提交 76338fb6 编写于 作者: L Liu Yiqun

Merge branch 'develop' into build_ios

......@@ -67,6 +67,9 @@ endif()
if(ANDROID OR IOS)
if(ANDROID AND ${CMAKE_SYSTEM_VERSION} VERSION_LESS "16")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16")
elseif(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# TODO: support glog for Android api 16 ~ 19 in the future
message(WARNING "Using the unofficial git repository <https://github.com/Xreki/glog.git> instead")
endif()
set(WITH_GPU OFF CACHE STRING
......
......@@ -6,13 +6,14 @@ RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ub
# ENV variables
ARG ANDROID_ABI
ARG ANDROID_API
ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"}
ENV ANDROID_API=${ANDROID_API:-21}
ENV HOME=/root \
ANDROID_NDK_HOME=/opt/android-ndk-linux \
ANDROID_ARM_STANDALONE_TOOLCHAIN=/opt/arm-toolchain \
ANDROID_ARM64_STANDALONE_TOOLCHAIN=/opt/arm64-toolchain
ANDROID_TOOLCHAINS_DIR=/opt/toolchains
RUN apt-get update && \
apt-get install -y \
......@@ -42,14 +43,12 @@ RUN pip install --upgrade pip && \
pip install pre-commit
# Android NDK
RUN mkdir /opt/android-ndk-tmp && \
RUN mkdir -p ${ANDROID_TOOLCHAINS_DIR} && \
mkdir -p /opt/android-ndk-tmp && \
cd /opt/android-ndk-tmp && \
wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \
unzip -q android-ndk-r14b-linux-x86_64.zip && \
mv android-ndk-r14b ${ANDROID_NDK_HOME} && \
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-23 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-23 --install-dir=${ANDROID_ARM64_STANDALONE_TOOLCHAIN} && \
rm -rf /opt/android-ndk-tmp && \
rm -rf ${ANDROID_NDK_HOME}
rm -rf /opt/android-ndk-tmp
CMD ["bash", "/paddle/paddle/scripts/docker/build_android.sh"]
......@@ -57,3 +57,12 @@ SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
ADD_DEPENDENCIES(gflags extern_gflags)
LIST(APPEND external_project_dependencies gflags)
IF(WITH_C_API)
INSTALL(DIRECTORY ${GFLAGS_INCLUDE_DIR} DESTINATION third_party/gflags)
IF(ANDROID)
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
......@@ -57,3 +57,12 @@ ADD_DEPENDENCIES(glog extern_glog gflags)
LINK_LIBRARIES(glog gflags)
LIST(APPEND external_project_dependencies glog)
IF(WITH_C_API)
INSTALL(DIRECTORY ${GLOG_INCLUDE_DIR} DESTINATION third_party/glog)
IF(ANDROID)
INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib)
ENDIF()
ENDIF()
......@@ -86,6 +86,26 @@ IF(NOT ${CBLAS_FOUND})
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
SET(TMP_INSTALL_DIR third_party/openblas/lib/${ANDROID_ABI})
ELSE()
SET(TMP_INSTALL_DIR third_party/openblas/lib)
ENDIF()
INSTALL(CODE "execute_process(
COMMAND ${CMAKE_COMMAND} -E copy_directory ${CBLAS_INSTALL_DIR}/lib
destination ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
)"
)
INSTALL(CODE "MESSAGE(STATUS \"Installing: \"
\"${CBLAS_INSTALL_DIR}/lib -> ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}\"
)"
)
ENDIF()
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
......
......@@ -224,6 +224,15 @@ IF(NOT PROTOBUF_FOUND)
SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY}
CACHE FILEPATH "protoc library." FORCE)
IF(WITH_C_API)
INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf)
IF(ANDROID)
INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib)
ENDIF()
ENDIF()
IF(CMAKE_CROSSCOMPILING)
PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf)
ELSE()
......
