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“f32ca6369099f5d3776ae87d431b9b39ea8eba3e”上不存在“paddle/phi/kernels/cpu/fold_kernel.cc”
提交 a121c898 编写于 作者: D Dang Qingqing
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into quantize_transpiler_update

上级 e3c7348f 225ecee5
隐藏空白更改
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Dockerfile
浏览文件 @ a121c898
......@@ -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
......
cmake/inference_lib.cmake
浏览文件 @ a121c898
......@@ -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")
......
doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md
浏览文件 @ a121c898
......@@ -60,6 +60,7 @@
图3. 编码器-解码器框架
</div>
<a name="编码器"></a>
#### 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`<s>`，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`<s>`，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：
$$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
......@@ -93,6 +94,7 @@ $$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
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)。
<a name="柱搜索算法"></a>
### 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“`<s>你好<e>`”，就算目标语言字典中只有3个词（`<s>`, `<e>`, `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。
......
doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md
浏览文件 @ a121c898
......@@ -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。
<a name="栈值双向LSTM"></a>
### 栈式双向LSTM
栈式双向神经网络`stacked_lstm_net`的代码片段如下：
......
doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md
浏览文件 @ a121c898
......@@ -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 <e>
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。
<a name="训练模型"></a>
## 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
...
```
<a name="模型应用"></a>
## 模型应用
在模型训练后，我们可以用它做一些预测。
......
doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
浏览文件 @ a121c898
......@@ -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}} $
......
doc/fluid/new_docs/user_guides/howto/debug/visualdl.md
浏览文件 @ a121c898
......@@ -104,6 +104,7 @@ visualDL --logdir=scratch_log --port=8080
# 访问 http://127.0.0.1:8080
```
如果出现`TypeError: __init__() got an unexpected keyword argument 'file'`, 是因为protobuf不是3.5以上，运行`pip install --upgrade protobuf`就能解决。
如果在虚拟环境下仍然遇到安装问题，请尝试以下方法。
......@@ -149,7 +150,7 @@ 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
......
doc/fluid/new_docs/user_guides/howto/inference/native_infer.rst
浏览文件 @ a121c898
......@@ -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 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/contrib/inference>`__
`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api>`_
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
------------
......
paddle/fluid/API.spec
浏览文件 @ a121c898
......@@ -43,6 +43,7 @@ paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list',
paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.Trainer.save_inference_model ArgSpec(args=['self', 'param_path', 'feeded_var_names', 'target_var_indexes'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.test ArgSpec(args=['self', 'reader', 'feed_order'], varargs=None, keywords=None, defaults=None)
......@@ -312,7 +313,7 @@ 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 +377,7 @@ 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))
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ a121c898
......@@ -326,7 +326,7 @@ std::unique_ptr<ir::Graph> 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<std::string> *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<GraphOps>(kGraphOps)) {
if (prev_op->Name() == prev_op_name) {
auto *dep_var = new DummyVarHandle(result->CreateControlDepVar());
prev_op->AddOutput(dep_var);
result->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
op->AddInput(dep_var);
}
}
}
void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
ir::Node *node) const {
int op_dev_id = -1;
......
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ a121c898
......@@ -69,9 +69,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
std::vector<std::string> FindDistTrainRecvVars(
const std::vector<ir::Node *> &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<std::string> *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;
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ a121c898
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)
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @ a121c898
......@@ -13,13 +13,10 @@
// limitations under the License.
#include "paddle/fluid/framework/ir/attention_lstm_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/api/helper.h"
namespace paddle {
namespace framework {
......@@ -99,17 +96,13 @@ 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);
}
......
paddle/fluid/framework/ir/fc_fuse_pass.cc
浏览文件 @ a121c898
......@@ -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<Node*>* links, Node* from, Node* to) {
for (auto*& n : *links) {
if (n == from) {
n = to;
return true;
}
}
return false;
}
std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init("fc", graph.get());
FusePassBase::Init("fc_fuse", graph.get());
std::unordered_set<Node*> 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<ir::Graph> 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<std::string>({fc_x_in}));
desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
desc.SetOutput("Out", std::vector<std::string>({fc_out}));
desc.SetOutput("Out", std::vector<std::string>({fc_out_out}));
desc.SetType("fc");
auto fc_node = g->CreateOpNode(&desc); // OpDesc will be copied.
fc_node->inputs =
std::vector<Node*>({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++;
};
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
浏览文件 @ a121c898
......@@ -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 <string>
#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<framework::LoDTensor>();
scope->Var(name_scope + "/BatchCellPreAct.new")
->GetMutable<framework::LoDTensor>();
scope->Var(name_scope + "/BatchedGate.new")
->GetMutable<framework::LoDTensor>();
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<const Node*> 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<const Node*> marked_nodes({mul_n, lstm_n});
GraphSafeRemoveNodes(graph, marked_nodes);
}
#undef GET_NODE
// Remove unneeded nodes.
std::unordered_set<const Node*> marked_nodes({mul_n, lstm_n});
GraphSafeRemoveNodes(graph, marked_nodes);
++fusion_count;
};
gpd(graph, fc_no_bias_handler);
gpd(graph, handler);
return fusion_count;
}
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.h
浏览文件 @ a121c898
......@@ -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"
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ a121c898
......@@ -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;
}
......@@ -86,7 +85,7 @@ void GraphPatternDetector::operator()(Graph* graph,
LOG(INFO) << "detect " << subgraphs.size() << " subgraph matches the pattern";
int id = 0;
for (auto& g : subgraphs) {
LOG(INFO) << "optimizing #" << id++ << " subgraph";
VLOG(3) << "optimizing #" << id++ << " subgraph";
handler(g, graph);
}
}
......@@ -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*>(&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<PDNode*>& 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*>(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*>(node)->outputs.erase(it);
} else
} else {
it++;
}
}
}
}
......
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ a121c898
......@@ -19,6 +19,9 @@
#endif
#include <numeric>
#include <string>
#include <utility>
#include <vector>
#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<subgraph_t>* 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<subgraph_t>* 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
paddle/fluid/framework/ir/graph_pattern_detector_tester.cc
浏览文件 @ a121c898
......@@ -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");
......
paddle/fluid/framework/ir/graph_viz_pass.cc
浏览文件 @ a121c898
......@@ -50,20 +50,37 @@ std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
Dot dot;
std::vector<Dot::Attr> op_attrs({Dot::Attr("style", "filled"),
Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "red")});
std::vector<Dot::Attr> var_attrs({Dot::Attr("style", "filled,rounded"),
// Dot::Attr("shape", "diamond"),
Dot::Attr("fillcolor", "yellow")});
std::vector<Dot::Attr> marked_op_attrs({Dot::Attr("style", "filled"),
Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "lightgray")});
std::vector<Dot::Attr> marked_var_attrs(
{Dot::Attr("style", "filled,rounded"),
// Dot::Attr("shape", "diamond"),
Dot::Attr("fillcolor", "lightgray")});
const std::vector<Dot::Attr> op_attrs({
Dot::Attr("style", "rounded,filled,bold"), //
Dot::Attr("shape", "box"), //
Dot::Attr("color", "#303A3A"), //
Dot::Attr("fontcolor", "#ffffff"), //
Dot::Attr("width", "1.3"), //
Dot::Attr("height", "0.84"), //
Dot::Attr("fontname", "Arial"), //
});
const std::vector<Dot::Attr> arg_attrs({
Dot::Attr("shape", "box"), //
Dot::Attr("style", "rounded,filled,bold"), //
Dot::Attr("fontname", "Arial"), //
Dot::Attr("fillcolor", "#999999"), //
Dot::Attr("color", "#dddddd"), //
});
const std::vector<Dot::Attr> param_attrs({
Dot::Attr("shape", "box"), //
Dot::Attr("style", "rounded,filled,bold"), //
Dot::Attr("fontname", "Arial"), //
Dot::Attr("color", "#148b97"), //
Dot::Attr("fontcolor", "#ffffff"), //
});
const std::vector<Dot::Attr> marked_op_attrs(
{Dot::Attr("style", "rounded,filled,bold"), Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "yellow")});
const std::vector<Dot::Attr> marked_var_attrs(
{Dot::Attr("style", "filled,rounded"), Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "yellow")});
auto marked_nodes = ConsumeMarkedNodes(graph.get());
// Create nodes
......@@ -74,9 +91,17 @@ std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
marked_nodes.count(n) ? marked_op_attrs : op_attrs;
dot.AddNode(node_id, attr, node_id);
} else if (n->IsVar()) {
decltype(op_attrs) attr =
marked_nodes.count(n) ? marked_var_attrs : var_attrs;
dot.AddNode(node_id, attr, node_id);
decltype(op_attrs)* attr;
if (marked_nodes.count(n)) {
attr = &marked_var_attrs;
} else if (const_cast<Node*>(n)->Var() &&
const_cast<Node*>(n)->Var()->Persistable()) {
attr = &param_attrs;
} else {
attr = &arg_attrs;
}
dot.AddNode(node_id, *attr, node_id);
}
node2dot[n] = node_id;
}
......
