diff --git a/Dockerfile b/Dockerfile
index 402adee2ea2822250ebc8f6229fd6a44545d58e5..634be18a51bf61e96a8bf6f263b6674a7932d6e4 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -53,7 +53,7 @@ RUN curl -s -q https://glide.sh/get | sh
# and its size is only one-third of the official one.
# 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle.
# See https://github.com/PaddlePaddle/Paddle/issues/10129 for details.
-RUN wget -qO- http://paddlepaddledeps.bj.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
+RUN wget -qO- http://paddlepaddledeps.cdn.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
tar -xz -C /usr/local && \
cp -rf /usr/local/TensorRT/include /usr && \
cp -rf /usr/local/TensorRT/lib /usr
diff --git a/cmake/inference_lib.cmake b/cmake/inference_lib.cmake
index bc36683a9facc253e7b9feb0c5a56e79491fb9b0..f61770514eb05a99c140cdb18575c89aa5235c14 100644
--- a/cmake/inference_lib.cmake
+++ b/cmake/inference_lib.cmake
@@ -128,16 +128,13 @@ set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
set(module "framework")
if (NOT WIN32)
-copy(framework_lib DEPS framework_py_proto
- SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
- DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
-)
-else()
-copy(framework_lib
+set(framework_lib_deps framework_py_proto)
+endif(NOT WIN32)
+copy(framework_lib DEPS ${framework_lib_deps}
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
- DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
+ ${src_dir}/${module}/ir/*.h
+ DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module} ${dst_dir}/${module}/ir
)
-endif(NOT WIN32)
set(module "memory")
copy(memory_lib
@@ -161,7 +158,8 @@ set(module "inference")
copy(inference_lib DEPS ${inference_deps}
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
${src_dir}/${module}/api/paddle_inference_api.h ${src_dir}/${module}/api/demo_ci
- DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
+ ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
+ DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
)
set(module "platform")
diff --git a/doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md b/doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md
index fa2b930be0d26d816566599cece8afbedc1157e0..6e5f77fec8a894c390ced8c93ee344fd8d27370e 100644
--- a/doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md
+++ b/doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md
@@ -60,6 +60,7 @@
图3. 编码器-解码器框架
+
#### 编码器
编码阶段分为三步：
@@ -81,7 +82,7 @@
机器翻译任务的训练过程中，解码阶段的目标是最大化下一个正确的目标语言词的概率。思路是：
1. 每一个时刻，根据源语言句子的编码信息（又叫上下文向量，context vector）`$c$`、真实目标语言序列的第`$i$`个词`$u_i$`和`$i$`时刻RNN的隐层状态`$z_i$`，计算出下一个隐层状态`$z_{i+1}$`。计算公式如下：
$$z_{i+1}=\phi_{\theta '} \left ( c,u_i,z_i \right )$$
-其中`$\phi _{\theta '}$`是一个非线性激活函数；`$c=q\mathbf{h}$`是源语言句子的上下文向量，在不使用[注意力机制](#注意力机制)时，如果[编码器](#编码器)的输出是源语言句子编码后的最后一个元素，则可以定义`$c=h_T$`；`$u_i$`是目标语言序列的第`$i$`个单词，`$u_0$`是目标语言序列的开始标记``，表示解码开始；`$z_i$`是`$i$`时刻解码RNN的隐层状态，`$z_0$`是一个全零的向量。
+其中`$\phi _{\theta '}$`是一个非线性激活函数；`$c=q\mathbf{h}$`是源语言句子的上下文向量，在不使用注意力机制时，如果[编码器](#编码器)的输出是源语言句子编码后的最后一个元素，则可以定义`$c=h_T$`；`$u_i$`是目标语言序列的第`$i$`个单词，`$u_0$`是目标语言序列的开始标记``，表示解码开始；`$z_i$`是`$i$`时刻解码RNN的隐层状态，`$z_0$`是一个全零的向量。
2. 将`$z_{i+1}$`通过`softmax`归一化，得到目标语言序列的第`$i+1$`个单词的概率分布`$p_{i+1}$`。概率分布公式如下：
$$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
@@ -93,6 +94,7 @@ $$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
机器翻译任务的生成过程，通俗来讲就是根据预先训练的模型来翻译源语言句子。生成过程中的解码阶段和上述训练过程的有所差异，具体介绍请见[柱搜索算法](#柱搜索算法)。
+
### 柱搜索算法
柱搜索（[beam search](http://en.wikipedia.org/wiki/Beam_search)）是一种启发式图搜索算法，用于在图或树中搜索有限集合中的最优扩展节点，通常用在解空间非常大的系统（如机器翻译、语音识别）中，原因是内存无法装下图或树中所有展开的解。如在机器翻译任务中希望翻译“`你好`”，就算目标语言字典中只有3个词（``, ``, `hello`），也可能生成无限句话（`hello`循环出现的次数不定），为了找到其中较好的翻译结果，我们可采用柱搜索算法。
diff --git a/doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md b/doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md
index 9900dfb9a67dc6f8940bd7dd3abfa15ac8a3488f..8477cf32146c33947ced447c8bdd287a3e1e71f5 100644
--- a/doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md
+++ b/doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md
@@ -149,6 +149,8 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
网络的输入`input_dim`表示的是词典的大小，`class_dim`表示类别数。这里，我们使用[`sequence_conv_pool`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/trainer_config_helpers/networks.py) API实现了卷积和池化操作。
+
+
### 栈式双向LSTM
栈式双向神经网络`stacked_lstm_net`的代码片段如下：
diff --git a/doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md b/doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md
index 2c68cdac4f10319359b74bc92569dfd3f65380b5..904d99fe2ffc9ead69a86c9763568a5c098348d5 100644
--- a/doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md
+++ b/doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md
@@ -50,7 +50,7 @@ similarity: -0.0997506977351
```
-以上结果可以通过运行`calculate_dis.py`, 加载字典里的单词和对应训练特征结果得到，我们将在[应用模型](#应用模型)中详细描述用法。
+以上结果可以通过运行`calculate_dis.py`, 加载字典里的单词和对应训练特征结果得到，我们将在[模型应用](#模型应用)中详细描述用法。
## 模型概览
@@ -189,6 +189,7 @@ dream that one day
最后，每个输入会按其单词次在字典里的位置，转化成整数的索引序列，作为PaddlePaddle的输入。
+
## 编程实现
本配置的模型结构如下图所示：
@@ -349,6 +350,7 @@ Step 20: Average Cost 5.766995
...
```
+
## 模型应用
在模型训练后，我们可以用它做一些预测。
diff --git a/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md b/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
index e6f89b23a95d1a07565f3e0a285e9c3f921930df..ac36c4ecf6b9b716fe5f0dbe2346e64918c22242 100644
--- a/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
+++ b/doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
@@ -102,7 +102,7 @@ Softmax回归模型采用了最简单的两层神经网络，即只有输入层
池化是非线性下采样的一种形式，主要作用是通过减少网络的参数来减小计算量，并且能够在一定程度上控制过拟合。通常在卷积层的后面会加上一个池化层。池化包括最大池化、平均池化等。其中最大池化是用不重叠的矩形框将输入层分成不同的区域，对于每个矩形框的数取最大值作为输出层，如图6所示。
-更详细的关于卷积神经网络的具体知识可以参考[斯坦福大学公开课]( http://cs231n.github.io/convolutional-networks/ )和[图像分类](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md)教程。
+更详细的关于卷积神经网络的具体知识可以参考[斯坦福大学公开课]( http://cs231n.github.io/convolutional-networks/ )和[图像分类]( https://github.com/PaddlePaddle/book/tree/develop/03.image_classification )教程。
### 常见激活函数介绍
- sigmoid激活函数： $ f(x) = sigmoid(x) = \frac{1}{1+e^{-x}} $
diff --git a/doc/fluid/new_docs/user_guides/howto/debug/visualdl.md b/doc/fluid/new_docs/user_guides/howto/debug/visualdl.md
index a2f30823a6fcd379f94e6e98d043b0d00681827f..84987ea5daee9abd0fe2fe71bdfde62ea3388ab5 100644
--- a/doc/fluid/new_docs/user_guides/howto/debug/visualdl.md
+++ b/doc/fluid/new_docs/user_guides/howto/debug/visualdl.md
@@ -149,7 +149,7 @@ python setup.py bdist_wheel
pip install --upgrade dist/visualdl-*.whl
```
-如果打包和安装遇到其他问题，不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/how_to_dev_frontend_en.md)
+如果打包和安装遇到其他问题，不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/develop/how_to_dev_frontend_cn.md)
## SDK
diff --git a/doc/fluid/new_docs/user_guides/howto/inference/native_infer.rst b/doc/fluid/new_docs/user_guides/howto/inference/native_infer.rst
index 21a6fe5cf54d0c0c760ade4ba602024ffa29675f..6d6f3035c0b5c985cd39d45df9f1bcce50dcefa0 100644
--- a/doc/fluid/new_docs/user_guides/howto/inference/native_infer.rst
+++ b/doc/fluid/new_docs/user_guides/howto/inference/native_infer.rst
@@ -4,13 +4,12 @@ Paddle 预测 API
为了更简单方便的预测部署，Fluid 提供了一套高层 API
用来隐藏底层不同的优化实现。
-`预测库相关代码 `__
+`预测库相关代码 `_
包括
- 头文件 ``paddle_inference_api.h`` 定义了所有的接口
- 库文件\ ``libpaddle_fluid.so`` 或 ``libpaddle_fluid.a``
-- 库文件 ``libpaddle_inference_api.so`` 或
- ``libpaddle_inference_api.a``
+
编译和依赖可以参考 :ref:`install_or_build_cpp_inference_lib` 。
@@ -97,8 +96,7 @@ engine
CHECK(predictor->Run(slots, &outputs));
// 获取 outputs ...
-编译时，联编 ``libpaddle_fluid.a/.so`` 和
-``libpaddle_inference_api.a/.so`` 便可。
+编译时，联编 ``libpaddle_fluid.a/.so`` 便可。
详细代码参考
------------
diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index bb5f2894c08b5d8941ad8914f6b83280aa053e37..70e5b97770a6c581c6a9c0145b03c42b83f14471 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -312,7 +312,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kw
paddle.fluid.layers.box_coder ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.polygon_box_transform ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
-paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 200, 1))
+paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 4095, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
@@ -376,7 +376,7 @@ paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'l
paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power'], varargs=None, keywords='kwargs', defaults=(0.0, 0.0, -0.5))
paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum'], varargs=None, keywords='kwargs', defaults=(0.95, 1e-06, 0.0))
+paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered'], varargs=None, keywords='kwargs', defaults=(0.95, 1e-06, 0.0, False))
paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho'], varargs=None, keywords='kwargs', defaults=(1e-06, 0.95))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
diff --git a/paddle/fluid/framework/details/multi_devices_graph_pass.cc b/paddle/fluid/framework/details/multi_devices_graph_pass.cc
index 0bfff745493d069e948e6d277ec2bbfb0673a70b..7a99169849debcbc57d6f197b36c5045b211f3ef 100644
--- a/paddle/fluid/framework/details/multi_devices_graph_pass.cc
+++ b/paddle/fluid/framework/details/multi_devices_graph_pass.cc
@@ -326,7 +326,7 @@ std::unique_ptr MultiDevSSAGraphBuilder::ApplyImpl(
ir::Graph &result = *graph;
for (auto &node : nodes) {
- if (node->NodeType() == ir::Node::Type::kVariable && node->Var()) {
+ if (node->IsVar() && node->Var()) {
all_vars_.emplace(node->Name(), node->Var());
}
}
@@ -583,18 +583,6 @@ void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
}
}
-bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
- const std::string &og,
- std::unordered_set *og_has_been_broadcast) const {
- bool is_pg_once =
- grad_names_.count(og) != 0 && og_has_been_broadcast->count(og) == 0;
- if (is_pg_once) {
- // Insert NCCL AllReduce Op
- og_has_been_broadcast->insert(og);
- }
- return is_pg_once;
-}
-
int MultiDevSSAGraphBuilder::GetOpDeviceID(const ir::Graph &graph,
ir::Node *node) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
@@ -688,20 +676,6 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
return var;
}
-// Find the first occurence of `prev_op_name` and make current `op` depend
-// on it.