......@@ -50,3 +50,12 @@ ExternalProject_Add(
)
LIST(APPEND external_project_dependencies zlib)
IF(WITH_C_API)
INSTALL(DIRECTORY ${ZLIB_INCLUDE_DIR} DESTINATION third_party/zlib)
IF(ANDROID)
INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib)
ENDIF()
ENDIF()
digraph G {
rnn [label="1-th level RNN" shape=box]
subgraph cluster0 {
label = "time step 0"
sent0 [label="sentence"]
sent1 [label="sentence"]
rnn1 [label="2-th level RNN" shape=box]
sent0 -> rnn1
sent1 -> rnn1
}
subgraph cluster1 {
label = "time step 1"
sent2 [label="sentence"]
sent3 [label="sentence"]
rnn2 [label="2-th level RNN" shape=box]
sent2 -> rnn2
sent3 -> rnn2
}
subgraph cluster2 {
label = "time step 2"
sent4 [label="sentence"]
sent5 [label="sentence"]
rnn3 [label="2-th level RNN" shape=box]
sent4 -> rnn3
sent5 -> rnn3
}
para0 [label="paragraph info 0"]
para1 [label="paragraph info 1"]
para2 [label="paragraph info 2"]
rnn1 -> para0
rnn2 -> para1
rnn3 -> para2
para0 -> rnn
para1 -> rnn
para2 -> rnn
chapter [label="chapter info"]
rnn -> chapter
}
digraph G {
label = "simple RNN implementation"
ranksep=2;
//graph [nodesep=1, ranksep=1];
node[nodesep=1]
subgraph cluster0 {
label = "global scope"
rankdir = TB
W
boot_memory
input
output
}
subgraph cluster1 {
label = "step-scope 0"
rankdir = TB
memory0[label="memory"]
prememory0[label="pre-memory"]
step_input0[label="step input"]
step_output0[label="step output"]
}
subgraph cluster2 {
label = "step-scope 1"
rankdir = TB
memory1[label="memory"]
prememory1[label="pre-memory"]
step_input1[label="step input"]
step_output1[label="step output"]
}
subgraph cluster3 {
label = "step-scope 2"
rankdir = TB
memory2[label="memory"]
prememory2[label="pre-memory"]
step_input2[label="step input"]
step_output2[label="step output"]
}
stepnet [shape=box]
stepnet0 [shape=box, style=dashed]
stepnet1 [shape=box, style=dashed]
stepnet2 [shape=box, style=dashed]
edge[color=blue]
boot_memory -> prememory0 [label="init" color="blue"]
memory0 -> prememory1 [label="copy/reference" color="blue"]
memory1 -> prememory2 [label="copy/reference" color="blue"]
edge[color=black]
W -> stepnet0[constraint=false, style=dashed]
W -> stepnet1[constraint=false, style=dashed]
W -> stepnet2[constraint=false, style=dashed]
memory0 -> stepnet0[style=dashed]
prememory0 -> stepnet0 -> step_output0[style=dashed]
memory1 -> stepnet1[style=dashed]
prememory1 -> stepnet1 -> step_output1[style=dashed]
memory2 -> stepnet2[style=dashed]
prememory2 -> stepnet2 -> step_output2[style=dashed]
input -> step_input0
input -> step_input1
input -> step_input2
step_input0 -> stepnet0 [style=dashed]
step_input1 -> stepnet1[style=dashed]
step_input2 -> stepnet2[style=dashed]
step_output0 -> output
step_output1 -> output
step_output2 -> output
stepnet0 -> stepnet[style=dashed]
stepnet1 -> stepnet[style=dashed]
stepnet2 -> stepnet[style=dashed]
}
digraph G {
chapter [label="chapter"]
subgraph cluster0 {
label = "paragraph 0"
top_rnn0[label="top rnn step 0" shape=box]
p0 [label="paragraph 0"]
p1 [label="paragraph 1"]
}
subgraph cluster1{
label = "paragraph 1"
top_rnn1[label="top rnn step 1" shape=box]
p2 [label="paragraph 0"]
p3 [label="paragraph 1"]
}
subgraph cluster_p0 {
label = "sentence 0"
low_rnn0 [label="low rnn step 0" shape=box]
s00 [label="sentence 0"]
s01 [label="sentence 1"]
low_rnn0 -> s00
low_rnn0 -> s01
}
subgraph cluster_p1 {
label = "sentence 1"
low_rnn1 [label="low rnn step 1" shape=box]
s10 [label="sentence 0"]
s11 [label="sentence 1"]
low_rnn1 -> s10
low_rnn1 -> s11
}
subgraph cluster_p2 {
label = "sentence 1"
low_rnn2 [label="low rnn step 0" shape=box]
s20 [label="sentence 0"]
s21 [label="sentence 1"]
low_rnn2 -> s20
low_rnn2 -> s21
}
subgraph cluster_p3 {
label = "sentence 1"
low_rnn3 [label="low rnn step 1" shape=box]
s30 [label="sentence 0"]
s31 [label="sentence 1"]
low_rnn3 -> s30
low_rnn3 -> s31
}
chapter -> top_rnn0
chapter -> top_rnn1
top_rnn0 -> p0
top_rnn0 -> p1
top_rnn1 -> p2
top_rnn1 -> p3
p0 -> low_rnn0
p1 -> low_rnn1
p2 -> low_rnn2
p3 -> low_rnn3
}
# RNNOp design
This document is about an RNN operator which requires that instances in a mini-batch have the same length. We will have a more flexible RNN operator.
