE_ENFORCE(!ctx->Inputs("X").empty(),
"MultiInput(X) shouldn't be empty.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
auto ids_dim = ctx->GetInputDim("Ids");
PADDLE_ENFORCE(
in_dim == dim,
"All the input tensors except the first one must have the same size");
ids_dim.size() == 2 && ids_dim[1] == 1,
"The index tensor must be a vector with size batchSize x 1.");
auto ins_dims = ctx->GetInputsDim("X");
auto num_ins = ins_dims.size();
PADDLE_ENFORCE(num_ins > 1,
"multiplex operator should have more than "
"one candidate input tensors.");
auto in_dim = ins_dims[0];
PADDLE_ENFORCE(in_dim.size() >= 2,
"The rank of candidate tensors must be not less than 2.");
for (size_t i = 1; i < num_ins; i++) {
auto dim = ins_dims[i];
PADDLE_ENFORCE(in_dim == dim,
"All the candidate tensors must have the same size.");
}
out->Resize(in_dim);
ctx->SetOutputDim("Out", in_dim);
}
};
class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MultiplexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MultiplexOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensors of multiplex operator.").AsDuplicable();
AddInput("Ids", "The index tensor of multiplex operator.");
AddInput("X", "The candidate tensors of multiplex operator.")
.AsDuplicable();
AddOutput("Out", "The output tensor of multiplex operator.");
AddComment(R"DOC(Multiplex operator
Multiplex multiple tensors according to the index provided by the first
input tensor.
Multiplex multiple tensors according to the index provided by the index tensor.
ins[0]: the index tensor.
ins[1:N]: the candidate output tensors.
Ids: the index tensor.
X[0 : N - 1]: the candidate tensors for output (N >= 2).
For each index i from 0 to batchSize - 1, the output is the i-th row of the
the (index[i] + 1)-th tensor.
the (Ids[i])-th tensor.
For i-th row of the output tensor:
y[i][j] = x_{k}[i][j], j = 0,1, ... , (x_{1}.width - 1)
y[i] = x_{k}[i]
where y is the output tensor. `x_{k}` is the k-th input tensor
and `k = x{0}[i] + 1`.
and `k = Ids[i]`.
)DOC");
}
};
......@@ -82,21 +85,19 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) should not be null");
PADDLE_ENFORCE(!ctx.MultiOutputVar(framework::GradVarName("X")).empty(),
"Output(X@Grad) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto d_ins = ctx.MultiOutput<LoDTensor>(framework::GradVarName("X"));
auto ins = ctx.MultiInput<Tensor>("X");
// don't compute gradient for index (ins[0])
for (size_t i = 1; i < ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->Resize(ins[i]->dims());
}
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "Input(X) should not be null.");
PADDLE_ENFORCE(!ctx->Outputs(framework::GradVarName("X")).empty(),
"Output(X@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
std::vector<framework::DDim> d_ins;
auto ins = ctx->GetInputsDim("X");
// No need to compute gradient for Input(Ids)
for (size_t i = 0; i < ins.size(); i++) {
d_ins.push_back(ins[i]);
}
ctx->SetOutputsDim(framework::GradVarName("X"), d_ins);
}
};
......
paddle/operators/multiplex_op.cu
浏览文件 @ 49697d9d
......@@ -18,27 +18,30 @@
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
class MultiplexGPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto ins = ctx.MultiInput<Tensor>("X");
auto* ids = ctx.Input<Tensor>("Ids");
auto* out = ctx.Output<Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
// copy index to cpu
framework::Tensor index_t_cpu;
index_t_cpu.CopyFrom<T>(*(ins[0]), platform::CPUPlace());
auto* index = index_t_cpu.data<T>();
Tensor index_t_cpu;
index_t_cpu.CopyFrom<int32_t>(*ids, platform::CPUPlace());
auto* index = index_t_cpu.data<int32_t>();
auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
int32_t k = index[i];
PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
PADDLE_ENFORCE_LT(k, ins.size(),
"index exceeds the number of candidate tensors.");
memory::Copy(place, out->data<T>() + i * cols, place,
......@@ -51,11 +54,11 @@ template <typename Place, typename T>
class MultiplexGradGPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto d_ins =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
for (size_t i = 1; i < d_ins.size(); i++) {
auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<Tensor>("X");
auto* ids = ctx.Input<Tensor>("Ids");
auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
......@@ -63,19 +66,19 @@ class MultiplexGradGPUKernel : public framework::OpKernel {
}
}
auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
// copy index to cpu
framework::Tensor index_t_cpu;
index_t_cpu.CopyFrom<T>(*(ins[0]), platform::CPUPlace());
auto* index = index_t_cpu.data<T>();
Tensor index_t_cpu;
index_t_cpu.CopyFrom<int32_t>(*ids, platform::CPUPlace());
auto* index = index_t_cpu.data<int32_t>();
auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
size_t k = static_cast<size_t>(index[i]);
if (d_ins[k]) {
memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
d_out->data<T>() + i * cols, cols * sizeof(T), stream);
......