-void MultiDevSSAGraphBuilder::ConnectOp(ir::Graph *result, OpHandleBase *op,
- const std::string &prev_op_name) const {
- for (auto &prev_op : result->Get(kGraphOps)) {
- if (prev_op->Name() == prev_op_name) {
- auto *dep_var = new DummyVarHandle(result->CreateControlDepVar());
- prev_op->AddOutput(dep_var);
- result->Get(kGraphDepVars).emplace(dep_var);
- op->AddInput(dep_var);
- }
- }
-}
-
void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
ir::Node *node) const {
int op_dev_id = -1;
diff --git a/paddle/fluid/framework/details/multi_devices_graph_pass.h b/paddle/fluid/framework/details/multi_devices_graph_pass.h
index 7a6f238f9cf7af18cb10ea271e453fec1902c833..ac6d9c5a64cfde60f75c76dae0a30cc7d735e996 100644
--- a/paddle/fluid/framework/details/multi_devices_graph_pass.h
+++ b/paddle/fluid/framework/details/multi_devices_graph_pass.h
@@ -69,9 +69,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
std::vector FindDistTrainRecvVars(
const std::vector &nodes) const;
- void ConnectOp(ir::Graph *result, OpHandleBase *op,
- const std::string &prev_op_name) const;
-
void CreateComputationalOps(ir::Graph *result, ir::Node *node,
size_t num_places) const;
@@ -83,10 +80,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
void CreateComputationalOp(ir::Graph *result, ir::Node *node,
int dev_id) const;
- bool IsParameterGradientOnce(
- const std::string &og,
- std::unordered_set *og_has_been_broadcast) const;
-
int GetOpDeviceID(const ir::Graph &graph, ir::Node *node) const;
void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
diff --git a/paddle/fluid/framework/ir/CMakeLists.txt b/paddle/fluid/framework/ir/CMakeLists.txt
index bfc649017f19d67660bd11d590134cf56772bb27..f5235f70ad79616801110644999d511eeda33a32 100644
--- a/paddle/fluid/framework/ir/CMakeLists.txt
+++ b/paddle/fluid/framework/ir/CMakeLists.txt
@@ -1,20 +1,35 @@
+set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
+file(WRITE ${pass_file} "// Generated by the paddle/fluid/framework/ir/CMakeLists.txt. DO NOT EDIT!\n\n")
+file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
+function(pass_library TARGET)
+ set(options "")
+ set(oneValueArgs "")
+ set(multiValueArgs SRCS DEPS)
+ cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+ cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass)
+ file(APPEND ${pass_file} "USE_PASS(${TARGET});\n")
+ set(PASS_LIBRARY ${TARGET} ${PASS_LIBRARY} PARENT_SCOPE)
+endfunction()
+
cc_library(node SRCS node.cc DEPS proto_desc)
cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
-cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
-cc_library(graph_to_program_pass SRCS graph_to_program_pass.cc DEPS graph pass graph_helper)
cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph graph_helper graph_traits)
-cc_library(fc_fuse_pass SRCS fc_fuse_pass.cc DEPS graph graph_pattern_detector)
-cc_library(attention_lstm_fuse_pass SRCS attention_lstm_fuse_pass.cc DEPS graph graph_pattern_detector)
-cc_library(infer_clean_graph_pass SRCS infer_clean_graph_pass.cc DEPS graph pass)
-cc_library(fc_lstm_fuse_pass SRCS fc_lstm_fuse_pass.cc DEPS graph graph_pattern_detector)
-cc_library(seq_concat_fc_fuse_pass SRCS seq_concat_fc_fuse_pass.cc DEPS graph graph_pattern_detector)
+
+pass_library(graph_to_program_pass)
+pass_library(graph_viz_pass)
+pass_library(fc_fuse_pass)
+pass_library(attention_lstm_fuse_pass)
+pass_library(infer_clean_graph_pass)
+pass_library(fc_lstm_fuse_pass)
+pass_library(seq_concat_fc_fuse_pass)
+set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
-cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass graph_pattern_detector graph pass graph_traits framework_proto)
+cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
diff --git a/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc b/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
index 0278ade6763ec614701674691797d766878a378e..d7580a1cfd689c122ea1e7db5918e2cd8711718f 100644
--- a/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
+++ b/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
@@ -99,17 +99,13 @@ void FindWhileOp(Graph* graph) {
auto* cell_init = graph->RetriveNode(6);
auto* hidden_init = graph->RetriveNode(8);
-#define LINK_TO(node0, node1) \
- node0->outputs.push_back(node1); \
- node1->inputs.push_back(node0);
-
auto* lstm_op = graph->CreateOpNode(&op_desc);
PrepareParameters(graph, param);
- LINK_TO(X, lstm_op);
- LINK_TO(cell_init, lstm_op);
- LINK_TO(hidden_init, lstm_op);
- LINK_TO(lstm_op, LSTMOUT);
+ IR_NODE_LINK_TO(X, lstm_op);
+ IR_NODE_LINK_TO(cell_init, lstm_op);
+ IR_NODE_LINK_TO(hidden_init, lstm_op);
+ IR_NODE_LINK_TO(lstm_op, LSTMOUT);
GraphSafeRemoveNodes(graph, marked_nodes);
}
diff --git a/paddle/fluid/framework/ir/fc_fuse_pass.cc b/paddle/fluid/framework/ir/fc_fuse_pass.cc
index 513742bab69d465aac1bfb7bcef2fe89108c14a0..5a4ebd6f3de555acccd72c61bd377ffd8ce69780 100644
--- a/paddle/fluid/framework/ir/fc_fuse_pass.cc
+++ b/paddle/fluid/framework/ir/fc_fuse_pass.cc
@@ -21,74 +21,26 @@ namespace paddle {
namespace framework {
namespace ir {
-bool VarOutLinksToOp(Node* node, const std::string& op_type) {
- for (auto* out : node->outputs) {
- if (out->IsOp() && out->Op()->Type() == op_type) {
- return true;
- }
- }
- return false;
-}
-
-void BuildFCPattern(PDPattern* pattern) {
- // Create Operators
- auto* mul_op = pattern->NewNode("mul")->assert_is_op("mul");
- auto* elementwise_add_op =
- pattern->NewNode("elementwise_add")->assert_is_op("elementwise_add");
- // Create variables
- // w
- auto* mul_weight_var = pattern->NewNode("mul_weight")
- ->AsInput()
- ->assert_is_op_nth_input("mul", "Y", 0);
- // x
- auto* mul_tmp_var = pattern->NewNode("mul_tmp_var")
- ->AsInput()
- ->assert_is_op_nth_input("mul", "X", 0);
- // intermediate variable, will be removed in the IR after fuse.
- auto* mul_out_var = pattern->NewNode("mul_out")
- ->AsIntermediate()
- ->assert_is_only_output_of_op("mul")
- ->assert_is_op_input("elementwise_add");
- // bias
- auto* elementwise_add_tmp_var = pattern->NewNode("elementwise_add_tmpvar")
- ->assert_is_op_input("elementwise_add")
- ->AsInput();
- // output
- auto* elementwise_add_out_var = pattern->NewNode("elementwise_add_out")
- ->AsOutput()
- ->assert_is_op_output("elementwise_add");
-
- mul_op->LinksFrom({mul_weight_var, mul_tmp_var}).LinksTo({mul_out_var});
- elementwise_add_op->LinksFrom({mul_out_var, elementwise_add_tmp_var})
- .LinksTo({elementwise_add_out_var});
-}
-
-// Replace the node `from` in the links to `to`
-bool LinksReplace(std::vector* links, Node* from, Node* to) {
- for (auto*& n : *links) {
- if (n == from) {
- n = to;
- return true;
- }
- }
- return false;
-}
-
std::unique_ptr FCFusePass::ApplyImpl(
std::unique_ptr graph) const {
PADDLE_ENFORCE(graph.get());
- FusePassBase::Init("fc", graph.get());
+ FusePassBase::Init("fc_fuse", graph.get());
std::unordered_set nodes2delete;
GraphPatternDetector gpd;
- BuildFCPattern(gpd.mutable_pattern());
-
-#define GET_NODE(id) \
- PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode(#id)), \
- "pattern has no Node called %s", #id); \
- auto* id = subgraph.at(gpd.pattern().RetrieveNode(#id)); \
- PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
+ // BuildFCPattern(gpd.mutable_pattern());
+ auto* x = gpd.mutable_pattern()
+ ->NewNode("fc_fuse/x")
+ ->AsInput()
+ ->assert_is_op_input("mul", "X");
+ patterns::FC(gpd.mutable_pattern(), "fc_fuse", x, true /*with bias*/);
+
+#define GET_NODE(id) \
+ PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode("fc_fuse/" #id)), \
+ "pattern has no Node called %s", #id); \
+ auto* id = subgraph.at(gpd.pattern().RetrieveNode("fc_fuse/" #id)); \
+ PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", "fc_fuse/" #id);
int found_fc_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
@@ -98,43 +50,33 @@ std::unique_ptr FCFusePass::ApplyImpl(
// scenerio.
// FC's fusion is simple, just op fuse, no need to process the
// parameters.
- GET_NODE(mul_tmp_var); // x
- GET_NODE(mul_weight); // Y
- GET_NODE(elementwise_add_tmpvar); // bias
- GET_NODE(elementwise_add_out); // Out
- GET_NODE(mul); // MUL op
- GET_NODE(elementwise_add); // ELEMENT_ADD op
- GET_NODE(mul_out); // tmp
+ GET_NODE(x); // x
+ GET_NODE(w); // Y
+ GET_NODE(fc_bias); // bias
+ GET_NODE(fc_out); // Out
+ GET_NODE(mul); // MUL op
+ GET_NODE(elementwise_add); // ELEMENT_ADD op
+ GET_NODE(mul_out); // tmp
#undef GET_NODE
// Create an FC Node.
OpDesc desc;
- std::string fc_x_in = mul_tmp_var->Name();
- std::string fc_Y_in = mul_weight->Name();
- std::string fc_bias_in = elementwise_add_tmpvar->Name();
- std::string fc_out = elementwise_add_out->Name();
+ std::string fc_x_in = x->Name();
+ std::string fc_Y_in = w->Name();
+ std::string fc_bias_in = fc_bias->Name();
+ std::string fc_out_out = fc_out->Name();
desc.SetInput("Input", std::vector({fc_x_in}));
desc.SetInput("W", std::vector({fc_Y_in}));
desc.SetInput("Bias", std::vector({fc_bias_in}));
- desc.SetOutput("Out", std::vector({fc_out}));
+ desc.SetOutput("Out", std::vector({fc_out_out}));
desc.SetType("fc");
auto fc_node = g->CreateOpNode(&desc); // OpDesc will be copied.