## RNN Algorithm Implementation
<p aligh="center">
<img src="./images/rnn.jpg"/>
</p>
The above diagram shows an RNN unrolled into a full network.
There are several important concepts:
- *step-net*: the sub-graph to run at each step,
- *memory*, $h_t$, the state of the current step,
- *ex-memory*, $h_{t-1}$, the state of the previous step,
- *initial memory value*, the ex-memory of the first step.
### Step-scope
There could be local variables defined in step-nets. PaddlePaddle runtime realizes these variables in *step-scopes* -- scopes created for each step.
<p aligh="center">
<img src="./images/rnn.png"/><br/>
Figure 2 the RNN's data flow
</p>
Please be aware that all steps run the same step-net. Each step
1. creates the step-scope,
2. realizes local variables, including step-outputs, in the step-scope, and
3. runs the step-net, which could use these variables.
The RNN operator will compose its output from step outputs in step scopes.
### Memory and Ex-memory
Let's give more details about memory and ex-memory via a simply example:
$$
h_t = U h_{t-1} + W x_t
$$,
where $h_t$ and $h_{t-1}$ are the memory and ex-memory of step $t$'s respectively.
In the implementation, we can make an ex-memory variable either "refers to" the memory variable of the previous step,
or copy the value of the previous memory value to the current ex-memory variable.
### Usage in Python
For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
We can define an RNN's step-net using Block:
```python
import paddle as pd
X = some_op() # x is some operator's output, and is a LoDTensor
a = some_op()
# declare parameters
W = pd.Variable(shape=[20, 30])
U = pd.Variable(shape=[20, 30])
rnn = pd.create_rnn_op(output_num=1)
with rnn.stepnet():
x = rnn.add_input(X)
# declare a memory (rnn's step)
h = rnn.add_memory(init=a)
# h.pre_state() means previous memory of rnn
new_state = pd.add_two( pd.matmul(W, x) + pd.matmul(U, h.pre_state()))
# update current memory
h.update(new_state)
# indicate that h variables in all step scopes should be merged
rnn.add_outputs(h)
out = rnn()
```
Python API functions in above example:
- `rnn.add_input` indicates the parameter is a variable that will be segmented into step-inputs.
- `rnn.add_memory` creates a variable used as the memory.
- `rnn.add_outputs` mark the variables that will be concatenated across steps into the RNN output.
### Nested RNN and LoDTensor
An RNN whose step-net includes other RNN operators is known as an *nested RNN*.
For example, we could have a 2-level RNN, where the top level corresponds to paragraphs, and the lower level corresponds to sentences.
The following figure illustrates the feeding of text into the lower level, one sentence each step, and the feeding of step outputs to the top level. The final top level output is about the whole text.
<p aligh="center">
<img src="./images/2_level_rnn.png"/>
</p>
```python
import paddle as pd
W = pd.Variable(shape=[20, 30])
U = pd.Variable(shape=[20, 30])
W0 = pd.Variable(shape=[20, 30])
U0 = pd.Variable(shape=[20, 30])
# a is output of some op
a = some_op()
# chapter_data is a set of 128-dim word vectors
# the first level of LoD is sentence
# the second level of LoD is chapter
chapter_data = pd.Variable(shape=[None, 128], type=pd.lod_tensor, level=2)
def lower_level_rnn(paragraph):
'''
x: the input
'''
rnn = pd.create_rnn_op(output_num=1)
with rnn.stepnet():
sentence = rnn.add_input(paragraph, level=0)
h = rnn.add_memory(shape=[20, 30])
h.update(
pd.matmul(W, sentence) + pd.matmul(U, h.pre_state()))
# get the last state as sentence's info
rnn.add_outputs(h)
return rnn
top_level_rnn = pd.create_rnn_op(output_num=1)
with top_level_rnn.stepnet():
paragraph_data = rnn.add_input(chapter_data, level=1)
low_rnn = lower_level_rnn(paragraph_data)
paragraph_out = low_rnn()
h = rnn.add_memory(init=a)
h.update(
pd.matmul(W0, paragraph_data) + pd.matmul(U0, h.pre_state()))
top_level_rnn.add_outputs(h)
# just output the last step
chapter_out = top_level_rnn(output_all_steps=False)
```
in above example, the construction of the `top_level_rnn` calls `lower_level_rnn`. The input is a LoD Tensor. The top level RNN segments input text data into paragraphs, and the lower level RNN segments each paragraph into sentences.