paddle/fluid/framework/ir/infer_clean_graph_pass.cc
浏览文件 @ a121c898
......@@ -13,42 +13,41 @@
// limitations under the License.
#include <algorithm>
#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<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> 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<Node*> invalid_nodes;
std::unordered_set<const Node*> 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;
}
......
paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc
浏览文件 @ a121c898
......@@ -219,16 +219,13 @@ std::unique_ptr<ir::Graph> 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<const Node*> marked_nodes;
......@@ -241,7 +238,6 @@ std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
marked_nodes.erase(sequence_expand0_in);
marked_nodes.erase(sequence_expand1_in);
marked_nodes.erase(fc_out);
GraphSafeRemoveNodes(graph, marked_nodes);
});
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ a121c898
......@@ -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 ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ a121c898
......@@ -33,7 +33,7 @@ function (inference_analysis_test TARGET)
endif()
cc_test(${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} ${LAC_DATA_URL} "lac_data.txt.tar.gz")
endif()
......@@ -108,3 +96,15 @@ inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api
ARGS --infer_model=${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} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
endif()
inference_analysis_test(test_text_classification SRCS analyzer_text_classification_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ a121c898
......@@ -14,6 +14,7 @@
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <string>
#include <vector>
#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 +98,15 @@ class DfgPassManagerImpl final : public DfgPassManager {
Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
void Analyzer::Run(Argument* argument) {
// Ugly support fluid-to-ir-pass
argument->Set(kFluidToIrPassesAttr,
new std::vector<std::string>({
// 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" //
}));
std::vector<std::string> 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");
argument->Set(kFluidToIrPassesAttr, new std::vector<std::string>(passes));
for (auto& x : data_) {
PADDLE_ENFORCE(x->Initialize(argument));
......@@ -122,6 +115,11 @@ void Analyzer::Run(Argument* argument) {
}
}
Analyzer& Analyzer::DisableIrPasses(const std::vector<std::string>& passes) {
disabled_ir_passes_.insert(passes.begin(), passes.end());
return *this;
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ a121c898
......@@ -36,16 +36,10 @@ limitations under the License. */
*/
#include <gflags/gflags.h>
#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<PassManager> {
void Run(Argument* argument);
Analyzer& DisableIrPasses(const std::vector<std::string>& 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<std::string> 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<std::string> disabled_ir_passes_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ a121c898
......@@ -16,19 +16,21 @@
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include <thread> // NOLINT
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#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"
DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
DEFINE_int32(batch_size, 10, "batch size.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
namespace paddle {
namespace inference {
......@@ -219,39 +221,6 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
os << " - type: ";
switch (tensor.dtype) {
case PaddleDType::FLOAT32:
os << "float32";
break;
case PaddleDType::INT64:
os << "int64";
break;
default:
os << "unset";
}
os << '\n';
os << " - shape: " << to_string(tensor.shape) << '\n';
os << " - lod: ";
for (auto &l : tensor.lod) {
os << to_string(l) << "; ";
}
os << "\n";
os << " - data: ";
int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
[](int a, int b) { return a * b; });
for (int i = 0; i < dim; i++) {
os << static_cast<float *>(tensor.data.data())[i] << " ";
}
os << '\n';
return os.str();
}
} // namespace
const float ditu_rnn_target_data[] = {
......@@ -265,57 +234,97 @@ const float ditu_rnn_target_data[] = {
10.7286, 12.0595, 10.6672, 0, 0, 0, 0, 0,
93.5771, 3.84641, 0, 0, 0, 0, 0, 0,
169.426, 0, 0, 0, 0, 0, 0, 0};
void CompareResult(const std::vector<PaddleTensor> &outputs,
const std::vector<PaddleTensor> &base_outputs) {
PADDLE_ENFORCE_GT(outputs.size(), 0);
PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
for (size_t i = 0; i < outputs.size(); i++) {
auto &out = outputs[i];
auto &base_out = base_outputs[i];
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
1, [](int a, int b) { return a * b; });
PADDLE_ENFORCE_EQ(size, size1);
PADDLE_ENFORCE_GT(size, 0);
float *data = static_cast<float *>(out.data.data());
float *base_data = static_cast<float *>(base_out.data.data());
for (size_t i = 0; i < size; i++) {
EXPECT_NEAR(data[i], base_data[i], 1e-3);
}
}
}
// Test with a really complicate model.
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;
void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
int num_threads) {
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.
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
auto base_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kAnalysis>(config);
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
std::vector<PaddleTensor> input_slots;
DataRecord data(data_path, batch_size);
DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
// Prepare inputs.
PrepareInputs(&input_slots, &data, batch_size);
std::vector<PaddleTensor> outputs, base_outputs;
base_predictor->Run(input_slots, &base_outputs);
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictor->Run(input_slots, &outputs);
}
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << num_times
<< ", latency: " << timer.toc() / num_times << "ms";
LOG(INFO) << "=====================================";
PADDLE_ENFORCE_GT(outputs.size(), 0);
PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
for (size_t i = 0; i < outputs.size(); i++) {
auto &out = outputs[i];
auto &base_out = base_outputs[i];
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
1, [](int a, int b) { return a * b; });
PADDLE_ENFORCE_EQ(size, size1);
PADDLE_ENFORCE_GT(size, 0);
float *data = static_cast<float *>(out.data.data());
float *base_data = static_cast<float *>(base_out.data.data());
for (size_t j = 0; j < size; j++) {
EXPECT_NEAR(data[j], base_data[j], 1e-3);
if (num_threads == 1) {
// Prepare inputs.
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictor->Run(input_slots, &outputs);
}
PrintTime(batch_size, num_times, 1, 0, timer.toc() / num_times);
CompareResult(outputs, base_outputs);
} else {
std::vector<std::thread> threads;
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
// TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
// because AttentionLSTM's hard code nodeid will be damanged.
for (int tid = 0; tid < num_threads; ++tid) {
predictors.emplace_back(
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config));
}
for (int tid = 0; tid < num_threads; ++tid) {
threads.emplace_back([&, tid]() {
// Each thread should have local input_slots and outputs.
std::vector<PaddleTensor> input_slots;
DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
PrepareInputs(&input_slots, &data, batch_size);
std::vector<PaddleTensor> outputs;
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictors[tid]->Run(input_slots, &outputs);
}
PrintTime(batch_size, num_times, num_threads, tid,
timer.toc() / num_times);
CompareResult(outputs, base_outputs);
});
}
for (int i = 0; i < num_threads; ++i) {
threads[i].join();
}
}
LOG(INFO) << "=====================================";
if (use_analysis && activate_ir) {
AnalysisPredictor *analysis_predictor =
......@@ -327,40 +336,45 @@ 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;
// Directly infer with the original model.
TEST(Analyzer, DituRNN_without_analysis) {
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, false, false, FLAGS_repeat);
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.