paddle/operators/multiplex_op.h
浏览文件 @ 49697d9d
......@@ -27,16 +27,18 @@ class MultiplexCPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto ids = ctx.Input<framework::Tensor>("Ids");
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto* index = ins[0]->data<T>();
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
auto index = ids->data<int32_t>();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
int32_t k = index[i];
PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
PADDLE_ENFORCE_LT(static_cast<size_t>(k), ins.size(),
"index exceeds the number of candidate tensors.");
memory::Copy(place, out->data<T>() + i * cols, place,
......@@ -50,10 +52,11 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* ids = ctx.Input<framework::Tensor>("Ids");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto d_ins =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
for (size_t i = 1; i < d_ins.size(); i++) {
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
......@@ -61,12 +64,12 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
}
}
auto rows = ins[1]->dims()[0];
auto cols = ins[1]->dims()[1];
auto* index = ins[0]->data<T>();
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
auto* index = ids->data<int32_t>();
Place place = boost::get<Place>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
int k = (int)index[i] + 1;
size_t k = static_cast<size_t>(index[i]);
if (d_ins[k]) {
memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
d_out->data<T>() + i * cols, cols * sizeof(T));
......
paddle/operators/pad_op.cc
浏览文件 @ 49697d9d
......@@ -24,14 +24,13 @@ class PadOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of PadOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of PadOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of PadOp should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto paddings = Attr<std::vector<int>>("paddings");
auto x_dim = ctx->GetInputDim("X");
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
"Size of paddings should be equal to 2 * dimension size "
"of input tensor.");
......@@ -39,19 +38,18 @@ class PadOp : public framework::OperatorWithKernel {
for (int i = 0; i < x_dim.size(); ++i) {
out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
}
ctx.Output<framework::Tensor>("Out")->Resize(
framework::make_ddim(out_dims));
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
if (out_dims[0] == x_dim[0]) {
// Only pass LoD when the first dimension is equal between
// output and input.
ctx.ShareLoD("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
}
};
class PadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PadOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
PadOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
......@@ -101,14 +99,14 @@ class PadOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_g = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
if (x_g != nullptr) {
x_g->Resize(x_dims);
auto x_dims = ctx->GetInputDim("X");
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
}
};
......
paddle/operators/prelu_op.cc
浏览文件 @ 49697d9d
......@@ -26,19 +26,14 @@ class PReluOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
auto *in = ctx.Input<framework::Tensor>("X");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Alpha"),
"Input(Alpha) should not be null");
auto *alpha = ctx.Input<framework::Tensor>("Alpha");
PADDLE_ENFORCE(alpha->numel() == 1, "Size of weight Alpha must be one.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) should not be null");
auto *out = ctx.Output<framework::Tensor>("Out");
out->Resize(in->dims());
ctx.ShareLoD("X", /*->*/ "Out");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput("Alpha"), "Input(Alpha) should not be null");
PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1,
"Size of weight Alpha must be one.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -68,19 +63,13 @@ class PReluGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto *dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *x = ctx.Input<framework::Tensor>("X");
auto *dalpha =
ctx.Output<framework::Tensor>(framework::GradVarName("Alpha"));
auto *alpha = ctx.Input<framework::Tensor>("Alpha");
dx->Resize(x->dims());
dalpha->Resize(alpha->dims());
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("Alpha"),
ctx->GetInputDim("Alpha"));
}
};
......