By default, the `RNNOp` will concatenate the outputs from all the time steps,
if the `output_all_steps` set to False, it will only output the final time step.
<p align="center">
<img src="images/rnn_2level_data.png"/>
</p>
......@@ -60,6 +60,26 @@ if(ANDROID)
install(TARGETS paddle_capi_whole paddle_capi_shared
ARCHIVE DESTINATION lib/${ANDROID_ABI}
LIBRARY DESTINATION lib/${ANDROID_ABI})
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=oneline -1
OUTPUT_VARIABLE GIT_COMMITS_LIST
RESULT_VARIABLE GIT_COMMITS_LIST_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(${GIT_COMMITS_LIST_RESULT})
set(GIT_COMMITS_LIST "No commits.")
endif()
install(CODE "FILE(WRITE ${CMAKE_INSTALL_PREFIX}/lib/${ANDROID_ABI}/BUILD.txt
\"Compiler:\n\"
\"\\t${CMAKE_C_COMPILER}\\n\"
\"\\t${CMAKE_CXX_COMPILER}\\n\"
\"Compiler Flags:\\n\"
\"\\t${CMAKE_F_FLAGS}\\n\"
\"\\t${CMAKE_CXX_FLAGS}\\n\"
\"Android API: ${CMAKE_SYSTEM_VERSION}\\n\"
\"Lastest commit:\\n\"
\"\\t${GIT_COMMITS_LIST}\\n\"
)"
)
else(ANDROID)
install(TARGETS paddle_capi_whole
ARCHIVE DESTINATION lib)
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/accuracy_op.h"
namespace paddle {
namespace operators {
class AccuracyOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
"Input of Inference must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input of Inference must be initialized.");
auto *inference = ctx.Input<framework::Tensor>("Inference");
auto *label = ctx.Input<framework::Tensor>("Label");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
"inference size must be the same as label size");
ctx.Output<Tensor>("Accuracy")->Resize({1});
}
};
class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AccuracyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
// TODO(typhoonzero): support both inference value and indices.
AddInput("Inference", "topk(indices) the network output");
AddInput("Label", "Label of the training data");
// TODO(typhoonzero): AddInput("Weight", ...
AddOutput("Accuracy", "The accuracy of current batch");
AddComment(
R"DOC(Accuracy. It will print accuracy rate for classification.
The accuracy is:
.. math::
accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples})DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker);
REGISTER_OP_CPU_KERNEL(accuracy,
ops::AccuracyKernel<paddle::platform::CPUPlace, float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/accuracy_op.h"
namespace paddle {
namespace operators {
__global__ void AccuracySingleKernel(const int N, const int D, const int top_k,
const int* Xdata, const int* labelData,
float* accuracy) {
int correct = 0;
for (int row = 0; row < N; row++) {
const int label = labelData[row];
for (int col = 0; col < D; col++) {
const int pred = Xdata[row * D + col];
if (pred == label) {
++correct;
break;
}
}
}
*accuracy = static_cast<float>(correct) / static_cast<float>(N);
}
template <typename T>
class AccuracyOpCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto* inference = ctx.Input<Tensor>("Inference");
auto* label = ctx.Input<Tensor>("Label");
auto* accuracy = ctx.Output<Tensor>("Accuracy");
// FIXME(typhoonzero): only support indices currently
// if add support for output values, how to detect the data type?