}
}
// Inference with the original model with the analysis turned on, the analysis
// module will transform the program to a data flow graph.
TEST(Analyzer, DituRNN_with_analysis) {
LOG(INFO) << "ditu rnn with analysis";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, true, false, FLAGS_repeat);
// Inference with analysis and IR, easy for profiling independently.
TEST(Analyzer, DituRNN) {
TestDituRNNPrediction(true, true, FLAGS_num_threads);
}
// Inference with analysis and IR. The IR module will fuse some large kernels.
TEST(Analyzer, DituRNN_with_analysis_with_IR) {
LOG(INFO) << "ditu rnn with analysis and IR fuse";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, true, true, FLAGS_repeat);
// Other unit-tests of DituRNN, test different options of use_analysis,
// activate_ir and multi-threads.
TEST(Analyzer, DituRNN_tests) {
int num_threads[2] = {1, 4};
for (auto i : num_threads) {
// Directly infer with the original model.
TestDituRNNPrediction(false, false, i);
// Inference with the original model with the analysis turned on, the
// analysis
// module will transform the program to a data flow graph.
TestDituRNNPrediction(true, false, i);
// Inference with analysis and IR. The IR module will fuse some large
// kernels.
TestDituRNNPrediction(true, true, i);
}
}
} // 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);
paddle/fluid/inference/analysis/analyzer_text_classification_tester.cc 0 → 100644
浏览文件 @ a121c898
// 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 "paddle/fluid/inference/analysis/analyzer.h"
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.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 <typename T>
std::string to_string(const std::vector<T> &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<PaddleTensor> 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<int>({3, 1});
// dtype --
input.dtype = PaddleDType::INT64;
// LoD --
input.lod = std::vector<std::vector<size_t>>({{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<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
inference::Timer timer;
double sum = 0;
std::vector<PaddleTensor> 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<float *>(output.data.data())[i] << " ";
}
LOG(INFO) << "output.data summary: " << ss.str();
// one batch ends
}
}
TEST(text_classification, basic) { Main(FLAGS_batch_size); }
} // namespace paddle
paddle/fluid/inference/analysis/flags.h 0 → 100644
浏览文件 @ a121c898
// 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 <gflags/gflags.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);
paddle/fluid/inference/analysis/fluid_to_ir_pass.h
浏览文件 @ a121c898
......@@ -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<Scope>(ir::kParamScopeAttr)));
}
const auto &ir_passes_to_apply =
argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
ir_passes.Apply(ir_passes_to_apply);
if (FLAGS_IA_enable_ir) {
const auto &ir_passes_to_apply =
argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
ir_passes.Apply(ir_passes_to_apply);
}
PADDLE_ENFORCE(argument_->main_dfg.get());
argument_->main_dfg->Build(ir_passes.graph());
......
paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc
浏览文件 @ a121c898
......@@ -16,6 +16,7 @@
#include <gtest/gtest.h>
#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);
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ a121c898
......@@ -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 +44,7 @@ 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)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ a121c898
......@@ -14,10 +14,13 @@
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include <memory>
#include <string>
#include <vector>
#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 {
......@@ -27,10 +30,11 @@ 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();
}
PADDLE_ENFORCE(!parent_scope);
if (parent_scope) {
scope_ = parent_scope;
sub_scope_ = &(parent_scope->NewScope());
......@@ -72,7 +76,7 @@ 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 +93,26 @@ 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::Scope>(framework::ir::kParamScopeAttr));
LOG(INFO) << "optimize end ==";
if (argument_.Has(framework::ir::kParamScopeAttr)) {
// Update scope.
scope_.reset(
argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
}
LOG(INFO) << "== optimize end ==";
}
template <>
std::unique_ptr<PaddlePredictor> 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 +139,3 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
}
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ a121c898
......@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <string>
#include <vector>
#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<framework::Scope>& parent_scope);
......@@ -44,7 +46,7 @@ class AnalysisPredictor : public NativePaddlePredictor {
Argument& analysis_argument() { return argument_; }
private:
NativeConfig config_;
AnalysisConfig config_;
Argument argument_;
};
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ a121c898
......@@ -176,7 +176,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &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) {
......
paddle/fluid/inference/api/demo_ci/run.sh
浏览文件 @ a121c898
......@@ -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() {
......
paddle/fluid/inference/api/helper.h
浏览文件 @ a121c898
......@@ -14,8 +14,10 @@
#pragma once
#include <glog/logging.h>
#include <sys/time.h>
#include <algorithm>
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
......@@ -87,5 +89,45 @@ static void TensorAssignData(PaddleTensor *tensor,
}
}
std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
os << " - type: ";
switch (tensor.dtype) {
case PaddleDType::FLOAT32:
os << "float32";
break;
case PaddleDType::INT64:
os << "int64";
break;
default:
os << "unset";
}
os << '\n';
os << " - shape: " << to_string(tensor.shape) << '\n';
os << " - lod: ";
for (auto &l : tensor.lod) {
os << to_string(l) << "; ";
}
os << "\n";
os << " - data: ";
int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
[](int a, int b) { return a * b; });
for (int i = 0; i < dim; i++) {
os << static_cast<float *>(tensor.data.data())[i] << " ";
}
os << '\n';
return os.str();
}
void PrintTime(int batch_size, int repeat, int num_threads, int tid,
double latency) {
LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << repeat
<< ", threads: " << num_threads << ", thread id: " << tid
<< ", latency: " << latency << "ms";
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ a121c898
......@@ -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<std::string> ir_passes{"attention_lstm_fuse_pass"};
};
// A factory to help create different predictors.
//
// FOR EXTENSION DEVELOPER:
......
paddle/fluid/operators/auc_op.cc
浏览文件 @ a121c898
......@@ -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 <string>
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<int>("num_thresholds");
int num_pred_buckets = ctx->Attrs().Get<int>("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<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
AddAttr<int>("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.
......
paddle/fluid/operators/auc_op.h
浏览文件 @ a121c898
......@@ -13,9 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#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 <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename DeviceContext, typename T>
class AucKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* predict = ctx.Input<Tensor>("Predict");
auto* label = ctx.Input<Tensor>("Label");
auto* auc = ctx.Output<Tensor>("AUC");
void Compute(const framework::ExecutionContext &ctx) const override {
auto *predict = ctx.Input<Tensor>("Predict");
auto *label = ctx.Input<Tensor>("Label");
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("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<Tensor>("TPOut");
auto* false_positive = ctx.Output<Tensor>("FPOut");
auto* true_negative = ctx.Output<Tensor>("TNOut");
auto* false_negative = ctx.Output<Tensor>("FNOut");
auto *auc = ctx.Output<Tensor>("AUC");
auto *stat_pos = ctx.Output<Tensor>("StatPosOut");
auto *stat_neg = ctx.Output<Tensor>("StatNegOut");
auto* auc_data = auc->mutable_data<double>(ctx.GetPlace());
auto *stat_pos_data = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *stat_neg_data = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
auc);
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("num_thresholds");
std::vector<double> thresholds_list;
thresholds_list.reserve(num_thresholds);
for (int i = 1; i < num_thresholds - 1; i++) {
thresholds_list[i] = static_cast<double>(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<Tensor>("BatchAUC");
std::vector<int64_t> stat_pos_batch(num_pred_buckets, 0);
std::vector<int64_t> 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<T>();
const auto *label_data = label->data<int64_t>();
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
const T* inference_data = predict->data<T>();
const auto* label_data = label->data<int64_t>();
auto* tp_data = true_positive->mutable_data<int64_t>(ctx.GetPlace());
auto* fn_data = false_negative->mutable_data<int64_t>(ctx.GetPlace());
auto* tn_data = true_negative->mutable_data<int64_t>(ctx.GetPlace());
auto* fp_data = false_positive->mutable_data<int64_t>(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<uint32_t>(
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<double>(ctx.GetPlace());
auto* fp_rate_data = fp_rate.mutable_data<double>(ctx.GetPlace());
auto* rec_rate_data = rec_rate.mutable_data<double>(ctx.GetPlace());
for (int i = 0; i < num_thresholds; i++) {
tp_rate_data[i] = (static_cast<double>(tp_data[i]) + epsilon) /
(tp_data[i] + fn_data[i] + epsilon);
fp_rate_data[i] =
static_cast<double>(fp_data[i]) / (fp_data[i] + tn_data[i] + epsilon);
rec_rate_data[i] = (static_cast<double>(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;
}
}
};
......