paddle/operators/rank_loss_op.cc
浏览文件 @ 49697d9d
......@@ -25,22 +25,21 @@ class RankLossOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase *ctx) const override {
// input check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) shouldn't be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
"Input(Left) shouldn't be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
"Input(Right) shouldn't be null");
auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
auto left_dims = ctx.Input<framework::Tensor>("Left")->dims();
auto right_dims = ctx.Input<framework::Tensor>("Right")->dims();
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null");
PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null");
PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null");
auto label_dims = ctx->GetInputDim("Label");
auto left_dims = ctx->GetInputDim("Left");
auto right_dims = ctx->GetInputDim("Right");
PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
"All inputs must have the same size");
PADDLE_ENFORCE((label_dims.size() == 2) && (label_dims[1] == 1),
"All inputs must be row vector with size batch_size x 1.");
ctx.Output<framework::Tensor>("Out")->Resize(label_dims);
ctx->SetOutputDim("Out", label_dims);
}
};
......@@ -91,25 +90,22 @@ class RankLossGradOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
"Input(Left) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
"Input(Right) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto dims = ctx.Input<framework::Tensor>("Left")->dims();
auto *left_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("Left"));
auto *right_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("Right"));
if (left_grad) {
left_grad->Resize(dims);
auto dims = ctx->GetInputDim("Left");
auto left_grad_name = framework::GradVarName("Left");
auto right_grad_name = framework::GradVarName("Right");
if (ctx->HasOutput(left_grad_name)) {
ctx->SetOutputDim(left_grad_name, dims);
}
if (right_grad) {
right_grad->Resize(dims);
if (ctx->HasOutput(right_grad_name)) {
ctx->SetOutputDim(right_grad_name, dims);
}
}
};
......
paddle/operators/reshape_op.cc
浏览文件 @ 49697d9d
......@@ -26,14 +26,14 @@ class ReshapeOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase *ctx) const override {
// input check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ReshapeOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ReshapeOp should not be null.");
auto shape = ctx.Attr<std::vector<int>>("shape");
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
for (auto dim : shape) {
PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
......@@ -41,8 +41,8 @@ class ReshapeOp : public framework::OperatorWithKernel {
// capacity check
int64_t capacity =
std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
auto *in = ctx.Input<framework::Tensor>("X");
int64_t in_size = framework::product(in->dims());
auto x_dims = ctx->GetInputDim("X");
int64_t in_size = framework::product(x_dims);
PADDLE_ENFORCE_EQ(capacity, in_size,
"The size of Input(X) mismatches with Attr(shape).");
// resize output
......@@ -50,11 +50,11 @@ class ReshapeOp : public framework::OperatorWithKernel {
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto out_dims = framework::make_ddim(shape_int64);
ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
if (shape[0] == in->dims()[0]) {
ctx->SetOutputDim("Out", out_dims);
if (shape[0] == x_dims[0]) {
// Only pass LoD when the first dimension is equal between
// output and input.
ctx.ShareLoD("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
}
};
......@@ -94,13 +94,11 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto dims = ctx.Input<framework::Tensor>("X")->dims();
auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
d_in->Resize(dims);
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ 49697d9d
......@@ -24,16 +24,16 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"),
PADDLE_ENFORCE(ctx->HasInput("b"),
"Input(b) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of RowwiseAddOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto b_dims = ctx.Input<Tensor>("b")->dims();
auto x_dims = ctx->GetInputDim("X");
auto b_dims = ctx->GetInputDim("b");
PADDLE_ENFORCE_GT(
x_dims.size(), b_dims.size(),
"The rank of input `X` must be larger than the one of input `b`.");
......@@ -43,16 +43,17 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
"The width of two operands must be same");
PADDLE_ENFORCE_EQ(ctx.OutputSize("Out"), 1, "The output size must be 1");
ctx.Output<framework::Tensor>("Out")->Resize(x_dims);
ctx.ShareLoD("X", /*->*/ "Out");
PADDLE_ENFORCE_EQ(ctx->Outputs("Out").size(), 1,
"The output size must be 1");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RowwiseAddOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RowwiseAddOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The left input of row-wise add op, must be matrix");
AddInput("b", "The right input of row-wise add op, must be vector");
......@@ -69,25 +70,29 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X should not be null");
PADDLE_ENFORCE(ctx->HasInput("b"), "b should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto b_dims = ctx.Input<Tensor>("b")->dims();
auto x_dims = ctx->GetInputDim("X");
auto b_dims = ctx->GetInputDim("b");
PADDLE_ENFORCE_GT(
x_dims.size(), b_dims.size(),
"The rank of input `X` must be larger than the one of input `b`.");
int num_col_dims = x_dims.size() - b_dims.size();
int64_t num_col_dims = x_dims.size() - b_dims.size();
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
"The width of two operands must be same");
auto *dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *db = ctx.Output<framework::Tensor>(framework::GradVarName("b"));
if (dx) dx->Resize(x_dims);
if (db) db->Resize(b_dims);
auto x_grad_name = framework::GradVarName("X");
auto b_grad_name = framework::GradVarName("b");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(b_grad_name)) {
ctx->SetOutputDim(b_grad_name, b_dims);
}
}
};
......