const int* inference_data = inference->data<int>();
const int* label_data = label->data<int>();
float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
size_t num_samples = inference->dims()[0];
size_t infer_width = inference->dims()[1];
cudaMemset((void**)&accuracy_data, 0, sizeof(float));
if (num_samples == 0) {
return;
}
AccuracySingleKernel<<<1, 1>>>(num_samples, infer_width, 1, inference_data,
label_data, accuracy_data);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(accuracy,
paddle::operators::AccuracyOpCUDAKernel<float>);
/* 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 <algorithm>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
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, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename Place, typename T>
class AccuracyKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* inference = ctx.Input<Tensor>("Inference");
auto* label = ctx.Input<Tensor>("Label");
auto* accuracy = ctx.Output<Tensor>("Accuracy");
float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
const T* inference_data = inference->data<T>();
const T* label_data = label->data<T>();
size_t num_samples = inference->dims()[0];
size_t class_dim = inference->dims()[1];
*accuracy_data = 0.0f;
if (num_samples == 0) {
return;
}
int num_correct = 0;
// assume inference is already the topk of the output
for (size_t i = 0; i < num_samples; ++i) {
PADDLE_ENFORCE_GE(label_data[i], 0, "label must >= 0");
for (size_t j = 0; j < class_dim; ++j) {
if (inference_data[i * class_dim + j] == label_data[i]) {
++num_correct;
break;
}
}
}
// FIXME(typhoonzero): we don't accumulate the accuracy for now.
*accuracy_data =
static_cast<float>(num_correct) / static_cast<float>(num_samples);
}
};
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/elementwise_mul_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class ElementWiseMulOp : public framework::OperatorWithKernel {
public:
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");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto y_dim = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.")
ctx.Output<Tensor>("Out")->Resize(x_dim);
}
};
class ElementWiseMulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ElementWiseMulOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of elementwise mul op");
AddInput("Y", "The second input of elementwise mul op");
AddAttr<int>("axis",
R"DOC(
When shape(Y) does not equal shape(X),Y will be broadcasted
to match the shape of X and axis should be dimension index Y in X
)DOC")
.SetDefault(-1)
.EqualGreaterThan(-1);
AddOutput("Out", "The output of elementwise mul op");
AddComment(R"DOC(
Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
1. The shape of Y should be same with X or
2. Y's shape is a subset of X.
Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
example:
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
)DOC");
}
};
class ElementWiseMulOpGrad : public framework::OperatorWithKernel {
public:
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")),
"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<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
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);
}
if (y_grad) {
y_grad->Resize(y_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(elementwise_mul, ops::ElementWiseMulOp, ops::ElementWiseMulOpMaker,
elementwise_mul_grad, ops::ElementWiseMulOpGrad);
REGISTER_OP_CPU_KERNEL(
elementwise_mul,
ops::ElementWiseMulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
elementwise_mul_grad,
ops::ElementWiseMulGradKernel<paddle::platform::CPUPlace, float>);
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/elementwise_mul_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
elementwise_mul,
ops::ElementWiseMulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
elementwise_mul_grad,
ops::ElementWiseMulGradKernel<paddle::platform::GPUPlace, float>);
/* 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 <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
/*
* Out = X ⊙ Y
* 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
* pre=2, n=3*4, post=5
* 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
* pre=2*3, n=4*5, post=1
*/
inline void get_mid_dims(const framework::DDim& x_dims,
const framework::DDim& y_dims, const int axis,
int& pre, int& n, int& post) {
pre = 1;
n = 1;
post = 1;
for (int i = 0; i < axis; ++i) {
pre *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
"Broadcast dimension mismatch.");
n *= y_dims[i];
}
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
post *= x_dims[i];
}
}
template <typename Place, typename T>
class ElementWiseMulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* z = ctx.Output<Tensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto z_e = framework::EigenVector<T>::Flatten(*z);
auto x_dims = x->dims();
auto y_dims = y->dims();
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Rank of first input must >= rank of second input.")
if (x_dims == y_dims || product(y_dims) == 1) {
z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_e;
return;
}
int axis = ctx.Attr<int>("axis");
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
"Axis should be in range [0, x_dims)");
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, pre, n, post);
if (post == 1) {
auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
.broadcast(Eigen::DSizes<int, 2>(pre, 1))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_bcast;
return;
} else {
auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
.broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
z_e.device(ctx.GetEigenDevice<Place>()) = x_e * y_bcast;
return;
}
}
};
template <typename Place, typename T>
class ElementWiseMulGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto x_e = framework::EigenVector<T>::Flatten(*x);
auto y_e = framework::EigenVector<T>::Flatten(*y);
auto dout_e = framework::EigenVector<T>::Flatten(*dout);
auto x_dims = x->dims();
auto y_dims = y->dims();
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
if (dx) {
dx->mutable_data<T>(ctx.GetPlace());
}
if (dy) {
dy->mutable_data<T>(ctx.GetPlace());
}
if (x_dims == y_dims || product(y_dims) == 1) {
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(ctx.GetEigenDevice<Place>()) = x_e * dout_e;
}
return;
}
int axis = ctx.Attr<int>("axis");
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, pre, n, post);
// TODO(gongweibao): wrap reshape to a function.