paddle/fluid/operators/distributed/request_handler_impl.cc
浏览文件 @ a121c898
......@@ -39,19 +39,6 @@ bool RequestSendHandler::Handle(const std::string& varname,
const std::string& out_var_name) {
VLOG(4) << "RequestSendHandler:" << varname;
// Async
if (!sync_mode_) {
rpc_server_->Profiler().OneStep();
try {
executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
scope);
} catch (std::exception& e) {
LOG(ERROR) << "async: run sub program error " << e.what();
return false;
}
return true;
}
// Sync
if (varname == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv BATCH_BARRIER_MESSAGE";
......@@ -60,17 +47,31 @@ bool RequestSendHandler::Handle(const std::string& varname,
VLOG(3) << "sync: recv complete message";
rpc_server_->Complete();
} else {
VLOG(3) << "sync: received var_name: " << varname;
rpc_server_->WaitCond(kRequestSend);
VLOG(3) << "sync: processing received var: " << varname;
if (invar == nullptr) {
LOG(FATAL) << "sync: Can not find server side var: " << varname;
return false;
}
if (invar->IsType<framework::SelectedRows>()) {
std::unique_lock<std::mutex> lock(mutex_sparse_vars_);
sparse_vars_.push_back(invar);
// Async
if (!sync_mode_) {
VLOG(3) << "async process var: " << varname;
rpc_server_->Profiler().OneStep();
try {
executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
scope);
} catch (std::exception& e) {
LOG(ERROR) << "async: run sub program error " << e.what();
return false;
}
return true;
} else { // sync
rpc_server_->WaitCond(kRequestSend);
VLOG(3) << "sync: processing received var: " << varname;
if (invar == nullptr) {
LOG(FATAL) << "sync: Can not find server side var: " << varname;
return false;
}
if (invar->IsType<framework::SelectedRows>()) {
std::unique_lock<std::mutex> lock(mutex_sparse_vars_);
sparse_vars_.push_back(invar);
}
}
}
return true;
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ a121c898
......@@ -119,7 +119,8 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
const framework::Tensor& last_scale,
const framework::Tensor& iter, const int window_size,
framework::Tensor* scales_arr, framework::Tensor* out_scale) {
auto& gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
T* scale_arr = scales_arr->mutable_data<T>(gpu_place);
T* out_scale_data = out_scale->mutable_data<T>(gpu_place);
......
paddle/fluid/operators/flatten_op.cc
浏览文件 @ a121c898
......@@ -157,6 +157,116 @@ class FlattenGradOp : public framework::OperatorBase {
}
};
// FIXME(zcd): flatten2 adds an intermediate output(XShape) based on flatten,
// the XShape is used to carry the shape and lod of X which will be used in
// flatten_grad, in this way, the framework can reuse the memory of X
// immediately the flatten2_op is finished.
// Considering compatibility issues, we could not fix flatten2_op
class Flatten2OpInferShape : public FlattenOpInferShape {
public:
void operator()(framework::InferShapeContext *ctx) const override {
FlattenOpInferShape::operator()(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output (XShape) of Flatten op should not be null.");
const auto &in_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(in_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < in_dims.size(); ++i) {
xshape_dims[i + 1] = in_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", "XShape");
}
};
class Flatten2Op : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axis = Attr<int>("axis");
auto in_dims =
scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
const auto &out_dims = FlattenOpInferShape::GetOutputShape(axis, in_dims);
framework::AttributeMap attrs;
attrs["shape"] = out_dims;
attrs["inplace"] = false;
// Invoke Reshape Op
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class Flatten2OpMaker : public FlattenOpMaker {
public:
void Make() override {
FlattenOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in FlattenGradOp.")
.AsIntermediate();
}
};
class Flatten2GradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("flatten2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Flatten2GradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("XShape"),
"Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = context->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
context->SetOutputDim(framework::GradVarName("X"), x_dims);
context->ShareLoD("XShape", framework::GradVarName("X"));
}
};
class Flatten2GradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto xshape_name = Input("XShape");
auto xshape_dims =
scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
attrs["inplace"] = false;
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {dout_name}}, {"Shape", {}}},
{{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
......@@ -167,3 +277,8 @@ REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
ops::FlattenOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker);
REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
ops::Flatten2GradInferShape);
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ a121c898
......@@ -89,12 +89,12 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
PADDLE_ENFORCE(!ctx->Attrs().Get<bool>("use_peepholes"),
"Do not support peephole yet.");
PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
auto use_peepholes = ctx->Attrs().Get<bool>("use_peepholes");
PADDLE_ENFORCE_EQ(b_dims[1], (use_peepholes ? 7 : 4) * frame_size,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes connection",
frame_size);
"7 * %d if enable peepholes connection or"
"4 * %d if disable peepholes",
frame_size, frame_size);
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
......@@ -232,16 +232,17 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
act_cand = act_functor(act_cand_str); \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* c0 = ctx.Input<Tensor>("C0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
auto* cell_out = ctx.Output<LoDTensor>("Cell"); \
#define INIT_BASE_INPUT_OUTPUT \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* c0 = ctx.Input<Tensor>("C0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
auto* cell_out = ctx.Output<LoDTensor>("Cell"); \
bool use_peepholes = ctx.Attr<bool>("use_peepholes"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
#define INIT_BASE_SIZES \
......@@ -266,12 +267,21 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : nullptr;
const T* c0_data = c0 ? c0->data<T>() : nullptr;
const T* bias_data = bias->data<T>();
const T* wc_data = bias_data + D4; // w_ic, w_fc, w_oc
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
// use local variable
framework::DDim check_dims({3, D});
Tensor checked_cell; // w_ic * Ct-1, w_fc * Ct-1, w_oc * Ct
auto checked_cell_data =
checked_cell.mutable_data<T>(check_dims, ctx.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D4, M, x_data, wx_data,
xx_data, bias->data<T>());
......@@ -297,46 +307,86 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
int seq_len = x_lod[0][bid + 1] - x_lod[0][bid];
const T* prev_c_data = nullptr;
const T* prev_h_data = nullptr;
int tstart = 0;
if (h0_data) {
prev_h_data = h0_data + bid * D;
prev_c_data = c0_data + bid * D;
} else {
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, xx_data + D, xx_data + D);
// If step == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros. Then W_h * H_t-1 can be skipped
// ~C_t
act_cand(D, xx_data, xx_data);
// cell out= input*tilde
if (use_peepholes) {
// I_t, F_t
act_gate(D2, xx_data + D, xx_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, xx_data + D, xx_data + D);
}
// C_t = I_t * ~C_t
blas.VMUL(D, xx_data, xx_data + D, cell_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cell_out_data, checked_cell_data + D2);
blas.VADD(D, xx_data + D3, checked_cell_data + D2, xx_data + D3);
// O_t
act_gate(D, xx_data + D3, xx_data + D3);
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_h_data = hidden_out_data;
prev_c_data = cell_out_data;
tstart = 1;
tstart = 1;
move_step();
}
for (int step = tstart; step < seq_len; ++step) {
// + W_h * H_t-1
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
prev_h_data, D, wh_data, D4, static_cast<T>(1), xx_data, D4);