paddle/operators/scale_op.cc
浏览文件 @ 49697d9d
......@@ -26,16 +26,13 @@ class ScaleOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ScaleOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ScaleOp should not be null.");
auto *in = ctx.Input<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
out->Resize(in->dims());
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/operators/scatter_op.cc
浏览文件 @ 49697d9d
......@@ -23,29 +23,30 @@ class ScatterOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ref"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ref"),
"Input(Ref) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Index"),
PADDLE_ENFORCE(ctx->HasInput("Index"),
"Input(Index) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Updates"),
PADDLE_ENFORCE(ctx->HasInput("Updates"),
"Input(Updates) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ScatterOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Index")->dims().size(), 1,
auto updates_dims = ctx->GetInputDim("Updates");
auto ref_dims = ctx->GetInputDim("Ref");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Index").size(), 1,
"Update Index should be 1-D.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Ref")->dims().size(),
ctx.Input<Tensor>("Updates")->dims().size(),
PADDLE_ENFORCE_EQ(ref_dims.size(), updates_dims.size(),
"Reference and Updates should have the same shape size");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Updates")->dims()[0],
ctx.Input<Tensor>("Index")->dims()[0],
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0],
ctx->GetInputDim("Index")[0],
"Updates and Index should have same batch-size.");
framework::DDim data_dim(ctx.Input<Tensor>("Updates")->dims());
for (int i = 1; i < data_dim.size(); ++i)
PADDLE_ENFORCE_EQ(data_dim[i], ctx.Input<Tensor>("Updates")->dims()[i]);
ctx.Output<framework::Tensor>("Out")->Resize(
ctx.Input<Tensor>("Ref")->dims());
framework::DDim data_dim(updates_dims);
for (int i = 1; i < data_dim.size(); ++i) {
PADDLE_ENFORCE_EQ(data_dim[i], updates_dims[i]);
}
ctx->SetOutputDim("Out", ref_dims);
}
};
......@@ -54,22 +55,17 @@ class ScatterGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto *dUpdates =
ctx.Output<framework::Tensor>(framework::GradVarName("Updates"));
auto *Updates = ctx.Input<Tensor>("Updates");
auto *dRef = ctx.Output<framework::Tensor>(framework::GradVarName("Ref"));
auto *Ref = ctx.Input<Tensor>("Ref");
dRef->Resize(Ref->dims());
dUpdates->Resize(Updates->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("Updates"),
ctx->GetInputDim("Updates"));
ctx->SetOutputDim(framework::GradVarName("Ref"), ctx->GetInputDim("Ref"));
}
};
class ScatterOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ScatterOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ScatterOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ref", "The source input of scatter op");
AddInput("Index",
......@@ -84,6 +80,7 @@ Out[Index] = Ref[Index] + Updates
)DOC");
}
};
} // namespace operators
} // namespace paddle
......
paddle/operators/sequence_pool_op.cc
浏览文件 @ 49697d9d
......@@ -22,23 +22,12 @@ class SequencePoolOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of SequencePoolOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SequencePoolOp should not be null.");
auto* x = ctx.Input<framework::LoDTensor>("X");
auto dims = x->dims();
auto lod = x->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_GE(
dims[0],
/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) must be large than batch size.");
dims[0] = lod[0].size() - 1;
ctx.Output<framework::LoDTensor>("Out")->Resize({dims});
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceAvgPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceAvgPoolOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
};
......@@ -85,22 +74,18 @@ class SequencePoolGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Gradient of Out should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"The input X should not be null.");
auto og_dims =
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx.Input<framework::LoDTensor>("X")->dims();
PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null.");
auto og_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(og_dims.size(), x_dims.size(),
"The rank of output grad must equal to Input(X).");
for (int64_t i = 1; i < og_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
}
auto* x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
x_grad->Resize(x_dims);
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
......
paddle/operators/sequence_pool_op.h
浏览文件 @ 49697d9d
......@@ -46,16 +46,27 @@ class SequencePoolKernel : public framework::OpKernel {
int strategy = context.Attr<int>("strategy");
auto dims = in->dims();
auto lod = in->lod()[0];
auto lod = in->lod();
int64_t w = in->numel() / dims[0];
// InferShape by lod
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_GE(
dims[0],
/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) must be large than batch size.");
dims[0] = lod[0].size() - 1;
out->Resize({dims});
auto lod_level_0 = lod[0];
out->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor in_t =
in->Slice<T>(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
Tensor in_t = in->Slice<T>(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
Tensor out_t = out->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t h = static_cast<int64_t>(lod_level_0[i + 1] - lod_level_0[i]);
auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
auto out_e = EigenVector<T>::Flatten(out_t);
......