- fc_node->inputs =
- std::vector({mul_tmp_var, mul_weight, elementwise_add_tmpvar});
- fc_node->outputs.push_back(elementwise_add_out);
-
- // Update link relatons
- PADDLE_ENFORCE(LinksReplace(&mul_tmp_var->outputs, mul, fc_node));
- PADDLE_ENFORCE(LinksReplace(&mul_weight->outputs, mul, fc_node));
- PADDLE_ENFORCE(LinksReplace(&elementwise_add_tmpvar->outputs,
- elementwise_add, fc_node));
- PADDLE_ENFORCE(
- LinksReplace(&elementwise_add_out->inputs, elementwise_add, fc_node));
+ GraphSafeRemoveNodes(graph.get(), {mul, elementwise_add, mul_out});
- // Drop old nodes
- graph->RemoveNode(mul);
- graph->RemoveNode(elementwise_add);
- graph->RemoveNode(mul_out); // tmp variable
+ IR_NODE_LINK_TO(x, fc_node);
+ IR_NODE_LINK_TO(w, fc_node);
+ IR_NODE_LINK_TO(fc_bias, fc_node);
+ IR_NODE_LINK_TO(fc_node, fc_out);
found_fc_count++;
};
diff --git a/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc b/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
index c404a6c44ccea8287ddfad976889a9f80cf6bad9..0d69dfa79aa26940f8f56f84b35ffed34f29f703 100644
--- a/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
+++ b/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
@@ -11,7 +11,6 @@
// 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/fluid/framework/ir/fc_lstm_fuse_pass.h"
#include
#include "paddle/fluid/framework/lod_tensor.h"
@@ -87,15 +86,24 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
}
op_desc.SetInput("Bias", {new_bias_var});
}
-
#undef GET_NODE
+ // Create temp variables.
+ scope->Var(name_scope + "/BatchedInput.new")
+ ->GetMutable();
+ scope->Var(name_scope + "/BatchCellPreAct.new")
+ ->GetMutable();
+ scope->Var(name_scope + "/BatchedGate.new")
+ ->GetMutable();
+
op_desc.SetInput("H0", {});
op_desc.SetInput("C0", {});
op_desc.SetOutput("Hidden", {hidden_n->Name()});
op_desc.SetOutput("Cell", {cell_n->Name()});
op_desc.SetOutput("XX", {xx_n->Name()});
- op_desc.SetOutput("BatchedInput", {"blstm_0.tmp_2"});
+ op_desc.SetOutput("BatchedGate", {name_scope + "/BatchedGate.new"});
+ op_desc.SetOutput("BatchCellPreAct", {name_scope + "/BatchCellPreAct.new"});
+ op_desc.SetOutput("BatchedInput", {name_scope + "/BatchedInput.new"});
op_desc.SetAttr("is_reverse", lstm_n->Op()->GetAttr("is_reverse"));
op_desc.SetAttr("use_peepholes", lstm_n->Op()->GetAttr("use_peepholes"));
// TODO(TJ): get from attr
@@ -121,22 +129,18 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
#undef TMP_NEW
#undef TMP_NAME
-#define LINK_TO(a, b) \
- a->outputs.push_back(b); \
- b->inputs.push_back(a);
- LINK_TO(input_n, op);
- LINK_TO(weight_x_n, op);
- LINK_TO(weight_h_n, op);
- LINK_TO(bias_n, op);
- LINK_TO(op, hidden_n);
-#undef LINK_TO
+ IR_NODE_LINK_TO(input_n, op);
+ IR_NODE_LINK_TO(weight_x_n, op);
+ IR_NODE_LINK_TO(weight_h_n, op);
+ IR_NODE_LINK_TO(bias_n, op);
+ IR_NODE_LINK_TO(op, hidden_n);
return op;
};
int fusion_count{0};
- auto fc_no_bias_handler = [&](
- const GraphPatternDetector::subgraph_t& subgraph, Graph* g) {
+ auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+ Graph* g) {
#define GET_NODE(name__) \
std::string name__##key = name_scope + "/" + #name__; \
auto* name__##n = pattern->RetrieveNode(name__##key); \
@@ -157,21 +161,24 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
if (with_fc_bias) {
GET_NODE(fc_bias);
+ GET_NODE(elementwise_add);
lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, fc_bias);
+ // Remove unneeded nodes.
+ std::unordered_set marked_nodes(
+ {mul_n, lstm_n, elementwise_add_n});
+ GraphSafeRemoveNodes(graph, marked_nodes);
} else {
lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, -1);
+ // Remove unneeded nodes.
+ std::unordered_set marked_nodes({mul_n, lstm_n});
+ GraphSafeRemoveNodes(graph, marked_nodes);
}
#undef GET_NODE
- // Remove unneeded nodes.
- std::unordered_set marked_nodes({mul_n, lstm_n});
-
- GraphSafeRemoveNodes(graph, marked_nodes);
-
++fusion_count;
};
- gpd(graph, fc_no_bias_handler);
+ gpd(graph, handler);
return fusion_count;
}
diff --git a/paddle/fluid/framework/ir/fc_lstm_fuse_pass.h b/paddle/fluid/framework/ir/fc_lstm_fuse_pass.h
index 5a6687872eb3ab4a032227fda9ff0e7f5254670b..3ee32c63a46fcc34bdccd1e14d4bbaf9668c49e9 100644
--- a/paddle/fluid/framework/ir/fc_lstm_fuse_pass.h
+++ b/paddle/fluid/framework/ir/fc_lstm_fuse_pass.h
@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
+#pragma once
+
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.cc b/paddle/fluid/framework/ir/graph_pattern_detector.cc
index f651ab635eadc9f248964e91dceebf3aa9c42926..434bee4ccee1c199088d09c934fe86435ec7d095 100644
--- a/paddle/fluid/framework/ir/graph_pattern_detector.cc
+++ b/paddle/fluid/framework/ir/graph_pattern_detector.cc
@@ -73,7 +73,6 @@ void PDPattern::AddEdge(PDNode* a, PDNode* b) {
void GraphPatternDetector::operator()(Graph* graph,
GraphPatternDetector::handle_t handler) {
if (!MarkPDNodesInGraph(*graph)) {
- LOG(INFO) << "Mark failed";
return;
}
@@ -111,6 +110,11 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph& graph) {
return false;
}
}
+ for (auto& item : pdnodes2nodes_) {
+ for (auto& n : item.second) {
+ GetMarkedNodes(const_cast(&graph)).insert(n);
+ }
+ }
VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
return !pdnodes2nodes_.empty();
@@ -278,7 +282,7 @@ void GraphPatternDetector::RemoveOverlappedMatch(
for (const auto& subgraph : *subgraphs) {
bool valid = true;
for (auto& item : subgraph) {
- if (node_set.count(item.second)) {
+ if (item.first->IsIntermediate() && node_set.count(item.second)) {
valid = false;
break;
}
@@ -334,22 +338,22 @@ PDNode& PDNode::LinksFrom(const std::vector& others) {
}
PDNode* PDNode::assert_is_op() {
- asserts_.emplace_back([this](Node* x) { return x && x->IsOp(); });
+ asserts_.emplace_back([](Node* x) { return x && x->IsOp(); });
return this;
}
PDNode* PDNode::assert_is_op(const std::string& op_type) {
- asserts_.emplace_back([this, op_type](Node* x) {
+ asserts_.emplace_back([op_type](Node* x) {
return x && x->IsOp() && x->Op()->Type() == op_type;
});
return this;
}
PDNode* PDNode::assert_is_var() {
- asserts_.emplace_back([this](Node* x) { return x && x->IsVar(); });
+ asserts_.emplace_back([](Node* x) { return x && x->IsVar(); });
return this;
}
PDNode* PDNode::assert_var_not_persistable() {
assert_is_var();
- asserts_.emplace_back([this](Node* x) { return !x->Var()->Persistable(); });
+ asserts_.emplace_back([](Node* x) { return !x->Var()->Persistable(); });
return this;
}
PDNode* PDNode::assert_is_persistable_var() {
@@ -491,14 +495,16 @@ void GraphSafeRemoveNodes(Graph* graph,
for (auto it = node->inputs.begin(); it != node->inputs.end();) {
if (nodes.count(*it)) {
it = const_cast(node)->inputs.erase(it);
- } else
+ } else {
it++;
+ }
}
for (auto it = node->outputs.begin(); it != node->outputs.end();) {
if (nodes.count(*it)) {
it = const_cast(node)->outputs.erase(it);
- } else
+ } else {
it++;
+ }
}
}
}
diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.h b/paddle/fluid/framework/ir/graph_pattern_detector.h
index 024ce8ce55616cc5e0eaced4a27a6e1fb004af2c..eacea1750f6f1e86a8fe79637c3bd757a7275398 100644
--- a/paddle/fluid/framework/ir/graph_pattern_detector.h
+++ b/paddle/fluid/framework/ir/graph_pattern_detector.h
@@ -19,6 +19,9 @@
#endif
#include
+#include
+#include
+#include
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/inference/analysis/dot.h"
@@ -245,6 +248,8 @@ class GraphPatternDetector {
void UniquePatterns(std::vector* subgraphs);
// Remove overlapped match subgraphs, when overlapped, keep the previous one.
+ // The intermediate PDNodes will be removed, so can't shared by multiple
+ // patterns.
void RemoveOverlappedMatch(std::vector* subgraphs);
// Validate whether the intermediate nodes are linked by external nodes.
@@ -295,6 +300,10 @@ PDNode* LSTM(PDPattern* pattern, const std::string& name_scope, PDNode* x);
} // namespace patterns
+#define IR_NODE_LINK_TO(a, b) \
+ a->outputs.push_back(b); \
+ b->inputs.push_back(a);
+
} // namespace ir
} // namespace framework
} // namespace paddle
diff --git a/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc b/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc
index 7e5c86b033a7c69a306491cf4bf8d099018c5f19..6c466fb21fb46e09961dc874e9e39655f83d17c6 100644
--- a/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc
+++ b/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc
@@ -140,8 +140,9 @@ TEST(GraphPatternDetecter, MultiSubgraph) {
return node->IsOp() && (node->Name() == "op2" || node->Name() == "op3");
},
"OP0");
- auto* any_var = x.mutable_pattern()->NewNode(
- [](Node* node) { return node->IsVar(); }, "VAR");
+ auto* any_var = x.mutable_pattern()
+ ->NewNode([](Node* node) { return node->IsVar(); }, "VAR")
+ ->AsIntermediate();
auto* any_op1 = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsOp(); }, "OP1");
diff --git a/paddle/fluid/framework/ir/infer_clean_graph_pass.cc b/paddle/fluid/framework/ir/infer_clean_graph_pass.cc
index f885567da1965b997b2063e06c839af95b43e1e1..7713ed1eab88ee4fa16d52e7425075ae66f721a3 100644
--- a/paddle/fluid/framework/ir/infer_clean_graph_pass.cc
+++ b/paddle/fluid/framework/ir/infer_clean_graph_pass.cc
@@ -13,42 +13,41 @@
// limitations under the License.
#include
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
-class InferCleanGraphPass : public Pass {
+class InferCleanGraphPass : public FusePassBase {
public:
virtual ~InferCleanGraphPass() {}
protected:
std::unique_ptr ApplyImpl(std::unique_ptr graph) const {
+ FusePassBase::Init("original_graph", graph.get());
PADDLE_ENFORCE(graph.get());
auto is_valid_node = [](Node* x) {
return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
};
- std::unordered_set invalid_nodes;
+ std::unordered_set invalid_nodes;
+ int valid_op = 0;
for (auto* node : graph->Nodes()) {
if (is_valid_node(node)) {
invalid_nodes.insert(node);
+ } else if (node->IsOp()) {
+ // Collect all the operators to help tracking number of operators.