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, xx_data + D, xx_data + D);
// ~C_t
act_cand(D, xx_data, xx_data);
// a = forget * prev_cell
if (use_peepholes) {
// + W_ic|W_fc * C_t-1 for peephole connection
blas.VMUL(D, wc_data, prev_c_data, checked_cell_data);
blas.VMUL(D, wc_data + D, prev_c_data, checked_cell_data + D);
blas.VADD(D2, xx_data + D, checked_cell_data, xx_data + D);
// I_t, F_t
act_gate(D2, xx_data + D, xx_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, xx_data + D, xx_data + D);
}
// F_t * C_t-1
blas.VMUL(D, xx_data + D2, prev_c_data, xx_data + D2);
// b = input * tilde
// I_t * ~C_t
blas.VMUL(D, xx_data, xx_data + D, xx_data + D);
// cell out= a+b
// C_t = F_t * C_t-1 + I_t * ~C_t
blas.VADD(D, xx_data + D, xx_data + D2, cell_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cell_out_data, checked_cell_data + D2);
blas.VADD(D, xx_data + D3, checked_cell_data + D2, xx_data + D3);
// O_t
act_gate(D, xx_data + D3, xx_data + D3);
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
......@@ -344,14 +394,14 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
prev_c_data = cell_out_data;
move_step();
}
}
} // for each step in batch
} // for each batch
}
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = platform::CPUDeviceContext;
INIT_BASE_INPUT_OUTPUT
if (x->lod()[0].size() == 2) {
if (x->lod()[0].size() == 2) { // batch size == 1
SeqCompute(ctx);
return;
}
......@@ -367,6 +417,8 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
const T* bias_data = bias->data<T>();
const T* wc_data = bias_data + D4; // w_ic, w_fc, w_oc
auto place = ctx.GetPlace();
T* xx_data = xx->mutable_data<T>(place);
T* batched_input_data = batched_input->mutable_data<T>(place);
......@@ -375,6 +427,12 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
hidden_out->mutable_data<T>(place);
cell_out->mutable_data<T>(place);
// use local variable
framework::DDim check_dims({3, D});
Tensor checked_cell; // w_ic * Ct-1, w_fc * Ct-1, w_oc * Ct
auto checked_cell_data =
checked_cell.mutable_data<T>(check_dims, ctx.GetPlace());
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
......@@ -396,17 +454,27 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
reordered_h0->Resize({max_bs, D});
reordered_c0->Resize({max_bs, D});
T* prev_batch_h_data = nullptr;
T* prev_batch_c_data = nullptr;
T* cur_batch_in_data = batched_input_data;
T* cur_batch_h_out_data = batched_h_out_data;
T* cur_batch_c_out_data = batched_c_out_data;
auto move_step = [&](int bs) {
cur_batch_in_data += bs * D4;
cur_batch_c_out_data += bs * D;
cur_batch_h_out_data += bs * D;
};
int tstart = 0;
T* prev_h_data = nullptr;
T* prev_c_data = nullptr;
if (h0) {
// reorder h0, c0
T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
T* reordered_c0_data = reordered_c0->mutable_data<T>(place);
const T* h0_data = h0->data<T>();
const T* c0_data = c0->data<T>();
prev_h_data = reordered_h0_data;
prev_c_data = reordered_c0_data;
prev_batch_h_data = reordered_h0_data;
prev_batch_c_data = reordered_c0_data;
size_t sz = sizeof(T) * D;
for (int i = 0; i < max_bs; ++i) {
std::memcpy(reordered_h0_data, h0_data + seq_order[i] * D, sz);
......@@ -415,71 +483,122 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
reordered_c0_data += D;
}
} else {
// compute without h0, c0
T* cur_in_data = batched_input_data;
T* cur_h_out_data = batched_h_out_data;
T* cur_c_out_data = batched_c_out_data;
// W_ch, W_ih, W_fh, W_oh
for (int i = 0; i < max_bs; ++i) {
act_gate(D3, cur_in_data + D, cur_in_data + D);
// Compute with no H0/C0
T* cur_in_data = cur_batch_in_data;
T* cur_c_out_data = cur_batch_c_out_data;
T* cur_h_out_data = cur_batch_h_out_data;
// If step == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros. Then W_h * H_t-1 can be skiped
for (int i = 0; i < max_bs; ++i) { // iterate each data in 1st batch
// ~C_t
act_cand(D, cur_in_data, cur_in_data);
// cell out= input*tilde
if (use_peepholes) {
// I_t, F_t
act_gate(D2, cur_in_data + D, cur_in_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, cur_in_data + D, cur_in_data + D);
}
// C_t = I_t * ~C_t
blas.VMUL(D, cur_in_data, cur_in_data + D, cur_c_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cur_c_out_data, checked_cell_data + D2);
blas.VADD(D, cur_in_data + D3, checked_cell_data + D2,
cur_in_data + D3);
// O_t
act_gate(D, cur_in_data + D3, cur_in_data + D3);
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cur_c_out_data, cur_in_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
// add offset
// move to next data in the same batch
cur_in_data += D4;
cur_c_out_data += D;
cur_h_out_data += D;
}
// move to data for next timestep
prev_batch_h_data = cur_batch_h_out_data;
prev_batch_c_data = cur_batch_c_out_data;
move_step(max_bs);
tstart = 1;
prev_h_data = batched_h_out_data;
prev_c_data = batched_c_out_data;
}
// Then start from next
const auto& batch_starts = batched_lod[0];
const int max_seq_len = batch_starts.size() - 1;
const int offset = tstart * max_bs * D;
batched_input_data = batched_input_data + offset * 4;
batched_h_out_data = batched_h_out_data + offset;
batched_c_out_data = batched_c_out_data + offset;
for (int step = tstart; step < max_seq_len; ++step) {
const int cur_bs = batch_starts[step + 1] - batch_starts[step];
// + W_h * H_t-1
blas.GEMM(CblasNoTrans, CblasNoTrans, cur_bs, D4, D, static_cast<T>(1),
prev_h_data, D, wh_data, D4, static_cast<T>(1),
batched_input_data, D4);
T* cur_in_data = batched_input_data;
T* cur_prev_c_data = prev_c_data;
T* cur_c_out_data = batched_c_out_data;
T* cur_h_out_data = batched_h_out_data;
for (int i = 0; i < cur_bs; ++i) {
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, cur_in_data + D, cur_in_data + D);
prev_batch_h_data, D, wh_data, D4, static_cast<T>(1),
cur_batch_in_data, D4);
T* cur_in_data = cur_batch_in_data;
T* cur_c_out_data = cur_batch_c_out_data;
T* cur_h_out_data = cur_batch_h_out_data;
T* prev_c_data = prev_batch_c_data; // NULL if no C0 in step0
T* prev_h_data = prev_batch_h_data; // NULL if no H0 in step0
auto next_data_in_batch = [&]() {
cur_in_data += D4;
cur_c_out_data += D;
cur_h_out_data += D;
prev_c_data = prev_c_data ? prev_c_data + D : nullptr;
prev_h_data = prev_h_data ? prev_h_data + D : nullptr;
};
for (int i = 0; i < cur_bs; ++i) { // iterate each data in same batch
// ~C_t
act_cand(D, cur_in_data, cur_in_data);
// a = forget * prev_cell
blas.VMUL(D, cur_in_data + D2, cur_prev_c_data, cur_in_data + D2);
// b = input * tilde
if (use_peepholes) {
// + W_ic|W_fc * C_t-1 for peephole connection
blas.VMUL(D, wc_data, prev_c_data, checked_cell_data);
blas.VMUL(D, wc_data + D, prev_c_data, checked_cell_data + D);
blas.VADD(D2, cur_in_data + D, checked_cell_data, cur_in_data + D);
// I_t, F_t
act_gate(D2, cur_in_data + D, cur_in_data + D);
} else {
// I_t, F_t, O_t
act_gate(D3, cur_in_data + D, cur_in_data + D);
}
// F_t * C_t-1
blas.VMUL(D, cur_in_data + D2, prev_c_data, cur_in_data + D2);
// I_t * ~C_t
blas.VMUL(D, cur_in_data, cur_in_data + D, cur_in_data + D);
// cell out= a+b
// C_t = F_t * C_t-1 + I_t * ~C_t
blas.VADD(D, cur_in_data + D, cur_in_data + D2, cur_c_out_data);
if (use_peepholes) {
// + W_oc * C_t for peephole connection
blas.VMUL(D, wc_data + D2, cur_c_out_data, checked_cell_data + D2);
blas.VADD(D, cur_in_data + D3, checked_cell_data + D2,
cur_in_data + D3);
// O_t
act_gate(D, cur_in_data + D3, cur_in_data + D3);
}
// hidden out= act_state(cellout) * outgate
act_cell(D, cur_c_out_data, cur_in_data + D2);
// H_t = O_t * act_state(C_t)
blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
cur_in_data += D4;
cur_prev_c_data += D;
cur_c_out_data += D;
cur_h_out_data += D;
// move to next data in same batch
next_data_in_batch();
}
prev_c_data = batched_c_out_data;
prev_h_data = batched_h_out_data;
batched_c_out_data = cur_c_out_data;
batched_h_out_data = cur_h_out_data;
batched_input_data = cur_in_data;
// move to data for next timestep
prev_batch_h_data = cur_batch_h_out_data;
prev_batch_c_data = cur_batch_c_out_data;
move_step(cur_bs);
}
math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
......