paddle/operators/sgd_op.cc
浏览文件 @ 49697d9d
......@@ -22,19 +22,18 @@ class SGDOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("param"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("param"),
"Input(param) of SGDOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("grad"),
PADDLE_ENFORCE(ctx->HasInput("grad"),
"Input(grad) of SGDOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("param_out"),
PADDLE_ENFORCE(ctx->HasOutput("param_out"),
"Output(param_out) of SGDOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("param")->dims(),
ctx.Input<Tensor>("grad")->dims(),
auto param_dim = ctx->GetInputDim("param");
PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("grad"),
"Two input of SGD Op's dimension must be same.");
ctx.Output<framework::Tensor>("param_out")
->Resize(ctx.Input<Tensor>("param")->dims());
ctx->SetOutputDim("param_out", param_dim);
}
};
......
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @ 49697d9d
......@@ -22,33 +22,28 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Y must be initialized.");
auto* x = ctx.Input<framework::Tensor>("X");
auto* y = ctx.Input<framework::Tensor>("Y");
PADDLE_ENFORCE_EQ(x->dims(), y->dims(),
"The shape of X and Y must be the same.");
PADDLE_ENFORCE_GE(x->dims().size(), 2,
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"The tensor rank of X must be at least 2.");
auto* inside_weight = ctx.Input<framework::Tensor>("InsideWeight");
if (inside_weight) {
auto* outside_weight = ctx.Input<framework::Tensor>("OutsideWeight");
PADDLE_ENFORCE_NOT_NULL(outside_weight,
if (ctx->HasInput("InsideWeight")) {
PADDLE_ENFORCE(ctx->HasInput("OutsideWeight"),
"If weights are provided, must specify both "
"inside and outside weights.");
PADDLE_ENFORCE_EQ(inside_weight->dims(), x->dims(),
PADDLE_ENFORCE_EQ(ctx->GetInputDim("InsideWeight"), x_dims,
"The shape of InsideWeight must be same as X.");
PADDLE_ENFORCE_EQ(outside_weight->dims(), x->dims(),
PADDLE_ENFORCE_EQ(ctx->GetInputDim("OutsideWeight"), x_dims,
"The shape of OutsideWeight must be same as X.");
}
auto* diff = ctx.Output<framework::Tensor>("Diff");
auto* out = ctx.Output<framework::Tensor>("Out");
diff->Resize(x->dims());
ctx->SetOutputDim("Diff", x_dims);
// loss is a two-rank tensor
out->Resize({x->dims()[0], 1});
ctx->SetOutputDim("Out", {x_dims[0], 1});
}
};
......@@ -99,12 +94,9 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
auto in_dims = ctx.Input<framework::Tensor>("X")->dims();
auto out_dims =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"))->dims();
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto in_dims = ctx->GetInputDim("X");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(out_dims.size(), 2,
"The tensor rank of Input(Out@Grad) should be 2.");
......@@ -114,8 +106,14 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(out_dims[1], 1,
"The 2nd dimension of Input(Out@Grad) must be 1.");
if (x_grad) x_grad->Resize(in_dims);
if (y_grad) y_grad->Resize(in_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, in_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, in_dims);
}
}
};
......
paddle/operators/softmax_op.cc
浏览文件 @ 49697d9d
......@@ -22,22 +22,23 @@ class SoftmaxOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SoftmaxOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of SoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(x_dims.size() == 2UL,
"The input of softmax op must be a matrix.");
ctx.Output<framework::Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
ctx->SetOutputDim("Y", x_dims);
}
};
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
SoftmaxOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input tensor of softmax. "
......@@ -68,16 +69,15 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) should be not null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Y")->dims(),
ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Y"),
ctx->GetInputDim(framework::GradVarName("Y")),
"Input(Y) and its gradients should have a same shape.");
ctx.Output<framework::Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
......