+ ++valid_op;
}
}
- // remove nodes from the graph.
- for (auto* node : invalid_nodes) {
- graph->RemoveNode(node);
- }
+ GraphSafeRemoveNodes(graph.get(), invalid_nodes);
- // clean edges.
- for (auto* node : graph->Nodes()) {
- CleanEdges(&node->inputs, invalid_nodes);
- CleanEdges(&node->outputs, invalid_nodes);
- }
+ AddStatis(valid_op);
return graph;
}
diff --git a/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc b/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc
index a776a898a5ee13b4dde12460dce71433268fb9d4..e1a441d09aaa3647c4b2a582210a2c7e2b64e0da 100644
--- a/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc
+++ b/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc
@@ -219,16 +219,13 @@ std::unique_ptr SeqConcatFcFusePass::ApplyImpl(
op_desc.SetAttr("fc_activation", act->Op()->Type());
auto* op_node = graph->CreateOpNode(&op_desc);
-// Add links
-#define NODE_LINKS(a, b) \
- a->outputs.push_back(b); \
- b->inputs.push_back(a);
- NODE_LINKS(fc_w, op_node);
- NODE_LINKS(fc_bias, op_node);
- NODE_LINKS(concat_in0, op_node);
- NODE_LINKS(sequence_expand0_in, op_node);
- NODE_LINKS(sequence_expand1_in, op_node);
- NODE_LINKS(op_node, fc_out);
+ // Add links
+ IR_NODE_LINK_TO(fc_w, op_node);
+ IR_NODE_LINK_TO(fc_bias, op_node);
+ IR_NODE_LINK_TO(concat_in0, op_node);
+ IR_NODE_LINK_TO(sequence_expand0_in, op_node);
+ IR_NODE_LINK_TO(sequence_expand1_in, op_node);
+ IR_NODE_LINK_TO(op_node, fc_out);
// Clean nodes.
std::unordered_set marked_nodes;
@@ -241,7 +238,6 @@ std::unique_ptr SeqConcatFcFusePass::ApplyImpl(
marked_nodes.erase(sequence_expand0_in);
marked_nodes.erase(sequence_expand1_in);
marked_nodes.erase(fc_out);
-
GraphSafeRemoveNodes(graph, marked_nodes);
});
diff --git a/paddle/fluid/inference/CMakeLists.txt b/paddle/fluid/inference/CMakeLists.txt
index 86392078b356df774fbc47aed9214e9f10fe33be..2006e3b24f71d0ae32b4e2ae34f1a1e4d3a82f91 100644
--- a/paddle/fluid/inference/CMakeLists.txt
+++ b/paddle/fluid/inference/CMakeLists.txt
@@ -10,7 +10,7 @@ set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor)
# TODO(panyx0718): Should this be called paddle_fluid_inference_api_internal?
cc_library(paddle_fluid_api
SRCS io.cc
- DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} graph_to_program_pass)
+ DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
@@ -22,7 +22,7 @@ cc_library(paddle_fluid_origin DEPS ${fluid_modules} paddle_fluid_api)
#endif()
# Create static library
-cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api)
+cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api analysis_predictor)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
@@ -32,6 +32,7 @@ endif()
# Create shared library
cc_library(paddle_fluid_shared SHARED
SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
+ ${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
DEPS ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
diff --git a/paddle/fluid/inference/analysis/CMakeLists.txt b/paddle/fluid/inference/analysis/CMakeLists.txt
index eb4908da24c98e4d9bf68559c247b905b7770973..43201fb0bbb88a16c8dc3b92cf52d712ea2d41f7 100644
--- a/paddle/fluid/inference/analysis/CMakeLists.txt
+++ b/paddle/fluid/inference/analysis/CMakeLists.txt
@@ -33,7 +33,7 @@ function (inference_analysis_test TARGET)
endif()
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
- DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detector pass ${analysis_test_EXTRA_DEPS}
+ DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt} ${analysis_test_ARGS})
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endif(WITH_TESTING)
@@ -56,25 +56,13 @@ if (NOT EXISTS ${DITU_INSTALL_DIR} AND WITH_TESTING)
endif()
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
- EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
- analysis_predictor
- # ir
- fc_fuse_pass
- fc_lstm_fuse_pass
- seq_concat_fc_fuse_pass
- graph_viz_pass
- infer_clean_graph_pass
- graph_pattern_detector
- infer_clean_graph_pass
- attention_lstm_fuse_pass
- paddle_inference_api
- pass
+ EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
ARGS --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
- --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
+ --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
-inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc EXTRA_DEPS paddle_inference_api)
-inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc EXTRA_DEPS paddle_fluid)
+inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
+inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc)
inference_analysis_test(test_fluid_to_data_flow_graph_pass SRCS fluid_to_data_flow_graph_pass_tester.cc)
inference_analysis_test(test_subgraph_splitter SRCS subgraph_splitter_tester.cc)
inference_analysis_test(test_dfg_graphviz_draw_pass SRCS dfg_graphviz_draw_pass_tester.cc)
@@ -86,7 +74,7 @@ inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
set(CHINESE_NER_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner_model.tar.gz")
set(CHINESE_NER_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner-data.txt.tar.gz")
set(CHINESE_NER_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/chinese_ner" CACHE PATH "Chinese ner model and data root." FORCE)
-if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING)
+if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_MODEL_URL} "chinese_ner_model.tar.gz")
inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_DATA_URL} "chinese_ner-data.txt.tar.gz")
endif()
@@ -99,7 +87,7 @@ inference_analysis_test(test_analyzer_ner SRCS analyzer_ner_tester.cc
set(LAC_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/lac_model.tar.gz")
set(LAC_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/lac_data.txt.tar.gz")
set(LAC_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/lac" CACHE PATH "LAC model and data root." FORCE)
-if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING)
+if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_MODEL_URL} "lac_model.tar.gz")
inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_DATA_URL} "lac_data.txt.tar.gz")
endif()
@@ -120,3 +108,15 @@ inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
pass
ARGS --infer_model=${LAC_INSTALL_DIR}/model
--infer_data=${LAC_INSTALL_DIR}/data.txt)
+
+
+set(TEXT_CLASSIFICATION_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/text-classification-Senta.tar.gz")
+set(TEXT_CLASSIFICATION_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/text_classification" CACHE PATH "Text Classification model and data root." FORCE)
+
+if (NOT EXISTS ${TEXT_CLASSIFICATION_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
+ inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
+endif()
+
+inference_analysis_test(test_text_classification SRCS test_text_classification.cc
+ EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
+ ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)
diff --git a/paddle/fluid/inference/analysis/analyzer.cc b/paddle/fluid/inference/analysis/analyzer.cc
index 192ac2daa6a78efec6db19870f71e80593c62da9..ca834406451d53fd44887300561a6327d97cafcd 100644
--- a/paddle/fluid/inference/analysis/analyzer.cc
+++ b/paddle/fluid/inference/analysis/analyzer.cc
@@ -14,6 +14,7 @@
#include "paddle/fluid/inference/analysis/analyzer.h"
#include
+#include
#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
@@ -41,20 +42,16 @@ class DfgPassManagerImpl final : public DfgPassManager {
public:
DfgPassManagerImpl() {
// TODO(Superjomn) set the key with pass reprs.
- LOG(INFO)
- << "-----------------------------------------------------------------";
- if (FLAGS_IA_enable_ir) {
- AddPass("fluid-to-ir-pass", new FluidToIrPass);
- } else {
+ if (!FLAGS_IA_enable_ir) {
AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
+ } else {
+ AddPass("fluid-to-ir-pass", new FluidToIrPass);
}
TryAddTensorRtPass();
AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
if (!FLAGS_IA_output_storage_path.empty()) {
AddPass("model-store-pass", new ModelStorePass);
}
- LOG(INFO)
- << "-----------------------------------------------------------------";
}
std::string repr() const override { return "dfg-pass-manager"; }
@@ -101,19 +98,16 @@ class DfgPassManagerImpl final : public DfgPassManager {
Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
void Analyzer::Run(Argument* argument) {
+ std::vector passes;
+ for (auto& pass : all_ir_passes_) {
+ if (!disabled_ir_passes_.count(pass)) {
+ passes.push_back(pass);
+ passes.push_back("graph_viz_pass"); // add graphviz for debug.
+ }
+ }
+ passes.push_back("graph_viz_pass");
// Ugly support fluid-to-ir-pass
- argument->Set(kFluidToIrPassesAttr,
- new std::vector({
- // Manual update the passes here.
- "graph_viz_pass", //
- "infer_clean_graph_pass", "graph_viz_pass", //
- "attention_lstm_fuse_pass", "graph_viz_pass", //
- "fc_lstm_fuse_pass", "graph_viz_pass", //
- "mul_lstm_fuse_pass", "graph_viz_pass", //
- "seq_concat_fc_fuse_pass", "graph_viz_pass", //
- "fc_fuse_pass", "graph_viz_pass" //
-
- }));
+ argument->Set(kFluidToIrPassesAttr, new std::vector(passes));
for (auto& x : data_) {
PADDLE_ENFORCE(x->Initialize(argument));
@@ -122,6 +116,11 @@ void Analyzer::Run(Argument* argument) {
}
}
+Analyzer& Analyzer::DisableIrPasses(const std::vector& passes) {
+ disabled_ir_passes_.insert(passes.begin(), passes.end());
+ return *this;
+}
+
} // namespace analysis
} // namespace inference
} // namespace paddle
diff --git a/paddle/fluid/inference/analysis/analyzer.h b/paddle/fluid/inference/analysis/analyzer.h
index 2e107c82dd50d5cf22797f4c82e69d302514f955..3fdd2b9ec7537c891a04efb3ca9a1d45075ffa5e 100644
--- a/paddle/fluid/inference/analysis/analyzer.h
+++ b/paddle/fluid/inference/analysis/analyzer.h
@@ -36,16 +36,10 @@ limitations under the License. */
*/
#include
+#include "paddle/fluid/inference/analysis/flags.h"
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
-// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
-// flag if not available.
-DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
-DECLARE_string(IA_graphviz_log_root);
-DECLARE_string(IA_output_storage_path);
-DECLARE_bool(IA_enable_ir);
-
namespace paddle {
namespace inference {
namespace analysis {
@@ -57,7 +51,26 @@ class Analyzer : public OrderedRegistry {
void Run(Argument* argument);
+ Analyzer& DisableIrPasses(const std::vector& passes);
+
DISABLE_COPY_AND_ASSIGN(Analyzer);
+
+ private:
+ // All avaiable IR passes.
+ // The bigger fuse comes first, so that the small operators prefer to be
+ // merged in a larger fuse op. The small fusion will not break the pattern of
+ // larger fusion.
+ const std::vector all_ir_passes_{{
+ // Manual update the passes here.