paddle/fluid/operators/gru_unit_op.h
浏览文件 @ a121c898
......@@ -92,12 +92,12 @@ class GRUUnitKernel : public framework::OpKernel<T> {
gate_data, frame_size * 3);
// calculate activited gate
Eigen::array<int, 2> extents = {batch_size, frame_size};
Eigen::array<int, 2> u_offsets = {0, 0};
Eigen::array<int, 2> extents{{batch_size, frame_size}};
Eigen::array<int, 2> u_offsets{{0, 0}};
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(u_offsets, extents), g.slice(u_offsets, extents));
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets = {0, frame_size};
Eigen::array<int, 2> r_offsets{{0, frame_size}};
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(r_offsets, extents), g.slice(r_offsets, extents));
auto r = g.slice(r_offsets, extents); // reset gate
......@@ -107,7 +107,7 @@ class GRUUnitKernel : public framework::OpKernel<T> {
weight_data + frame_size * frame_size * 2, frame_size, 1,
gate_data + frame_size * 2, frame_size * 3);
Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
ActCompute(context.Attr<int>("activation"), place,
g.slice(c_offsets, extents), g.slice(c_offsets, extents));
auto c = g.slice(c_offsets, extents); // output candidate
......@@ -171,12 +171,12 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
Eigen::array<int, 2> extents = {batch_size, frame_size};
Eigen::array<int, 2> u_offsets = {0, 0};
Eigen::array<int, 2> extents{{batch_size, frame_size}};
Eigen::array<int, 2> u_offsets{{0, 0}};
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets = {0, frame_size};
Eigen::array<int, 2> r_offsets{{0, frame_size}};
auto r = g.slice(r_offsets, extents); // reset gate
Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
auto c = g.slice(c_offsets, extents); // output candidate
// backward for unactivated update gate
......
paddle/fluid/operators/layer_norm_op.cu
浏览文件 @ a121c898
......@@ -67,27 +67,27 @@ template <typename T, int BlockDim>
__global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
T *y, T *mean, T *var, float epsilon,
int feature_size) {
using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
using BlockReduce = cub::BlockReduce<PairForLayerNorm<double>, BlockDim>;
__shared__ typename BlockReduce::TempStorage temp_storage;
int beg_idx = blockIdx.x * feature_size + threadIdx.x;
int end_idx = (blockIdx.x + 1) * feature_size;
// Step 1: Reduce to calculate mean and var
T mean_val = static_cast<T>(0);
T var_val = static_cast<T>(0);
double mean_val = 0;
double var_val = 0;
for (int i = beg_idx; i < end_idx; i += BlockDim) {
T tmp = x[i];
mean_val += tmp;
var_val += (tmp * tmp);
}
auto pair = BlockReduce(temp_storage)
.Reduce(PairForLayerNorm<T>(mean_val, var_val),
PairForLayerNormAddFunctor<T>());
.Reduce(PairForLayerNorm<double>(mean_val, var_val),
PairForLayerNormAddFunctor<double>());
if (threadIdx.x == 0) {
auto tmp = pair.first_ / feature_size;
mean[blockIdx.x] = tmp;
var[blockIdx.x] = pair.second_ / feature_size - tmp * tmp;
mean[blockIdx.x] = static_cast<T>(tmp);
var[blockIdx.x] = static_cast<T>(pair.second_ / feature_size - tmp * tmp);
}
__syncthreads();
mean_val = mean[blockIdx.x];
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ a121c898
......@@ -57,7 +57,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
memset(output + i * row_width, 0, row_width * sizeof(T));
} else {
PADDLE_ENFORCE_LT(ids[i], row_number);
PADDLE_ENFORCE_GE(ids[i], 0);
PADDLE_ENFORCE_GE(ids[i], 0, "ids %d", i);
memcpy(output + i * row_width, table + ids[i] * row_width,
row_width * sizeof(T));
}
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ a121c898
......@@ -246,6 +246,88 @@ class ReshapeGradKernel {
}
};
// FIXME(zcd): reshape2 adds an intermediate output(XShape) based on reshape,
// the XShape is used to carry the shape and lod of X which will be used in
// reshape_grad, in this way, the framework can reuse the memory of X
// immediately the reshape_op is finished.
// Considering compatibility issues, we could not fix reshape_op
class Reshape2Op : public ReshapeOp {
public:
Reshape2Op(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ReshapeOp(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
ReshapeOp::InferShape(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) of ReshapeOp should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
};
class Reshape2OpMaker : public ReshapeOpMaker {
public:
void Make() override {
ReshapeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in FlattenGradOp.")
.AsIntermediate();
}
};
class Reshape2GradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("reshape2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Reshape2GradOp : public framework::OperatorWithKernel {
public:
Reshape2GradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = ctx->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD("XShape", framework::GradVarName("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
->type()),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
......@@ -261,6 +343,17 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
ops::Reshape2GradMaker);
REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
......@@ -269,4 +362,11 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
#endif
paddle/fluid/operators/rmsprop_op.cc
浏览文件 @ a121c898
......@@ -36,9 +36,13 @@ class RmspropOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(param_out) of RmspropOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
"Output(Momentum_out) of RmspropOp should not be null.");
"Output(MomentOut) of RmspropOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MeanSquareOut"),
"Output(MeanSquareOut) of RmspropOp should not be null.");
if (ctx->Attrs().Get<bool>("centered")) {
PADDLE_ENFORCE(ctx->HasOutput("MeanGradOut"),
"Output(MeanGradOut) of RmspropOp should not be null.");
}
auto param_dim = ctx->GetInputDim("Param");
PADDLE_ENFORCE_EQ(
......@@ -58,6 +62,9 @@ class RmspropOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dim);
ctx->SetOutputDim("MomentOut", param_dim);
ctx->SetOutputDim("MeanSquareOut", param_dim);
if (ctx->Attrs().Get<bool>("centered")) {
ctx->SetOutputDim("MeanGradOut", param_dim);
}
}
};
......@@ -70,6 +77,10 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("MeanSquare",
"(Tensor, default Tensor<float>)"
" The mean square value that gets updated.");
AddInput("MeanGrad",
"(Tensor, default Tensor<float>)"
" The moving average of gradient")
.AsDispensable();
AddInput("LearningRate",
"(Tensor, default Tensor<float>) "
"The learning rate should be a tensor of size 1.");
......@@ -82,6 +93,8 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("ParamOut", "(Tensor) Output updated parameter value.");
AddOutput("MomentOut", "(Tensor) Output updated moment.");
AddOutput("MeanSquareOut", "(Tensor) Output Mean squared updated value.");
AddOutput("MeanGradOut",
"(Tensor) Output moving average of gradient updated value.");
AddAttr<float>("epsilon",
"(float, default 1e-10) Constant "
......@@ -93,6 +106,8 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(0.9f);
AddAttr<float>("momentum", "(float, default 0.0) Constant value.")