if (post == 1) {
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
.broadcast(Eigen::DSizes<int, 2>(pre, 1))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e_bcast;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(ctx.GetEigenDevice<Place>()) =
(x_e * dout_e)
.reshape(Eigen::DSizes<int, 2>(pre, n))
.sum(Eigen::array<int, 1>{{0}});
}
return;
} else {
auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
.broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
.reshape(Eigen::DSizes<int, 1>(x_e.size()));
if (dx) {
auto dx_e = framework::EigenVector<T>::Flatten(*dx);
dx_e.device(ctx.GetEigenDevice<Place>()) = dout_e * y_e_bcast;
}
if (dy) {
auto dy_e = framework::EigenVector<T>::Flatten(*dy);
dy_e.device(ctx.GetEigenDevice<Place>()) =
(x_e * dout_e)
.reshape(Eigen::DSizes<int, 3>(pre, n, post))
.sum(Eigen::array<int, 2>{{0, 2}});
}
return;
}
}
};
} // namespace operators
} // namespace paddle
......@@ -38,9 +38,11 @@ public:
AccumulateOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor.
If the output size is not the same as input size,
the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
AddOutput("Out", "(Tensor) Accumulated output tensor");
AddAttr<float>("gamma", "(float, default 1.0) Accumulation multiplier");
AddAttr<float>("gamma", "(float, default 1.0) Accumulation multiplier").SetDefault(1.0f);
AddComment(R"DOC(
Accumulate operator accumulates the input tensor to the output tensor. If the
output tensor already has the right size, we add to it; otherwise, we first
......@@ -51,7 +53,7 @@ Accumulation is done as shown:
Out = 1*X + gamma*Out
where X is the input tensor, Y is the output tensor and gamma is the multiplier
where X is the input tensor, Out is the output tensor and gamma is the multiplier
argument.
)DOC");
}
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/pad_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class PadOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto paddings = Attr<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.");
std::vector<int64_t> out_dims(x_dim.size());
for (int i = 0; i < x_dim.size(); ++i) {
out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
}
ctx.Output<Tensor>("Out")->Resize(framework::make_ddim(out_dims));
}
};
class PadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PadOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddOutput("Out",
"The output of pad op."
"A tensor with the same shape as X.")
.NotInGradient();
AddComment(R"DOC(
Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
X = [[1, 2],
[3, 4]]
and
paddings = [0, 1, 1, 2]
and
pad_value = 0
then we get
Out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
)DOC");
AddAttr<std::vector<int>>(
"paddings",
"A list<int> to describes padding rules for each dimension."
" For 2-D image tensor, paddings=[0, 1, 2, 3] means"
" padding 0 row to top, 1 row to bottom, 2 columns to left"
" and 3 columns to right.Size of paddings should be equal to"
" 2 * dimension size of input tensor.");
AddAttr<float>("pad_value",
"(float) default to 0; "
"The value to fill padded areas.")
.SetDefault(0.0f);
}
};
class PadOpGrad : public framework::OperatorWithKernel {
public:
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")),
"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);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad);
REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(pad_grad,
ops::PadGradKernel<paddle::platform::CPUPlace, float>);
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/pad_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pad, ops::PadKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(pad_grad,
ops::PadGradKernel<paddle::platform::GPUPlace, float>);
/* 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/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename Place, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
T pad_value = context.Attr<T>("pad_value");
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto x_tensor = EigenTensor<T, D>::From(*x);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto place = context.GetEigenDevice<Place>();
out_tensor.device(place) = x_tensor.pad(paddings, pad_value);
}
template <typename Place, typename T>
class PadKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
int rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
case 1:
PadFunction<Place, T, 1>(context);
break;
case 2:
PadFunction<Place, T, 2>(context);
break;
case 3:
PadFunction<Place, T, 3>(context);
break;
case 4:
PadFunction<Place, T, 4>(context);
break;
case 5:
PadFunction<Place, T, 5>(context);
break;
case 6:
PadFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
template <typename Place, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
d_x->mutable_data<T>(context.GetPlace());
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto place = context.GetEigenDevice<Place>();
d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
}
}
template <typename Place, typename T>
class PadGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
size_t rank =
context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
switch (rank) {
case 1:
PadGradFunction<Place, T, 1>(context);
break;
case 2:
PadGradFunction<Place, T, 2>(context);
break;
case 3:
PadGradFunction<Place, T, 3>(context);
break;
case 4:
PadGradFunction<Place, T, 4>(context);
break;
case 5:
PadGradFunction<Place, T, 5>(context);
break;
case 6:
PadGradFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
} // namespace operators
} // namespace paddle
......@@ -35,6 +35,7 @@ USE_OP(add);
USE_OP(onehot_cross_entropy);
USE_OP(sgd);
USE_OP(mul);
USE_OP(elementwise_mul);
USE_OP(mean);
USE_OP(sigmoid);
USE_OP(softmax);
......@@ -49,7 +50,9 @@ USE_NO_KERNEL_OP(identity);
USE_OP(minus);
USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_OP(pad);
USE_CPU_ONLY_OP(scatter);
USE_OP(accuracy);
USE_CPU_ONLY_OP(concat);
USE_OP(top_k);
USE_OP(squared_l2_distance);
......