+ "infer_clean_graph_pass", //
+ "attention_lstm_fuse_pass", //
+ "fc_lstm_fuse_pass", //
+ "mul_lstm_fuse_pass", //
+ "seq_concat_fc_fuse_pass", //
+ "fc_fuse_pass", //
+ }};
+
+ std::unordered_set disabled_ir_passes_;
};
} // namespace analysis
diff --git a/paddle/fluid/inference/analysis/analyzer_tester.cc b/paddle/fluid/inference/analysis/analyzer_tester.cc
index ec1f3979a74bd86ee7402bca441e95d3d177d113..94be6733f63488634ee1302e015a57c9a8892d84 100644
--- a/paddle/fluid/inference/analysis/analyzer_tester.cc
+++ b/paddle/fluid/inference/analysis/analyzer_tester.cc
@@ -22,6 +22,7 @@
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.h"
@@ -270,17 +271,22 @@ void TestDituRNNPrediction(const std::string &model_path,
const std::string &data_path, int batch_size,
bool use_analysis, bool activate_ir,
int num_times = 1) {
- NativeConfig config;
+ AnalysisConfig config;
config.prog_file = FLAGS_infer_ditu_rnn_model + "/__model__";
config.param_file = FLAGS_infer_ditu_rnn_model + "/param";
config.use_gpu = false;
config.device = 0;
config.specify_input_name = true;
+ config.enable_ir_optim = activate_ir;
+ PADDLE_ENFORCE(config.ir_mode ==
+ AnalysisConfig::IrPassMode::kExclude); // default
+ config.ir_passes.clear(); // Do not exclude any pass.
auto base_predictor =
CreatePaddlePredictor(config);
auto predictor =
- CreatePaddlePredictor(config);
+ CreatePaddlePredictor(
+ config);
std::vector input_slots;
DataRecord data(data_path, batch_size);
// Prepare inputs.
@@ -327,9 +333,20 @@ void TestDituRNNPrediction(const std::string &model_path,
LOG(INFO) << "fused " << item.first << " " << item.second;
}
- ASSERT_TRUE(fuse_statis.count("fc"));
- EXPECT_EQ(fuse_statis.at("fc"), 1);
- EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 1);
+ int num_ops = 0;
+ for (auto &node :
+ analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
+ if (node->IsFunction()) {
+ ++num_ops;
+ }
+ }
+ LOG(INFO) << "has num ops: " << num_ops;
+
+ ASSERT_TRUE(fuse_statis.count("fc_fuse"));
+ EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
+ EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2); // bi-directional LSTM
+ EXPECT_EQ(num_ops,
+ 13); // After graph optimization, only 13 operators exists.
}
}
@@ -357,10 +374,3 @@ TEST(Analyzer, DituRNN_with_analysis_with_IR) {
} // namespace analysis
} // namespace inference
} // namespace paddle
-
-USE_PASS(fc_fuse_pass);
-USE_PASS(seq_concat_fc_fuse_pass);
-USE_PASS(fc_lstm_fuse_pass);
-USE_PASS(graph_viz_pass);
-USE_PASS(infer_clean_graph_pass);
-USE_PASS(attention_lstm_fuse_pass);
diff --git a/paddle/fluid/inference/analysis/flags.h b/paddle/fluid/inference/analysis/flags.h
new file mode 100644
index 0000000000000000000000000000000000000000..717e543f01dfa071865a5c14c0b7679e65239daf
--- /dev/null
+++ b/paddle/fluid/inference/analysis/flags.h
@@ -0,0 +1,22 @@
+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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
+
+// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
+// flag if not available.
+DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
+DECLARE_string(IA_graphviz_log_root);
+DECLARE_string(IA_output_storage_path);
+DECLARE_bool(IA_enable_ir);
diff --git a/paddle/fluid/inference/analysis/fluid_to_ir_pass.h b/paddle/fluid/inference/analysis/fluid_to_ir_pass.h
index 6731b1f759363eec5dd8645783212a72ace67b2f..3086085710d6e850ed27e82d2323690dfdd3ef19 100644
--- a/paddle/fluid/inference/analysis/fluid_to_ir_pass.h
+++ b/paddle/fluid/inference/analysis/fluid_to_ir_pass.h
@@ -15,6 +15,7 @@
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/inference/analysis/flags.h"
#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
#include "paddle/fluid/inference/analysis/pass.h"
@@ -85,9 +86,11 @@ class FluidToIrPass final : public DataFlowGraphPass {
new Scope *(&argument_->Get(ir::kParamScopeAttr)));
}
- const auto &ir_passes_to_apply =
- argument_->Get>(kFluidToIrPassesAttr);
- ir_passes.Apply(ir_passes_to_apply);
+ if (FLAGS_IA_enable_ir) {
+ const auto &ir_passes_to_apply =
+ argument_->Get>(kFluidToIrPassesAttr);
+ ir_passes.Apply(ir_passes_to_apply);
+ }
PADDLE_ENFORCE(argument_->main_dfg.get());
argument_->main_dfg->Build(ir_passes.graph());
diff --git a/paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc b/paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc
index 6a13c60e7b2ebf645b12d5ddf83ef6ab3a2e83bd..367c25805d05f8d10fb8341158760ac6356a5c48 100644
--- a/paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc
+++ b/paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc
@@ -16,6 +16,7 @@
#include
#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
namespace paddle {
namespace inference {
@@ -33,10 +34,3 @@ TEST(FluidToIrPass, Test) {
} // namespace analysis
} // namespace inference
} // namespace paddle
-
-USE_PASS(graph_viz_pass);
-USE_PASS(infer_clean_graph_pass);
-USE_PASS(attention_lstm_fuse_pass);
-USE_PASS(fc_lstm_fuse_pass);
-USE_PASS(seq_concat_fc_fuse_pass);
-USE_PASS(fc_fuse_pass);
diff --git a/paddle/fluid/inference/analysis/test_text_classification.cc b/paddle/fluid/inference/analysis/test_text_classification.cc
new file mode 100644
index 0000000000000000000000000000000000000000..2913824f62301795aea967c22021b2af11f343c1
--- /dev/null
+++ b/paddle/fluid/inference/analysis/test_text_classification.cc
@@ -0,0 +1,109 @@
+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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
+#include // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
+#include
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/inference/analysis/analyzer.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/timer.h"
+
+DEFINE_string(infer_model, "", "Directory of the inference model.");
+DEFINE_string(infer_data, "", "Path of the dataset.");
+DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(repeat, 1, "How many times to repeat run.");
+
+namespace paddle {
+
+template
+std::string to_string(const std::vector &vec) {
+ std::stringstream ss;
+ for (const auto &c : vec) {
+ ss << c << " ";
+ }
+ return ss.str();
+}
+
+void PrintTime(const double latency, const int bs, const int repeat) {
+ LOG(INFO) << "===========profile result===========";
+ LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
+ << ", avg latency: " << latency / repeat << "ms";
+ LOG(INFO) << "=====================================";
+}
+
+void Main(int batch_size) {
+ // Three sequence inputs.
+ std::vector input_slots(1);
+ // one batch starts
+ // data --
+ int64_t data0[] = {0, 1, 2};
+ for (auto &input : input_slots) {
+ input.data.Reset(data0, sizeof(data0));
+ input.shape = std::vector({3, 1});
+ // dtype --
+ input.dtype = PaddleDType::INT64;
+ // LoD --
+ input.lod = std::vector>({{0, 3}});
+ }
+
+ // shape --
+ // Create Predictor --
+ AnalysisConfig config;
+ config.model_dir = FLAGS_infer_model;
+ config.use_gpu = false;
+ config.enable_ir_optim = true;
+ config.ir_passes.push_back("fc_lstm_fuse_pass");
+ auto predictor =
+ CreatePaddlePredictor(
+ config);
+
+ inference::Timer timer;
+ double sum = 0;
+ std::vector output_slots;
+ for (int i = 0; i < FLAGS_repeat; i++) {
+ timer.tic();
+ CHECK(predictor->Run(input_slots, &output_slots));
+ sum += timer.toc();
+ }
+ PrintTime(sum, batch_size, FLAGS_repeat);
+
+ // Get output
+ LOG(INFO) << "get outputs " << output_slots.size();
+
+ for (auto &output : output_slots) {
+ LOG(INFO) << "output.shape: " << to_string(output.shape);
+ // no lod ?
+ CHECK_EQ(output.lod.size(), 0UL);
+ LOG(INFO) << "output.dtype: " << output.dtype;
+ std::stringstream ss;
+ for (int i = 0; i < 5; i++) {
+ ss << static_cast(output.data.data())[i] << " ";
+ }
+ LOG(INFO) << "output.data summary: " << ss.str();
+ // one batch ends
+ }
+}
+
+TEST(text_classification, basic) { Main(FLAGS_batch_size); }
+
+} // namespace paddle
+
+USE_PASS(fc_fuse_pass);
+USE_PASS(seq_concat_fc_fuse_pass);
+USE_PASS(fc_lstm_fuse_pass);
+USE_PASS(graph_viz_pass);
+USE_PASS(infer_clean_graph_pass);
+USE_PASS(attention_lstm_fuse_pass);
diff --git a/paddle/fluid/inference/api/CMakeLists.txt b/paddle/fluid/inference/api/CMakeLists.txt
index a94c79a698d32279f8b9fe140357adab4b6dff39..e976b9397d60e02f6a672a358e243aa06725bf8f 100644
--- a/paddle/fluid/inference/api/CMakeLists.txt
+++ b/paddle/fluid/inference/api/CMakeLists.txt
@@ -18,10 +18,7 @@ if(APPLE)
endif(APPLE)
-set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager
- graph_viz_pass fc_fuse_pass
- infer_clean_graph_pass
- )
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager ${GLOB_PASS_LIB})
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
@@ -47,7 +44,19 @@ function(inference_api_test TARGET_NAME)
endfunction(inference_api_test)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
-cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
+cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api
+ analysis
+ ir_pass_manager
+ pass
+ fc_fuse_pass
+ fc_lstm_fuse_pass
+ seq_concat_fc_fuse_pass
+ graph_viz_pass
+ infer_clean_graph_pass
+ graph_pattern_detector
+ infer_clean_graph_pass
+ attention_lstm_fuse_pass
+)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc
index 33862232bdaae817b9ca72879605386c32ed3e8b..a8fa677202d8429c274a6e3fdfd18ef5d48620c2 100644
--- a/paddle/fluid/inference/api/analysis_predictor.cc
+++ b/paddle/fluid/inference/api/analysis_predictor.cc
@@ -14,10 +14,13 @@
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include
+#include
+#include
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
namespace paddle {
@@ -27,6 +30,8 @@ bool AnalysisPredictor::Init(
VLOG(3) << "Predictor::init()";
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
+ LOG(WARNING) << "ir optimize only supports CPU currently";
+ config_.enable_ir_optim = false;
} else {
place_ = paddle::platform::CPUPlace();
}
@@ -72,7 +77,7 @@ bool AnalysisPredictor::Init(
void AnalysisPredictor::OptimizeInferenceProgram() {
LOG(INFO) << "optimize begin";
- FLAGS_IA_enable_ir = true;
+ FLAGS_IA_enable_ir = config_.enable_ir_optim;
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
FLAGS_IA_output_storage_path = ""; // Don't output the model.
// Analyze inference_program
@@ -89,24 +94,26 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
}
argument_.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
- Analyzer().Run(&argument_);
+ PADDLE_ENFORCE(config_.ir_mode == AnalysisConfig::IrPassMode::kExclude,
+ "Only kExclude is supported yet.");
+ Analyzer().DisableIrPasses(config_.ir_passes).Run(&argument_);
+
CHECK(argument_.transformed_program_desc);
VLOG(5) << "to prepare executor";
- // LOG(INFO) << "transformed_parogram_desc " <<
- // argument.transformed_program_desc->DebugString();
inference_program_.reset(
new framework::ProgramDesc(*argument_.transformed_program_desc));
- PADDLE_ENFORCE(argument_.Has(framework::ir::kParamScopeAttr));
- // Update scope.
- scope_.reset(
- argument_.Release(framework::ir::kParamScopeAttr));
- LOG(INFO) << "optimize end ==";
+ if (argument_.Has(framework::ir::kParamScopeAttr)) {
+ // Update scope.