paddle/operators/split_op.cc
浏览文件 @ 49697d9d
......@@ -24,40 +24,42 @@ class SplitOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// infershape
auto *in = ctx.Input<framework::Tensor>("X");
auto outs = ctx.MultiOutput<framework::Tensor>("Out");
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
size_t num = static_cast<size_t>(ctx.Attr<int>("num"));
std::vector<int> sections =
static_cast<std::vector<int>>(ctx.Attr<std::vector<int>>("sections"));
const size_t n = outs.size();
void InferShape(framework::InferShapeContextBase *ctx) const override {
auto in_dims = ctx->GetInputDim("X");
auto outs_names = ctx->Outputs("Out");
size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
size_t num = static_cast<size_t>(ctx->Attrs().Get<int>("num"));
std::vector<int> sections = static_cast<std::vector<int>>(
ctx->Attrs().Get<std::vector<int>>("sections"));
const size_t outs_number = outs_names.size();
std::vector<framework::DDim> outs_dims;
outs_dims.reserve(outs_number);
if (num > 0) {
int64_t in_axis_dim = in->dims()[axis];
int64_t in_axis_dim = in_dims[axis];
PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
"tensor split does not result"
" in an equal division");
size_t out_axis_dim = in_axis_dim / num;
for (size_t i = 0; i < n; ++i) {
auto dim = in->dims();
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = out_axis_dim;
outs[i]->Resize(dim);
outs_dims.push_back(dim);
}
} else if (sections.size() > 0) {
PADDLE_ENFORCE_EQ(sections.size(), n,
PADDLE_ENFORCE_EQ(sections.size(), outs_number,
"tensor split sections size"
"should be equal to output size.");
for (size_t i = 0; i < n; ++i) {
auto dim = in->dims();
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = sections[i];
outs[i]->Resize(dim);
outs_dims.push_back(dim);
}
} else {
PADDLE_ENFORCE_NOT_NULL(nullptr, "split operator should",
" specify indices or sections.");
}
ctx->SetOutputsDim("Out", outs_dims);
}
};
......
paddle/operators/squared_l2_distance_op.cc
浏览文件 @ 49697d9d
......@@ -22,24 +22,19 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Y"),
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("sub_result"),
PADDLE_ENFORCE(
ctx->HasOutput("sub_result"),
"Output(sub_result) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SquaredL2DistanceOp should not be null.");
auto* x = ctx.Input<Tensor>("X");
auto x_dims = x->dims();
auto* y = ctx.Input<Tensor>("Y");
auto y_dims = y->dims();
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims),
"Tensor rank of both SquaredL2DistanceOp's "
......@@ -47,17 +42,16 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
int rank = framework::arity(x_dims);
PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
PADDLE_ENFORCE_EQ(x->numel() / x_dims[0], y->numel() / y_dims[0],
PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
"Product of dimensions expcet the first dimension of "
"input and target must be equal.");
PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
"First dimension of target must be equal to input "
"or to 1.");
ctx.Output<framework::Tensor>("sub_result")
->Resize({x_dims[0], x->numel() / x_dims[0]});
ctx.Output<framework::Tensor>("Out")->Resize({x_dims[0], 1});
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("sub_result", {x_dims[0], product(x_dims) / x_dims[0]});
ctx->SetOutputDim("Out", {x_dims[0], 1});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -92,22 +86,22 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Gradient of Out should not be null");
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
"First dimension of output gradient and "
"input value must be equal.");
PADDLE_ENFORCE_EQ(out_dims[1], 1,
"Second dimension of output gradient "
"must be 1.");
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) ctx->SetOutputDim(x_grad_name, x_dims);
if (ctx->HasOutput(y_grad_name)) ctx->SetOutputDim(y_grad_name, y_dims);
}
};
......
paddle/operators/sum_op.cc
浏览文件 @ 49697d9d
......@@ -21,31 +21,27 @@ class SumOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) of SumOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto x_dims = ctx->GetInputsDim("X");
PADDLE_ENFORCE(!x_dims.empty(), "Input(X) of SumOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SumOp should not be null.");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
int N = ins.size();
auto in_dim = ins[0]->dims();
auto in_dim = x_dims[0];
size_t N = x_dims.size();
PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
for (int i = 1; i < N; i++) {
auto dim = ins[i]->dims();
for (size_t i = 1; i < N; i++) {
auto dim = x_dims[i];
PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
}
out->Resize(in_dim);
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", in_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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.");
......@@ -63,13 +59,16 @@ class SumGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto outputs =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
auto dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
for (auto output : outputs) {
output->Resize(dims);
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_grad_names = ctx->Outputs(framework::GradVarName("X"));
size_t x_length = x_grad_names.size();
std::vector<framework::DDim> x_grad_dims;
x_grad_dims.reserve(x_length);
for (size_t i = 0; i < x_length; ++i) {
x_grad_dims.push_back(out_grad_dims);
}
ctx->SetOutputsDim(framework::GradVarName("X"), x_grad_dims);
}
};
......