......@@ -2,8 +2,30 @@
set -xe
if [ $ANDROID_ABI == "arm64-v8a" ]; then
ANDROID_ARCH=arm64
else # armeabi, armeabi-v7a
ANDROID_ARCH=arm
fi
ANDROID_STANDALONE_TOOLCHAIN=$ANDROID_TOOLCHAINS_DIR/$ANDROID_ARCH-android-$ANDROID_API
cat <<EOF
============================================
Generating the standalone toolchain ...
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh
--arch=$ANDROID_ARCH
--platform=android-$ANDROID_API
--install-dir=${ANDROID_STANDALONE_TOOLCHAIN}
============================================
EOF
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh \
--arch=$ANDROID_ARCH \
--platform=android-$ANDROID_API \
--install-dir=$ANDROID_STANDALONE_TOOLCHAIN
BUILD_ROOT=/paddle/build_android
DEST_ROOT=/paddle/install
DEST_ROOT=/paddle/install_android
rm -rf $BUILD_ROOT 2>/dev/null || true
mkdir -p $BUILD_ROOT
......@@ -11,7 +33,7 @@ cd $BUILD_ROOT
if [ $ANDROID_ABI == "armeabi-v7a" ]; then
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \
-DANDROID_ABI=$ANDROID_ABI \
-DANDROID_ARM_NEON=ON \
-DANDROID_ARM_MODE=ON \
......@@ -26,7 +48,7 @@ if [ $ANDROID_ABI == "armeabi-v7a" ]; then
..
elif [ $ANDROID_ABI == "arm64-v8a" ]; then
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \
-DANDROID_ABI=$ANDROID_ABI \
-DANDROID_ARM_MODE=ON \
-DHOST_C_COMPILER=/usr/bin/gcc \
......@@ -40,12 +62,12 @@ elif [ $ANDROID_ABI == "arm64-v8a" ]; then
..
elif [ $ANDROID_ABI == "armeabi" ]; then
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \
-DANDROID_ABI=$ANDROID_ABI \
-DANDROID_ARM_MODE=ON \
-DHOST_C_COMPILER=/usr/bin/gcc \
-DHOST_CXX_COMPILER=/usr/bin/g++ \
-DCMAKE_INSTALL_PREFIX=/paddle/install \
-DCMAKE_INSTALL_PREFIX=$DEST_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
......@@ -55,5 +77,10 @@ else
echo "Invalid ANDROID_ABI: $ANDROID_ABI"
fi
cat <<EOF
============================================
Building in $BUILD_ROOT ...
============================================
EOF
make -j `nproc`
make install -j `nproc`
......@@ -169,6 +169,7 @@ class LayerType(object):
EXCONV_LAYER = 'exconv'
EXCONVTRANS_LAYER = 'exconvt'
CUDNNCONV_LAYER = 'cudnn_conv'
CUDNNCONVTRANS_LAYER = 'cudnn_convt'
POOL_LAYER = 'pool'
POOL3D_LAYER = 'pool3d'
BATCH_NORM_LAYER = 'batch_norm'
......
......@@ -97,7 +97,7 @@ class OpDescCreationMethod(object):
new_attr.strings.extend(user_defined_attr)
elif attr.type == framework_pb2.INT_PAIRS:
for p in user_defined_attr:
pair = new_attr.pairs.add()
pair = new_attr.int_pairs.add()
pair.first = p[0]
pair.second = p[1]
else:
......