+ scope_.reset(
+ argument_.Release(framework::ir::kParamScopeAttr));
+ }
+ LOG(INFO) << "== optimize end ==";
}
template <>
std::unique_ptr CreatePaddlePredictor<
- NativeConfig, PaddleEngineKind::kAnalysis>(const NativeConfig& config) {
- VLOG(3) << "create NativePredictor";
+ AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig& config) {
+ VLOG(3) << "create AnalysisConfig";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
@@ -133,7 +140,3 @@ std::unique_ptr CreatePaddlePredictor<
}
} // namespace paddle
-
-USE_PASS(fc_fuse_pass);
-USE_PASS(graph_viz_pass);
-USE_PASS(infer_clean_graph_pass);
diff --git a/paddle/fluid/inference/api/analysis_predictor.h b/paddle/fluid/inference/api/analysis_predictor.h
index e32b6185f6044ab3577bde0a8f8dcf2391688aa8..e53925366e9214cd60422efe56884751297c15e5 100644
--- a/paddle/fluid/inference/api/analysis_predictor.h
+++ b/paddle/fluid/inference/api/analysis_predictor.h
@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
+#include
+#include
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
@@ -28,7 +30,7 @@ using framework::proto::ProgramDesc;
*/
class AnalysisPredictor : public NativePaddlePredictor {
public:
- explicit AnalysisPredictor(const NativeConfig& config)
+ explicit AnalysisPredictor(const AnalysisConfig& config)
: NativePaddlePredictor(config), config_(config) {}
bool Init(const std::shared_ptr& parent_scope);
@@ -44,7 +46,7 @@ class AnalysisPredictor : public NativePaddlePredictor {
Argument& analysis_argument() { return argument_; }
private:
- NativeConfig config_;
+ AnalysisConfig config_;
Argument argument_;
};
diff --git a/paddle/fluid/inference/api/api_impl.cc b/paddle/fluid/inference/api/api_impl.cc
index 38b11d9113e4b03f8365b969009f7a385a683a70..bd9b4b1a814f995e3979105f5b9830b95fd8ea7d 100644
--- a/paddle/fluid/inference/api/api_impl.cc
+++ b/paddle/fluid/inference/api/api_impl.cc
@@ -176,7 +176,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector &inputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::set_feed";
if (inputs.size() != feeds_.size()) {
- LOG(ERROR) << "wrong feed input size.";
+ LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
+ << inputs.size();
return false;
}
for (size_t i = 0; i < inputs.size(); ++i) {
diff --git a/paddle/fluid/inference/api/demo_ci/run.sh b/paddle/fluid/inference/api/demo_ci/run.sh
index 7824ef2649af81a2390ff3bc537eb7c93c70e402..0f7d541c5edfc62e80cf50f83b491f06dcb42644 100755
--- a/paddle/fluid/inference/api/demo_ci/run.sh
+++ b/paddle/fluid/inference/api/demo_ci/run.sh
@@ -14,7 +14,7 @@ else
fi
PREFIX=inference-vis-demos%2F
-URL_ROOT=http://paddlemodels.bj.bcebos.com/${PREFIX}
+URL_ROOT=http://paddlemodels.cdn.bcebos.com/${PREFIX}
# download vis_demo data
function download() {
diff --git a/paddle/fluid/inference/api/helper.h b/paddle/fluid/inference/api/helper.h
index bdc9a15d543818da94ac2acf34ecabbbbae3291e..ce4728ab8046f886fc40af3644d855a4f971fb71 100644
--- a/paddle/fluid/inference/api/helper.h
+++ b/paddle/fluid/inference/api/helper.h
@@ -16,6 +16,7 @@
#include
#include
+#include
#include
#include
#include
diff --git a/paddle/fluid/inference/api/paddle_inference_api.h b/paddle/fluid/inference/api/paddle_inference_api.h
index 1baa64c249f291ec1bc874be5031abe6d4368274..995da11e4a30eca72a91a53d3293aa8b033b012b 100644
--- a/paddle/fluid/inference/api/paddle_inference_api.h
+++ b/paddle/fluid/inference/api/paddle_inference_api.h
@@ -150,6 +150,21 @@ struct TensorRTConfig : public NativeConfig {
int workspace_size{1 << 30};
};
+// NOTE WIP, not stable yet.
+struct AnalysisConfig : public NativeConfig {
+ //
+ enum class IrPassMode {
+ kSystem, // Use system default passes, not customize.
+ kInclude, // Specify the passes in `ir_passes`.
+ kExclude // Specify the disabled passes in `ir_passes`.
+ };
+
+ bool enable_ir_optim = true;
+ IrPassMode ir_mode{IrPassMode::kExclude};
+ // attention lstm fuse works only on some specific models, disable as default.
+ std::vector ir_passes{"attention_lstm_fuse_pass"};
+};
+
// A factory to help create different predictors.
//
// FOR EXTENSION DEVELOPER:
diff --git a/paddle/fluid/inference/paddle_fluid.map b/paddle/fluid/inference/paddle_fluid.map
index 5203784dc1fcb672eb6a26d9dfd3ffbe02e08038..7e5cae04b81e6ce759b92f6c4b921ecf974e8260 100644
--- a/paddle/fluid/inference/paddle_fluid.map
+++ b/paddle/fluid/inference/paddle_fluid.map
@@ -1,6 +1,7 @@
{
global:
*paddle*;
+ *Pass*;
local:
*;
};
diff --git a/paddle/fluid/operators/auc_op.cc b/paddle/fluid/operators/auc_op.cc
index 5edecd18e673da326ec119cf9a383f24f8045089..dfaa7456f917c1308984b361afed752f96ea6f59 100644
--- a/paddle/fluid/operators/auc_op.cc
+++ b/paddle/fluid/operators/auc_op.cc
@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/auc_op.h"
-#include
namespace paddle {
namespace operators {
@@ -36,15 +35,12 @@ class AucOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(predict_height, label_height,
"Out and Label should have same height.");
- int num_thres = ctx->Attrs().Get("num_thresholds");
+ int num_pred_buckets = ctx->Attrs().Get("num_thresholds") + 1;
ctx->SetOutputDim("AUC", {1});
- ctx->SetOutputDim("TPOut", {num_thres});
- ctx->SetOutputDim("TNOut", {num_thres});
- ctx->SetOutputDim("FPOut", {num_thres});
- ctx->SetOutputDim("FNOut", {num_thres});
-
- ctx->ShareLoD("Predict", /*->*/ "AUC");
+ ctx->SetOutputDim("BatchAUC", {1});
+ ctx->SetOutputDim("StatPosOut", {num_pred_buckets});
+ ctx->SetOutputDim("StatNegOut", {num_pred_buckets});
}
protected:
@@ -66,25 +62,24 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Label",
"A 2D int tensor indicating the label of the training data. "
"shape: [batch_size, 1]");
- AddInput("TP", "True-Positive value.");
- AddInput("FP", "False-Positive value.");
- AddInput("TN", "True-Negative value.");
- AddInput("FN", "False-Negative value.");
// TODO(typhoonzero): support weight input
+ AddInput("StatPos", "Statistic value when label = 1");
+ AddInput("StatNeg", "Statistic value when label = 0");
+
AddOutput("AUC",
"A scalar representing the "
"current area-under-the-curve.");
- AddOutput("TPOut", "True-Positive value.");
- AddOutput("FPOut", "False-Positive value.");
- AddOutput("TNOut", "True-Negative value.");
- AddOutput("FNOut", "False-Negative value.");
+ AddOutput("BatchAUC", "The AUC for current batch");
+ AddOutput("StatPosOut", "Statistic value when label = 1");
+ AddOutput("StatNegOut", "Statistic value when label = 0");
AddAttr("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
+
AddAttr("num_thresholds",
"The number of thresholds to use when discretizing the"
" roc curve.")
- .SetDefault(200);
+ .SetDefault((2 << 12) - 1);
AddComment(R"DOC(
Area Under The Curve (AUC) Operator.
diff --git a/paddle/fluid/operators/auc_op.h b/paddle/fluid/operators/auc_op.h
index 0a18585edb54a76aff5ae72ecc71e0eebb9f9361..fb0517d70635e090f8c5b59ff9d8420fc34c747b 100644
--- a/paddle/fluid/operators/auc_op.h
+++ b/paddle/fluid/operators/auc_op.h
@@ -13,9 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
+
#include
#include
-#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
@@ -23,106 +23,85 @@ namespace operators {
using Tensor = framework::Tensor;
-template
-using EigenVector = framework::EigenVector;
-
template
class AucKernel : public framework::OpKernel {
public:
- void Compute(const framework::ExecutionContext& ctx) const override {
- auto* predict = ctx.Input("Predict");
- auto* label = ctx.Input("Label");
- auto* auc = ctx.Output("AUC");
+ void Compute(const framework::ExecutionContext &ctx) const override {
+ auto *predict = ctx.Input("Predict");
+ auto *label = ctx.Input("Label");
+
+ std::string curve = ctx.Attr("curve");
+ int num_thresholds = ctx.Attr("num_thresholds");
+ int num_pred_buckets = num_thresholds + 1;
+
// Only use output var for now, make sure it's persistable and
// not cleaned up for each batch.
- auto* true_positive = ctx.Output("TPOut");
- auto* false_positive = ctx.Output("FPOut");
- auto* true_negative = ctx.Output("TNOut");
- auto* false_negative = ctx.Output("FNOut");
+ auto *auc = ctx.Output("AUC");
+ auto *stat_pos = ctx.Output("StatPosOut");
+ auto *stat_neg = ctx.Output("StatNegOut");
- auto* auc_data = auc->mutable_data(ctx.GetPlace());
+ auto *stat_pos_data = stat_pos->mutable_data(ctx.GetPlace());
+ auto *stat_neg_data = stat_neg->mutable_data(ctx.GetPlace());
+ calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
+ auc);
- std::string curve = ctx.Attr("curve");
- int num_thresholds = ctx.Attr("num_thresholds");
- std::vector thresholds_list;
- thresholds_list.reserve(num_thresholds);
- for (int i = 1; i < num_thresholds - 1; i++) {
- thresholds_list[i] = static_cast(i) / (num_thresholds - 1);
- }
- const double kEpsilon = 1e-7;
- thresholds_list[0] = 0.0f - kEpsilon;
- thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
+ auto *batch_auc = ctx.Output("BatchAUC");
+ std::vector stat_pos_batch(num_pred_buckets, 0);
+ std::vector stat_neg_batch(num_pred_buckets, 0);
+ calcAuc(ctx, label, predict, stat_pos_batch.data(), stat_neg_batch.data(),
+ num_thresholds, batch_auc);
+ }
+ private:
+ inline static double trapezoidArea(double X1, double X2, double Y1,
+ double Y2) {
+ return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
+ }
+
+ inline static void calcAuc(const framework::ExecutionContext &ctx,
+ const framework::Tensor *label,
+ const framework::Tensor *predict,
+ int64_t *stat_pos, int64_t *stat_neg,
+ int num_thresholds,
+ framework::Tensor *auc_tensor) {
size_t batch_size = predict->dims()[0];
size_t inference_width = predict->dims()[1];
+ const T *inference_data = predict->data();
+ const auto *label_data = label->data();
+
+ auto *auc = auc_tensor->mutable_data(ctx.GetPlace());
- const T* inference_data = predict->data();
- const auto* label_data = label->data();
-
- auto* tp_data = true_positive->mutable_data(ctx.GetPlace());
- auto* fn_data = false_negative->mutable_data(ctx.GetPlace());
- auto* tn_data = true_negative->mutable_data(ctx.GetPlace());
- auto* fp_data = false_positive->mutable_data(ctx.GetPlace());
-
- for (int idx_thresh = 0; idx_thresh < num_thresholds; idx_thresh++) {
- // calculate TP, FN, TN, FP for current thresh
- int64_t tp = 0, fn = 0, tn = 0, fp = 0;
- for (size_t i = 0; i < batch_size; i++) {
- // NOTE: label_data used as bool, labels > 0 will be treated as true.