paddle/operators/top_k_op.cc
浏览文件 @ 49697d9d
......@@ -22,26 +22,26 @@ class TopkOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of TopkOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of TopkOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Indices"),
PADDLE_ENFORCE(ctx->HasOutput("Indices"),
"Output(Indices) of TopkOp should not be null.");
auto *input = ctx.Input<framework::Tensor>("X");
const int k = static_cast<int>(ctx.Attr<int>("k"));
auto input_dims = ctx->GetInputDim("X");
const int k = static_cast<int>(ctx->Attrs().Get<int>("k"));
PADDLE_ENFORCE_GE(k, 1, "k must >= 1");
PADDLE_ENFORCE_GE(input->dims().size(), 1, "input must have >= 1d shape");
PADDLE_ENFORCE_GE(input->dims()[input->dims().size() - 1], k,
PADDLE_ENFORCE_GE(input_dims.size(), 1, "input must have >= 1d shape");
PADDLE_ENFORCE_GE(input_dims[input_dims.size() - 1], k,
"input must have >= k columns");
framework::DDim dims = input->dims();
framework::DDim dims = input_dims;
dims[dims.size() - 1] = k;
ctx.Output<framework::Tensor>("Out")->Resize(dims);
ctx.Output<framework::Tensor>("Indices")->Resize(dims);
ctx->SetOutputDim("Out", dims);
ctx->SetOutputDim("Indices", dims);
}
};
......
paddle/operators/top_k_op.cu
浏览文件 @ 49697d9d
......@@ -301,14 +301,16 @@ class TopkOpCUDAKernel : public framework::OpKernel {
// NOTE: pass lds and dim same to input width.
// NOTE: old matrix implementation of stride is different to eigen.
// TODO(typhoonzero): launch kernel on specified stream.
// TODO(typhoonzero): refine this kernel.
dim3 threads(256, 1);
dim3 grid(input_height, 1);
KeMatrixTopK<T, 5, 256><<<grid, threads>>>(
output_data, output->dims()[1], indices_data, input_data, input_width,
input_width, int(k));
KeMatrixTopK<T, 5, 256><<<
grid, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(output_data, output->dims()[1],
indices_data, input_data,
input_width, input_width, int(k));
}
};
......
paddle/operators/transpose_op.cc
浏览文件 @ 49697d9d
......@@ -24,12 +24,11 @@ class TransposeOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
auto x_dims = ctx->GetInputDim("X");
std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
size_t x_rank = x_dims.size();
size_t axis_size = axis.size();
......@@ -51,14 +50,14 @@ class TransposeOp : public framework::OperatorWithKernel {
for (size_t i = 0; i < axis_size; i++) {
out_dims[i] = x_dims[axis[i]];
}
ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
ctx->SetOutputDim("Out", out_dims);
}
};
class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TransposeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
TransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......@@ -94,14 +93,15 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
if (x_grad) x_grad->Resize(x_dims);
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
};
......
paddle/operators/uniform_random_op.cc
浏览文件 @ 49697d9d
......@@ -23,18 +23,18 @@ namespace operators {
template <typename T>
class CPUUniformRandomKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
void Compute(const framework::ExecutionContext& ctx) const override {
auto* tensor = ctx.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(ctx.GetPlace());
unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
}
engine.seed(seed);
std::uniform_real_distribution<T> dist(
static_cast<T>(context.Attr<float>("min")),
static_cast<T>(context.Attr<float>("max")));
static_cast<T>(ctx.Attr<float>("min")),
static_cast<T>(ctx.Attr<float>("max")));
int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
......@@ -47,21 +47,20 @@ class UniformRandomOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of UniformRandomOp should not be null.");
PADDLE_ENFORCE(Attr<float>("min") < Attr<float>("max"),
PADDLE_ENFORCE(
ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = Attr<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
temp.push_back(static_cast<int64_t>(dim));
}
tensor->Resize(framework::make_ddim(temp));
ctx->SetOutputDim("Out", framework::make_ddim(temp));
}
};
......
paddle/parameter/FirstOrderOptimizer.h
浏览文件 @ 49697d9d
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include "ParameterOptimizer.h"
#include "ParameterUpdateFunctions.h"
#include "Regularizer.h"
namespace paddle {
......@@ -37,6 +38,15 @@ public:
real torch_learningRate = optConfig_.learning_method() == "torch_momentum"
? 1.0 - paraConfig.momentum()
: 1.0;
#ifdef PADDLE_USE_MKLDNN
sgdUpdate(learningRate_ * paraConfig.learning_rate() *
(firstTime_ ? 1.0 : torch_learningRate),
paraConfig.momentum(),
applyDecay_ ? paraConfig.decay_rate() : 0,
vecs[PARAMETER_VALUE].get(),
vecs[PARAMETER_GRADIENT].get(),
vecs[PARAMETER_MOMENTUM].get());
#else
vecs[PARAMETER_VALUE]->sgdUpdate(
*vecs[PARAMETER_GRADIENT],
*vecs[PARAMETER_MOMENTUM],
......@@ -44,6 +54,7 @@ public:
(firstTime_ ? 1.0 : torch_learningRate),
paraConfig.momentum(),
applyDecay_ ? paraConfig.decay_rate() : 0);
#endif
}
virtual void finishBatch() { firstTime_ = false; }
};
......