......@@ -85,7 +85,7 @@ def get_numeric_gradient(scope,
op,
inputs,
input_to_check,
output_name,
output_names,
delta=0.005,
in_place=False):
......@@ -100,8 +100,11 @@ def get_numeric_gradient(scope,
ctx = core.DeviceContext.create(core.CPUPlace())
def get_output():
sum = 0.0
for output_name in output_names:
op.run(scope, ctx)
return np.array(scope.find_var(output_name).get_tensor()).sum()
sum += np.array(scope.find_var(output_name).get_tensor()).sum()
return sum
tensor_to_check = scope.find_var(input_to_check).get_tensor()
tensor_size = product(tensor_to_check.get_dims())
......@@ -225,7 +228,7 @@ class OpTest(unittest.TestCase):
def check_grad(self,
inputs_to_check,
output_name,
output_names,
no_grad_set=None,
in_place=False,
max_relative_error=0.005):
......@@ -237,13 +240,16 @@ class OpTest(unittest.TestCase):
if no_grad_set is None:
no_grad_set = set()
if not type(output_names) is list:
output_names = [output_names]
numeric_grads = [
get_numeric_gradient(
self.scope,
self.op,
self.inputs,
input_to_check,
output_name,
output_names,
in_place=in_place) for input_to_check in inputs_to_check
]
grad_names = [
......
import unittest
import numpy as np
from op_test import OpTest
class TestAccuracyOp(OpTest):
def setUp(self):
self.op_type = "accuracy"
infer = np.random.randint(0, 2, (32, 1)).astype("int")
label = np.random.randint(0, 2, (32, )).astype("int")
self.inputs = {'Inference': infer, "Label": label}
num_correct = 0
for rowid in xrange(32):
for ele in infer[rowid]:
if ele == label[rowid]:
num_correct += 1
break
self.outputs = {'Accuracy': [num_correct / 32.0]}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
import unittest
import numpy as np
from op_test import OpTest
class TestElementwiseMulOp_Matrix(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
""" Warning
CPU gradient check error!
'X': np.random.random((32,84)).astype("float32"),
'Y': np.random.random((32,84)).astype("float32")
"""
self.inputs = {
'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
}
self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
class TestElementwiseMulOp_Vector(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.random((32, )).astype("float32"),
'Y': np.random.random((32, )).astype("float32")
}
self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
class TestElementwiseMulOp_broadcast_0(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(2).astype(np.float32)
}
self.attrs = {'axis': 0}
self.outputs = {
'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
class TestElementwiseMulOp_broadcast_1(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(3).astype(np.float32)
}
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 1)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
class TestElementwiseMulOp_broadcast_2(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(4).astype(np.float32)
}
self.outputs = {
'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 4)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
class TestElementwiseMulOp_broadcast_3(OpTest):
def setUp(self):
self.op_type = "elementwise_mul"
self.inputs = {
'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
'Y': np.random.rand(3, 4).astype(np.float32)
}
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 4, 1)
}
if __name__ == '__main__':
unittest.main()
......@@ -12,7 +12,8 @@ class GetNumericGradientTest(unittest.TestCase):
z = x + y
scope = core.Scope()
add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict())
arr = get_numeric_gradient(scope, add_op, {'X': x, 'Y': y}, 'X', 'Out')
arr = get_numeric_gradient(scope, add_op, {'X': x,
'Y': y}, 'X', ['Out'])
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
def test_softmax_op(self):
......
import unittest
import numpy as np
from op_test import OpTest
class TestPadOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "pad"
self.inputs = {'X': np.random.random(self.shape).astype("float32"), }
self.attrs = {}
self.attrs['paddings'] = np.array(self.paddings).flatten()
self.attrs['pad_value'] = self.pad_value
self.outputs = {
'Out': np.pad(self.inputs['X'],
self.paddings,
mode='constant',
constant_values=self.pad_value)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out')
def initTestCase(self):
self.shape = (16, 16)
self.paddings = [(0, 1), (2, 3)]
self.pad_value = 0
class TestCase1(TestPadOp):
def initTestCase(self):
self.shape = (2, 3, 4, 4)
self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)]
self.pad_value = 0.5
class TestCase2(TestPadOp):
def initTestCase(self):
self.shape = (2, 2, 2)
self.paddings = [(0, 0), (0, 0), (1, 2)]
self.pad_value = 1
class TestCase3(TestPadOp):
def initTestCase(self):
self.shape = (8)
self.paddings = [(0, 1)]
self.pad_value = 0.9
if __name__ == '__main__':
unittest.main()
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