- if (label_data[i]) {
- if (inference_data[i * inference_width + 1] >=
- (thresholds_list[idx_thresh])) {
- tp++;
- } else {
- fn++;
- }
- } else {
- if (inference_data[i * inference_width + 1] >=
- (thresholds_list[idx_thresh])) {
- fp++;
- } else {
- tn++;
- }
- }
+ for (size_t i = 0; i < batch_size; i++) {
+ uint32_t binIdx = static_cast(
+ inference_data[i * inference_width + 1] * num_thresholds);
+ if (label_data[i]) {
+ stat_pos[binIdx] += 1.0;
+ } else {
+ stat_neg[binIdx] += 1.0;
}
- // store rates
- tp_data[idx_thresh] += tp;
- fn_data[idx_thresh] += fn;
- tn_data[idx_thresh] += tn;
- fp_data[idx_thresh] += fp;
}
- // epsilon to avoid divide by zero.
- double epsilon = 1e-6;
- // Riemann sum to caculate auc.
- Tensor tp_rate, fp_rate, rec_rate;
- tp_rate.Resize({num_thresholds});
- fp_rate.Resize({num_thresholds});
- rec_rate.Resize({num_thresholds});
- auto* tp_rate_data = tp_rate.mutable_data(ctx.GetPlace());
- auto* fp_rate_data = fp_rate.mutable_data(ctx.GetPlace());
- auto* rec_rate_data = rec_rate.mutable_data(ctx.GetPlace());
- for (int i = 0; i < num_thresholds; i++) {
- tp_rate_data[i] = (static_cast(tp_data[i]) + epsilon) /
- (tp_data[i] + fn_data[i] + epsilon);
- fp_rate_data[i] =
- static_cast(fp_data[i]) / (fp_data[i] + tn_data[i] + epsilon);
- rec_rate_data[i] = (static_cast(tp_data[i]) + epsilon) /
- (tp_data[i] + fp_data[i] + epsilon);
+
+ *auc = 0.0f;
+
+ double totPos = 0.0;
+ double totNeg = 0.0;
+ double totPosPrev = 0.0;
+ double totNegPrev = 0.0;
+
+ int idx = num_thresholds;
+
+ while (idx >= 0) {
+ totPosPrev = totPos;
+ totNegPrev = totNeg;
+ totPos += stat_pos[idx];
+ totNeg += stat_neg[idx];
+ *auc += trapezoidArea(totNeg, totNegPrev, totPos, totPosPrev);
+
+ --idx;
}
- *auc_data = 0.0f;
- if (curve == "ROC") {
- for (int i = 0; i < num_thresholds - 1; i++) {
- auto dx = fp_rate_data[i] - fp_rate_data[i + 1];
- auto y = (tp_rate_data[i] + tp_rate_data[i + 1]) / 2.0f;
- *auc_data = *auc_data + dx * y;
- }
- } else if (curve == "PR") {
- for (int i = 1; i < num_thresholds; i++) {
- auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
- auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;
- *auc_data = *auc_data + dx * y;
- }
+
+ if (totPos > 0.0 && totNeg > 0.0) {
+ *auc = *auc / totPos / totNeg;
}
}
};
diff --git a/paddle/fluid/operators/detection/bbox_util.h b/paddle/fluid/operators/detection/bbox_util.h
new file mode 100644
index 0000000000000000000000000000000000000000..0dee1781623d5a62830545c0952e5aadbe37accb
--- /dev/null
+++ b/paddle/fluid/operators/detection/bbox_util.h
@@ -0,0 +1,66 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+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/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace operators {
+
+/*
+ * transform that computes target bounding-box regression deltas
+ * given proposal boxes and ground-truth boxes.
+ */
+template
+inline void BoxToDelta(const int box_num, const framework::Tensor& ex_boxes,
+ const framework::Tensor& gt_boxes, const T* weights,
+ const bool normalized, framework::Tensor* box_delta) {
+ auto ex_boxes_et = framework::EigenTensor::From(ex_boxes);
+ auto gt_boxes_et = framework::EigenTensor::From(gt_boxes);
+ auto trg = framework::EigenTensor::From(*box_delta);
+ T ex_w, ex_h, ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
+ for (int64_t i = 0; i < box_num; ++i) {
+ ex_w = ex_boxes_et(i, 2) - ex_boxes_et(i, 0) + (normalized == false);
+ ex_h = ex_boxes_et(i, 3) - ex_boxes_et(i, 1) + (normalized == false);
+ ex_ctr_x = ex_boxes_et(i, 0) + 0.5 * ex_w;
+ ex_ctr_y = ex_boxes_et(i, 1) + 0.5 * ex_h;
+
+ gt_w = gt_boxes_et(i, 2) - gt_boxes_et(i, 0) + (normalized == false);
+ gt_h = gt_boxes_et(i, 3) - gt_boxes_et(i, 1) + (normalized == false);
+ gt_ctr_x = gt_boxes_et(i, 0) + 0.5 * gt_w;
+ gt_ctr_y = gt_boxes_et(i, 1) + 0.5 * gt_h;
+
+ trg(i, 0) = (gt_ctr_x - ex_ctr_x) / ex_w;
+ trg(i, 1) = (gt_ctr_y - ex_ctr_y) / ex_h;
+ trg(i, 2) = std::log(gt_w / ex_w);
+ trg(i, 3) = std::log(gt_h / ex_h);
+
+ if (weights) {
+ trg(i, 0) = trg(i, 0) / weights[0];
+ trg(i, 1) = trg(i, 1) / weights[1];
+ trg(i, 2) = trg(i, 2) / weights[2];
+ trg(i, 3) = trg(i, 3) / weights[3];
+ }
+ }
+}
+
+template
+void Gather(const T* in, const int in_stride, const int* index, const int num,
+ T* out) {
+ const int stride_bytes = in_stride * sizeof(T);
+ for (int i = 0; i < num; ++i) {
+ int id = index[i];
+ memcpy(out + i * in_stride, in + id * in_stride, stride_bytes);
+ }
+}
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/fluid/operators/detection/generate_proposal_labels_op.cc b/paddle/fluid/operators/detection/generate_proposal_labels_op.cc
index 0571c46f6be99c9a06b7dd2abb310eeda506ecd5..be06dc19743cfa6f093bcb3f4e9f91af315d4211 100644
--- a/paddle/fluid/operators/detection/generate_proposal_labels_op.cc
+++ b/paddle/fluid/operators/detection/generate_proposal_labels_op.cc
@@ -14,6 +14,7 @@ limitations under the License. */
#include
#include
#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/math_function.h"
@@ -133,31 +134,6 @@ void BboxOverlaps(const Tensor& r_boxes, const Tensor& c_boxes,
}
}
-template
-void BoxToDelta(int box_num, const Tensor& ex_boxes, const Tensor& gt_boxes,
- const std::vector& weights, Tensor* box_delta) {
- auto ex_boxes_et = framework::EigenTensor::From(ex_boxes);
- auto gt_boxes_et = framework::EigenTensor::From(gt_boxes);
- auto box_delta_et = framework::EigenTensor::From(*box_delta);
- T ex_w, ex_h, ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
- for (int64_t i = 0; i < box_num; ++i) {
- ex_w = ex_boxes_et(i, 2) - ex_boxes_et(i, 0) + 1;
- ex_h = ex_boxes_et(i, 3) - ex_boxes_et(i, 1) + 1;
- ex_ctr_x = ex_boxes_et(i, 0) + 0.5 * ex_w;
- ex_ctr_y = ex_boxes_et(i, 1) + 0.5 * ex_h;
-
- gt_w = gt_boxes_et(i, 2) - gt_boxes_et(i, 0) + 1;
- gt_h = gt_boxes_et(i, 3) - gt_boxes_et(i, 1) + 1;
- gt_ctr_x = gt_boxes_et(i, 0) + 0.5 * gt_w;
- gt_ctr_y = gt_boxes_et(i, 1) + 0.5 * gt_h;
-
- box_delta_et(i, 0) = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0];
- box_delta_et(i, 1) = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1];
- box_delta_et(i, 2) = log(gt_w / ex_w) / ex_w / weights[2];
- box_delta_et(i, 3) = log(gt_h / ex_h) / ex_h / weights[3];
- }
-}
-
template
std::vector> SampleFgBgGt(
const platform::CPUDeviceContext& context, Tensor* iou,
@@ -243,12 +219,11 @@ void GatherBoxesLabels(const platform::CPUDeviceContext& context,
Tensor* sampled_labels, Tensor* sampled_gts) {
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
- int gt_num = fg_num + bg_num;
Tensor fg_inds_t, bg_inds_t, gt_box_inds_t, gt_label_inds_t;
int* fg_inds_data = fg_inds_t.mutable_data({fg_num}, context.GetPlace());
int* bg_inds_data = bg_inds_t.mutable_data({bg_num}, context.GetPlace());
int* gt_box_inds_data =
- gt_box_inds_t.mutable_data({gt_num}, context.GetPlace());
+ gt_box_inds_t.mutable_data({fg_num}, context.GetPlace());
int* gt_label_inds_data =
gt_label_inds_t.mutable_data({fg_num}, context.GetPlace());
std::copy(fg_inds.begin(), fg_inds.end(), fg_inds_data);
@@ -303,18 +278,20 @@ std::vector SampleRoisForOneImage(
// Gather boxes and labels
Tensor sampled_boxes, sampled_labels, sampled_gts;
- int boxes_num = fg_inds.size() + bg_inds.size();
+ int fg_num = fg_inds.size();
+ int bg_num = bg_inds.size();
+ int boxes_num = fg_num + bg_num;
framework::DDim bbox_dim({boxes_num, kBoxDim});
sampled_boxes.mutable_data(bbox_dim, context.GetPlace());
sampled_labels.mutable_data({boxes_num}, context.GetPlace());
- sampled_gts.mutable_data(bbox_dim, context.GetPlace());
+ sampled_gts.mutable_data({fg_num, kBoxDim}, context.GetPlace());
GatherBoxesLabels(context, boxes, *gt_boxes, *gt_classes, fg_inds, bg_inds,
gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts);
// Compute targets
Tensor bbox_targets_single;
bbox_targets_single.mutable_data(bbox_dim, context.GetPlace());
- BoxToDelta(boxes_num, sampled_boxes, sampled_gts, bbox_reg_weights,
+ BoxToDelta(fg_num, sampled_boxes, sampled_gts, nullptr, false,
&bbox_targets_single);
// Scale rois
@@ -427,7 +404,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel {
auto rpn_rois_lod = rpn_rois->lod().back();
auto gt_classes_lod = gt_classes->lod().back();
auto gt_boxes_lod = gt_boxes->lod().back();
- for (size_t i = 0; i < n; ++i) {
+ for (int i = 0; i < n; ++i) {
Tensor rpn_rois_slice =
rpn_rois->Slice(rpn_rois_lod[i], rpn_rois_lod[i + 1]);
Tensor gt_classes_slice =
diff --git a/paddle/fluid/operators/detection/generate_proposals_op.cc b/paddle/fluid/operators/detection/generate_proposals_op.cc
index fcdcafae7273afa6887ee531dfc37ef833b92d68..ebe6830eccd87a156768eb0d4b96220bcc9f4edc 100644
--- a/paddle/fluid/operators/detection/generate_proposals_op.cc
+++ b/paddle/fluid/operators/detection/generate_proposals_op.cc