.SetDefault(0.0f);
AddAttr<bool>("centered", "(bool, default false) use centered rmsprop.")
.SetDefault(false);
AddComment(R"DOC(
Rmsprop Optimizer.
......@@ -103,6 +118,14 @@ MomentOut = momentum * Moment +
ParamOut = Param - MomentOut
$$
if centered is true:
mean_grad = decay * mean_square{t-1} + (1-decay) * gradient
mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
mom = momentum * mom{t-1} + learning_rate * g_t /
sqrt(mean_square - mean_grad**2 + epsilon)
param -= mom
The original slides that proposed Rmsprop: Slide 29 of
http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
......
paddle/fluid/operators/rmsprop_op.h
浏览文件 @ a121c898
......@@ -41,6 +41,7 @@ class RmspropOpKernel : public framework::OpKernel<T> {
float epsilon = ctx.Attr<float>("epsilon");
float rho = ctx.Attr<float>("decay");
float momentum = ctx.Attr<float>("momentum");
bool centered = ctx.Attr<bool>("centered");
auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
auto ms = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanSquare"));
......@@ -53,12 +54,24 @@ class RmspropOpKernel : public framework::OpKernel<T> {
auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
Eigen::DSizes<int, 1> grad_dsize(grad->numel());
Eigen::DSizes<int, 1> grad_dsize(static_cast<int>(grad->numel()));
ms_out.device(place) = rho * ms + (1 - rho) * g * g;
mom_out.device(place) =
momentum * mom +
lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
if (centered) {
auto mg = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanGrad"));
auto* mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
mean_grad_out->mutable_data<T>(ctx.GetPlace());
auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
mg_out.device(place) = rho * mg + (1 - rho) * g;
mom_out.device(place) = momentum * mom +
lr.broadcast(grad_dsize) * g /
(ms_out - mg_out.square() + epsilon).sqrt();
} else {
mom_out.device(place) =
momentum * mom +
lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
}
p_out.device(place) = p - mom_out;
}
};
......
paddle/fluid/operators/squeeze_op.cc
浏览文件 @ a121c898
......@@ -126,15 +126,15 @@ class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault({});
AddComment(R"DOC(
Squeeze Operator.
Remove single-dimensional entries from the shape of a tensor.
Takes a parameter axes with a list of axes to squeeze.
If axes is not provided, all the single dimensions will be removed from the shape.
Remove single-dimensional entries from the shape of a tensor.
Takes a parameter axes with a list of axes to squeeze.
If axes is not provided, all the single dimensions will be removed from the shape.
If an axis is selected with shape entry not equal to one, an error is raised.
Examples:
Case 1:
Given
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
......@@ -144,7 +144,7 @@ class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
and
axes = []
we get:
Out.shape = (3, 5)
......@@ -181,6 +181,113 @@ class SqueezeGradOp : public framework::OperatorBase {
}
};
// FIXME(zcd): squeeze2 adds an intermediate output(XShape) based on squeeze,
// the XShape is used to carry the shape and lod of X which will be used in
// squeeze_grad, in this way, the framework can reuse the memory of X
// immediately the squeeze2_op is finished.
// Considering compatibility issues, we could not fix squeeze2_op
class Squeeze2OpMaker : public SqueezeOpMaker {
public:
void Make() override {
SqueezeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in SqueezeGradOp.")
.AsIntermediate();
}
};
class Squeeze2OpInferShape : public SqueezeOpInferShape {
public:
void operator()(framework::InferShapeContext *ctx) const override {
SqueezeOpInferShape::operator()(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) of Squeeze operator should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
};
class Squeeze2Op : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
// Invoke Reshape Op
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class Squeeze2GradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("squeeze2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Squeeze2GradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("XShape"),
"Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = context->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
context->SetOutputDim(framework::GradVarName("X"), x_dims);
context->ShareLoD("XShape", framework::GradVarName("X"));
}
};
class Squeeze2GradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto xshape_name = Input("XShape");
auto xshape_dims =
scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {dout_name}}, {"Shape", {}}},
{{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
......@@ -192,3 +299,8 @@ REGISTER_OPERATOR(squeeze, ops::SqueezeOp, ops::SqueezeOpMaker,
ops::SqueezeOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(squeeze_grad, ops::SqueezeGradOp, ops::SqueezeGradInferShape);
REGISTER_OPERATOR(squeeze2, ops::Squeeze2Op, ops::Squeeze2OpMaker,
ops::Squeeze2OpInferShape, ops::Squeeze2GradOpMaker);
REGISTER_OPERATOR(squeeze2_grad, ops::Squeeze2GradOp,
ops::Squeeze2GradInferShape);
paddle/fluid/operators/transpose_op.cc
浏览文件 @ a121c898
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/transpose_op.h"
#include <string>
#include <vector>
namespace paddle {
......@@ -24,7 +25,7 @@ class TransposeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
auto x_dims = ctx->GetInputDim("X");
......@@ -90,7 +91,7 @@ The behavior of this operator is similar to how `numpy.transpose` works.
2 &5
\end{pmatrix}$$
- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
$[0, 2, 3, 1]$, then shape of the output tensor will be: $(N, H, W, C)$.
)DOC");
......@@ -101,7 +102,7 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
......@@ -113,6 +114,93 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
}
};
// FIXME(zcd): transpose2 adds an intermediate output(XShape) based on
// transpose, the XShape is used to carry the shape and lod of X which
// will be used in transpose_grad, in this way, the framework can reuse
// the memory of X immediately the transpose2_op is finished.
// Considering compatibility issues, we could not fix transpose2_op
class Transpose2Op : public TransposeOp {
public:
Transpose2Op(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: TransposeOp(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
TransposeOp::InferShape(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) should not be null");
const auto &in_dims = ctx->GetInputDim("X");
std::vector<int64_t> x_shape_dim(in_dims.size() + 1);
x_shape_dim[0] = 0;
for (int i = 0; i < in_dims.size(); ++i) {
x_shape_dim[i + 1] = in_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(x_shape_dim));
ctx->ShareLoD("X", /*->*/ "XShape");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class Transpose2OpMaker : public TransposeOpMaker {
public:
void Make() override {
TransposeOpMaker::Make();
AddOutput("XShape", "(Tensor)The output tensor.").AsIntermediate();
}
};
class Transpose2GradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("transpose2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Transpose2OpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
if (ctx->HasOutput(framework::GradVarName("X"))) {
auto xshape_dim = ctx->GetInputDim("XShape");
auto x_shape_dim =
framework::slice_ddim(xshape_dim, 1, xshape_dim.size());
ctx->SetOutputDim(framework::GradVarName("X"), x_shape_dim);
ctx->ShareLoD("XShape", framework::GradVarName("X"));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
->type()),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
......@@ -120,8 +208,20 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
ops::Transpose2GradMaker);
REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
REGISTER_OP_CPU_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/fluid/operators/transpose_op.cu.cc
浏览文件 @ a121c898
......@@ -21,3 +21,10 @@ REGISTER_OP_CUDA_KERNEL(
REGISTER_OP_CUDA_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/fluid/operators/unsqueeze_op.cc
浏览文件 @ a121c898
......@@ -127,13 +127,13 @@ class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddComment(R"DOC(
Unsqueeze Operator.
Insert single-dimensional entries to the shape of a tensor.
Takes one required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
For example:
Given a tensor such that tensor with shape [3, 4, 5],
Insert single-dimensional entries to the shape of a tensor.
Takes one required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
For example:
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
)DOC");
}
......@@ -168,6 +168,112 @@ class UnsqueezeGradOp : public framework::OperatorBase {
}
};
// FIXME(zcd): unsqueeze2 adds an intermediate output(XShape) based on
// unsqueeze, the XShape is used to carry the shape and lod of X which
// will be used in unsqueeze_grad, in this way, the framework can reuse
// the memory of X immediately the unsqueeze2_op is finished.