paddle/parameter/ParameterUpdateFunctions.cpp
浏览文件 @ 49697d9d
......@@ -30,6 +30,9 @@ void sgdUpdateCpu(real learningRate,
const real* grad,
real* momentumVec) {
decayRate *= learningRate;
#ifdef PADDLE_USE_MKLDNN
#pragma omp parallel for
#endif
for (size_t i = 0; i < size; ++i) {
momentumVec[i] = momentum * momentumVec[i] - learningRate * grad[i] -
decayRate * value[i];
......
python/paddle/trainer/config_parser.py
浏览文件 @ 49697d9d
......@@ -1566,7 +1566,7 @@ class LayerBase(object):
self.config = g_config.model_config.layers.add()
assert isinstance(self.config, LayerConfig)
use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0)))
mkldnn_acts = ['relu', 'tanh']
mkldnn_acts = ['relu', 'tanh', 'softmax']
if use_mkldnn and active_type in mkldnn_acts:
active_type = "mkldnn_" + active_type
self.config.name = name
......
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @ 49697d9d
......@@ -4,22 +4,24 @@ from op_test import OpTest
class TestCrossEntropyOp1(OpTest):
"""Test standard cross-entropy, with index representation of labels.
"""Test cross-entropy with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 30
class_num = 10
X = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label = np.random.randint(0, class_num, (batch_size, 1), dtype="int32")
cross_entropy = np.asmatrix(
[[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])],
dtype="float32")
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {'soft_label': False}
self.attrs = {"softLabel": False}
def test_check_output(self):
self.check_output()
......@@ -29,13 +31,14 @@ class TestCrossEntropyOp1(OpTest):
class TestCrossEntropyOp2(OpTest):
"""Test soft-label cross-entropy, with vecterized soft labels.
"""Test cross-entropy with vectorized soft labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 10
class_num = 5
batch_size = 5
class_num = 37
X = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label = np.random.uniform(0.1, 1.0,
......@@ -43,46 +46,49 @@ class TestCrossEntropyOp2(OpTest):
label /= label.sum(axis=1, keepdims=True)
cross_entropy = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
self.inputs = {'X': X, 'Label': label}
self.outputs = {'Y': cross_entropy}
self.attrs = {'soft_label': True}
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"softLabel": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y')
self.check_grad(["X"], "Y", max_relative_error=0.05)
class TestCrossEntropyOp3(OpTest):
"""Test one-hot cross-entropy, with vecterized one-hot representation of
labels.
"""Test cross-entropy with vectorized one-hot representation of labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 30
class_num = 10
batch_size = 5
class_num = 17
X = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label_index = np.random.randint(
0, class_num, (batch_size), dtype="int32")
label = np.zeros(X.shape)
label[np.arange(batch_size), label_index] = 1
cross_entropy = np.asmatrix(
[[-np.log(X[i][label_index[i]])] for i in range(X.shape[0])],
dtype="float32")
cross_entropy2 = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
self.inputs = {'X': X, 'Label': label}
self.outputs = {'Y': cross_entropy}
self.attrs = {'soft_label': True}
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"softLabel": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y')
self.check_grad(["X"], "Y", max_relative_error=0.05)
if __name__ == "__main__":
......
python/paddle/v2/framework/tests/test_multiplex_op.py
浏览文件 @ 49697d9d
......@@ -6,20 +6,22 @@ from op_test import OpTest
class TestMultiplexOp(OpTest):
def setUp(self):
self.op_type = "multiplex"
rows = 3
index = np.array([3, 1, 0])
rows = 4
index = np.arange(0, rows).astype('int32')
np.random.shuffle(index)
index = np.reshape(index, (rows, 1))
ins1 = np.random.random((rows, 10)).astype("float32")
ins2 = np.random.random((rows, 10)).astype("float32")
ins3 = np.random.random((rows, 10)).astype("float32")
ins4 = np.random.random((rows, 10)).astype("float32")
self.inputs = {
'X': [('index', index), ('x1', ins1), ('x2', ins2), ('x3', ins3),
('x4', ins4)]
'Ids': index,
'X': [('x1', ins1), ('x2', ins2), ('x3', ins3), ('x4', ins4)]
}
# multiplex output
output = np.zeros_like(ins1)
for i in range(0, rows):
k = index[i] + 1
k = index[i][0]
output[i] = self.inputs['X'][k][1][i]
self.outputs = {'Out': output}
......
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