@@ -311,8 +311,7 @@ class GenerateProposalsKernel : public framework::OpKernel {
rpn_rois->mutable_data({bbox_deltas->numel() / 4, 4},
context.GetPlace());
- rpn_roi_probs->mutable_data({scores->numel() / 4, 1},
- context.GetPlace());
+ rpn_roi_probs->mutable_data({scores->numel(), 1}, context.GetPlace());
Tensor bbox_deltas_swap, scores_swap;
bbox_deltas_swap.mutable_data({num, h_bbox, w_bbox, c_bbox},
@@ -421,7 +420,7 @@ class GenerateProposalsKernel : public framework::OpKernel {
CPUGather(ctx, proposals, keep, &bbox_sel);
CPUGather(ctx, scores_sel, keep, &scores_filter);
if (nms_thresh <= 0) {
- return std::make_pair(bbox_sel, scores_sel);
+ return std::make_pair(bbox_sel, scores_filter);
}
Tensor keep_nms = NMS(ctx, &bbox_sel, &scores_filter, nms_thresh, eta);
diff --git a/paddle/fluid/operators/detection/rpn_target_assign_op.cc b/paddle/fluid/operators/detection/rpn_target_assign_op.cc
index 177ff7cf187bc9daf69889e99ca57ae18766de90..88757f25cd9a5789758640de2d9cae0b12350b25 100644
--- a/paddle/fluid/operators/detection/rpn_target_assign_op.cc
+++ b/paddle/fluid/operators/detection/rpn_target_assign_op.cc
@@ -14,6 +14,7 @@ limitations under the License. */
#include
#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
@@ -46,156 +47,219 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
auto in_dims = ctx->GetInputDim("DistMat");
PADDLE_ENFORCE_EQ(in_dims.size(), 2,
"The rank of Input(DistMat) must be 2.");
+
+ ctx->SetOutputDim("LocationIndex", {-1});
+ ctx->SetOutputDim("ScoreIndex", {-1});
+ ctx->SetOutputDim("TargetLabel", {-1, 1});
+ ctx->SetOutputDim("TargetBBox", {-1, 4});
+ }
+
+ protected:
+ framework::OpKernelType GetExpectedKernelType(
+ const framework::ExecutionContext& ctx) const override {
+ return framework::OpKernelType(
+ framework::ToDataType(
+ ctx.Input("DistMat")->type()),
+ platform::CPUPlace());
}
};
template
class RpnTargetAssignKernel : public framework::OpKernel {
public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto* anchor_t = context.Input("Anchor"); // (H*W*A) * 4
+ auto* gt_bbox_t = context.Input("GtBox");
+ auto* dist_t = context.Input("DistMat");
+
+ auto* loc_index_t = context.Output("LocationIndex");
+ auto* score_index_t = context.Output("ScoreIndex");
+ auto* tgt_bbox_t = context.Output("TargetBBox");
+ auto* tgt_lbl_t = context.Output("TargetLabel");
+
+ auto lod = dist_t->lod().back();
+ int64_t batch_num = static_cast(lod.size() - 1);
+ int64_t anchor_num = dist_t->dims()[1];
+ PADDLE_ENFORCE_EQ(anchor_num, anchor_t->dims()[0]);
+
+ int rpn_batch_size = context.Attr("rpn_batch_size_per_im");
+ float pos_threshold = context.Attr("rpn_positive_overlap");
+ float neg_threshold = context.Attr("rpn_negative_overlap");
+ float fg_fraction = context.Attr("fg_fraction");
+
+ int fg_num_per_batch = static_cast(rpn_batch_size * fg_fraction);
+
+ int64_t max_num = batch_num * anchor_num;
+ auto place = context.GetPlace();
+
+ tgt_bbox_t->mutable_data({max_num, 4}, place);
+ auto* loc_index = loc_index_t->mutable_data({max_num}, place);
+ auto* score_index = score_index_t->mutable_data({max_num}, place);
+
+ Tensor tmp_tgt_lbl;
+ auto* tmp_lbl_data = tmp_tgt_lbl.mutable_data({max_num}, place);
+ auto& dev_ctx = context.device_context();
+ math::SetConstant iset;
+ iset(dev_ctx, &tmp_tgt_lbl, static_cast(-1));
+
+ std::random_device rnd;
+ std::minstd_rand engine;
+ int seed =
+ context.Attr("fix_seed") ? context.Attr("seed") : rnd();
+ engine.seed(seed);
+
+ int fg_num = 0;
+ int bg_num = 0;
+ for (int i = 0; i < batch_num; ++i) {
+ Tensor dist = dist_t->Slice(lod[i], lod[i + 1]);
+ Tensor gt_bbox = gt_bbox_t->Slice(lod[i], lod[i + 1]);
+ auto fg_bg_gt = SampleFgBgGt(dev_ctx, dist, pos_threshold, neg_threshold,
+ rpn_batch_size, fg_num_per_batch, engine,
+ tmp_lbl_data + i * anchor_num);
+
+ int cur_fg_num = fg_bg_gt[0].size();
+ int cur_bg_num = fg_bg_gt[1].size();
+ std::transform(fg_bg_gt[0].begin(), fg_bg_gt[0].end(), loc_index,
+ [i, anchor_num](int d) { return d + i * anchor_num; });
+ memcpy(score_index, loc_index, cur_fg_num * sizeof(int));
+ std::transform(fg_bg_gt[1].begin(), fg_bg_gt[1].end(),
+ score_index + cur_fg_num,
+ [i, anchor_num](int d) { return d + i * anchor_num; });
+
+ // get target bbox deltas
+ if (cur_fg_num) {
+ Tensor fg_gt;
+ T* gt_data = fg_gt.mutable_data({cur_fg_num, 4}, place);
+ Tensor tgt_bbox = tgt_bbox_t->Slice(fg_num, fg_num + cur_fg_num);
+ T* tgt_data = tgt_bbox.data();
+ Gather(anchor_t->data(), 4,
+ reinterpret_cast(&fg_bg_gt[0][0]), cur_fg_num,
+ tgt_data);
+ Gather(gt_bbox.data(), 4, reinterpret_cast(&fg_bg_gt[2][0]),
+ cur_fg_num, gt_data);
+ BoxToDelta(cur_fg_num, tgt_bbox, fg_gt, nullptr, false, &tgt_bbox);
+ }
+
+ loc_index += cur_fg_num;
+ score_index += cur_fg_num + cur_bg_num;
+ fg_num += cur_fg_num;
+ bg_num += cur_bg_num;
+ }
+
+ int lbl_num = fg_num + bg_num;
+ PADDLE_ENFORCE_LE(fg_num, max_num);
+ PADDLE_ENFORCE_LE(lbl_num, max_num);
+
+ tgt_bbox_t->Resize({fg_num, 4});
+ loc_index_t->Resize({fg_num});
+ score_index_t->Resize({lbl_num});
+ auto* lbl_data = tgt_lbl_t->mutable_data({lbl_num, 1}, place);
+ Gather(tmp_lbl_data, 1, score_index_t->data(), lbl_num,
+ lbl_data);
+ }
+
+ private:
void ScoreAssign(const T* dist_data, const Tensor& anchor_to_gt_max,
const int row, const int col, const float pos_threshold,
- const float neg_threshold, int64_t* target_label_data,
+ const float neg_threshold, int64_t* target_label,
std::vector* fg_inds, std::vector* bg_inds) const {
- int fg_offset = fg_inds->size();
- int bg_offset = bg_inds->size();
+ float epsilon = 0.0001;
for (int64_t i = 0; i < row; ++i) {
const T* v = dist_data + i * col;
- T max_dist = *std::max_element(v, v + col);
+ T max = *std::max_element(v, v + col);
for (int64_t j = 0; j < col; ++j) {
- T val = dist_data[i * col + j];
- if (val == max_dist) target_label_data[j] = 1;
+ if (std::abs(max - v[j]) < epsilon) {
+ target_label[j] = 1;
+ }
}
}
- // Pick the fg/bg and count the number
+ // Pick the fg/bg
+ const T* anchor_to_gt_max_data = anchor_to_gt_max.data();
for (int64_t j = 0; j < col; ++j) {
- if (anchor_to_gt_max.data()[j] > pos_threshold) {
- target_label_data[j] = 1;
- } else if (anchor_to_gt_max.data()[j] < neg_threshold) {
- target_label_data[j] = 0;
+ if (anchor_to_gt_max_data[j] >= pos_threshold) {
+ target_label[j] = 1;
+ } else if (anchor_to_gt_max_data[j] < neg_threshold) {
+ target_label[j] = 0;
}
- if (target_label_data[j] == 1) {
- fg_inds->push_back(fg_offset + j);
- } else if (target_label_data[j] == 0) {
- bg_inds->push_back(bg_offset + j);
+ if (target_label[j] == 1) {
+ fg_inds->push_back(j);
+ } else if (target_label[j] == 0) {
+ bg_inds->push_back(j);
}
}
}
- void ReservoirSampling(const int num, const int offset,
- std::minstd_rand engine,
+ void ReservoirSampling(const int num, std::minstd_rand engine,
std::vector* inds) const {
std::uniform_real_distribution uniform(0, 1);
- const int64_t size = static_cast(inds->size() - offset);
- if (size > num) {
- for (int64_t i = num; i < size; ++i) {
+ size_t len = inds->size();
+ if (len > static_cast(num)) {
+ for (size_t i = num; i < len; ++i) {
int rng_ind = std::floor(uniform(engine) * i);
if (rng_ind < num)
- std::iter_swap(inds->begin() + rng_ind + offset,
- inds->begin() + i + offset);
+ std::iter_swap(inds->begin() + rng_ind, inds->begin() + i);
}
+ inds->resize(num);
}
}
- void RpnTargetAssign(const framework::ExecutionContext& ctx,
- const Tensor& dist, const float pos_threshold,
- const float neg_threshold, const int rpn_batch_size,
- const int fg_num, std::minstd_rand engine,
- std::vector* fg_inds, std::vector* bg_inds,
- int64_t* target_label_data) const {
+ // std::vector> RpnTargetAssign(
+ std::vector> SampleFgBgGt(
+ const platform::CPUDeviceContext& ctx, const Tensor& dist,
+ const float pos_threshold, const float neg_threshold,
+ const int rpn_batch_size, const int fg_num, std::minstd_rand engine,
+ int64_t* target_label) const {
auto* dist_data = dist.data();
- int64_t row = dist.dims()[0];
- int64_t col = dist.dims()[1];
- int fg_offset = fg_inds->size();
- int bg_offset = bg_inds->size();
+ int row = dist.dims()[0];
+ int col = dist.dims()[1];
+
+ std::vector fg_inds;
+ std::vector bg_inds;
+ std::vector gt_inds;
// Calculate the max IoU between anchors and gt boxes
- Tensor anchor_to_gt_max;
- anchor_to_gt_max.mutable_data(
- framework::make_ddim({static_cast(col), 1}),
- platform::CPUPlace());
- auto& place = *ctx.template device_context()
- .eigen_device();
- auto x = EigenMatrix::From(dist);
- auto x_col_max = EigenMatrix::From(anchor_to_gt_max);
- x_col_max.device(place) =
- x.maximum(Eigen::DSizes(0))
- .reshape(Eigen::DSizes(static_cast(col), 1));
+ // Map from anchor to gt box that has highest overlap
+ auto place = ctx.GetPlace();
+ Tensor anchor_to_gt_max, anchor_to_gt_argmax;
+ anchor_to_gt_max.mutable_data