// Considering compatibility issues, we could not fix unsqueeze2_op
class Unsqueeze2OpInferShape : public UnsqueezeOpInferShape {
public:
void operator()(framework::InferShapeContext *ctx) const override {
UnsqueezeOpInferShape::operator()(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) of Unsqueeze operator should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
};
class Unsqueeze2OpMaker : public UnsqueezeOpMaker {
public:
void Make() override {
UnsqueezeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in UnsqueezeGradOp.")
.AsIntermediate();
}
};
class Unsqueeze2Op : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = Unsqueeze2OpInferShape::GetOutputShape(axes, x_dims);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
// Invoke Reshape op.
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class Unsqueeze2GradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("unsqueeze2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Unsqueeze2GradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("XShape"),
"Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = context->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
context->SetOutputDim(framework::GradVarName("X"), x_dims);
context->ShareLoD("XShape", framework::GradVarName("X"));
}
};
class Unsqueeze2GradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto xshape_name = Input("XShape");
auto xshape_dims =
scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {dout_name}}, {"Shape", {}}},
{{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
......@@ -180,3 +286,8 @@ REGISTER_OPERATOR(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp,
ops::UnsqueezeGradInferShape);
REGISTER_OPERATOR(unsqueeze2, ops::Unsqueeze2Op, ops::Unsqueeze2OpMaker,
ops::Unsqueeze2OpInferShape, ops::Unsqueeze2GradOpMaker);
REGISTER_OPERATOR(unsqueeze2_grad, ops::Unsqueeze2GradOp,
ops::Unsqueeze2GradInferShape);
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ a121c898
......@@ -121,6 +121,12 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
if (nullptr == dso_handle) {
LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
<< dlerror() << ")";
if (dlPath.find("nccl") != std::string::npos) {
std::cout
<< "You may need to install 'nccl2' from NVIDIA official website: "
<< "https://developer.nvidia.com/nccl/nccl-download"
<< "before install PaddlePaddle" << std::endl;
}
dlPath = dso_name;
dso_handle = GetDsoHandleFromDefaultPath(dlPath, dynload_flags);
}
......
paddle/scripts/paddle_build.sh
浏览文件 @ a121c898
......@@ -115,6 +115,7 @@ function cmake_gen() {
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_CONTRIB=${WITH_CONTRIB:-ON}
-DWITH_INFERENCE=${WITH_INFERENCE:-ON}
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
-DPY_VERSION=${PY_VERSION:-2.7}
========================================
......@@ -144,6 +145,7 @@ EOF
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-DWITH_INFERENCE=${WITH_INFERENCE:-ON} \
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
-DPY_VERSION=${PY_VERSION:-2.7}
}
......@@ -498,7 +500,7 @@ EOF
EOF
if [[ ${WITH_GPU} == "ON" ]]; then
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.1.2-1+cuda${CUDA_MAJOR} libnccl-dev=2.1.2-1+cuda${CUDA_MAJOR} &&"
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} &&"
else
NCCL_DEPS=""
fi
......
python/paddle/dataset/image.py
浏览文件 @ a121c898
......@@ -104,7 +104,7 @@ def batch_images_from_tar(data_file,
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
protocol=2)
file_id += 1
data = []
labels = []
......@@ -113,9 +113,7 @@ def batch_images_from_tar(data_file,
output['label'] = labels
output['data'] = data
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
output, open('%s/batch_%d' % (out_path, file_id), 'wb'), protocol=2)
with open(meta_file, 'a') as meta:
for file in os.listdir(out_path):
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ a121c898
......@@ -78,7 +78,7 @@ def accuracy(input, label, k=1, correct=None, total=None):
return acc_out
def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
"""
**Area Under the Curve (AUC) Layer**
......@@ -118,16 +118,14 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
"""
helper = LayerHelper("auc", **locals())
auc_out = helper.create_tmp_variable(dtype="float64")
batch_auc_out = helper.create_tmp_variable(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
tp = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
tn = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
fp = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
fn = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
for var in [tp, tn, fp, fn]:
stat_pos = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds + 1])
stat_neg = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds + 1])
for var in [stat_pos, stat_neg]:
helper.set_variable_initializer(
var, Constant(
value=0.0, force_cpu=True))
......@@ -137,18 +135,15 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
inputs={
"Predict": [input],
"Label": [label],
"TP": [tp],
"TN": [tn],
"FP": [fp],
"FN": [fn]
"StatPos": [stat_pos],
"StatNeg": [stat_neg]
},
attrs={"curve": curve,
"num_thresholds": num_thresholds},
outputs={
"AUC": [auc_out],
"TPOut": [tp],
"TNOut": [tn],
"FPOut": [fp],
"FNOut": [fn]
"BatchAUC": [batch_auc_out],
"StatPosOut": [stat_pos],
"StatNegOut": [stat_neg]
})
return auc_out, [tp, tn, fp, fn]
return auc_out, batch_auc_out, [stat_pos, stat_neg]
python/paddle/fluid/layers/nn.py
浏览文件 @ a121c898
......@@ -3546,11 +3546,6 @@ def topk(input, k, name=None):
top5_values, top5_indices = layers.topk(input, k=5)
"""
shape = input.shape
if k < 1 or k >= shape[-1]:
raise ValueError("k must be greater than 0 and less than %d." %
(shape[-1]))
helper = LayerHelper("top_k", **locals())
values = helper.create_tmp_variable(dtype=input.dtype)
indices = helper.create_tmp_variable(dtype="int64")
......@@ -4030,10 +4025,12 @@ def transpose(x, perm, name=None):
helper = LayerHelper('transpose', **locals())
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='transpose',
type='transpose2',
inputs={'X': [x]},
outputs={'Out': [out]},
outputs={'Out': [out],
'XShape': [x_shape]},
attrs={'axis': perm})
return out
......@@ -4525,13 +4522,15 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"Each dimension size given in shape must not be negtive "
"except one unknown dimension.")
helper = LayerHelper("reshape", **locals())
helper = LayerHelper("reshape2", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
x_shape = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reshape",
type="reshape2",
inputs=inputs,
attrs={"shape": shape},
outputs={"Out": out})
outputs={"Out": out,
"XShape": x_shape})
return helper.append_activation(out)
......@@ -4575,11 +4574,13 @@ def squeeze(input, axes, name=None):
"""
helper = LayerHelper("squeeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="squeeze",
type="squeeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out})
outputs={"Out": out,
"XShape": x_shape})
return out
......@@ -4610,11 +4611,13 @@ def unsqueeze(input, axes, name=None):
"""
helper = LayerHelper("unsqueeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="unsqueeze",
type="unsqueeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out})
outputs={"Out": out,
"XShape": x_shape})
return out
......@@ -5816,10 +5819,12 @@ def flatten(x, axis=1, name=None):
raise ValueError("The axis should be a int, and in range [0, rank(x)]")
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='flatten',
type='flatten2',
inputs={"X": x},
outputs={'Out': out},
outputs={'Out': out,
'XShape': x_shape},
attrs={"axis": axis})
return out
......
python/paddle/fluid/metrics.py
浏览文件 @ a121c898
......@@ -558,8 +558,6 @@ class Auc(MetricBase):
name: metric name
curve: Specifies the name of the curve to be computed, 'ROC' [default] or
'PR' for the Precision-Recall-curve.
num_thresholds: The number of thresholds to use when discretizing the roc
curve.
"NOTE: only implement the ROC curve type via Python now."
......@@ -574,15 +572,14 @@ class Auc(MetricBase):
numpy_auc = metric.eval()
"""
def __init__(self, name, curve='ROC', num_thresholds=200):
def __init__(self, name, curve='ROC', num_thresholds=4095):
super(Auc, self).__init__(name=name)
self._curve = curve
self._num_thresholds = num_thresholds
self._epsilon = 1e-6
self.tp_list = np.zeros((num_thresholds, ))
self.fn_list = np.zeros((num_thresholds, ))
self.tn_list = np.zeros((num_thresholds, ))
self.fp_list = np.zeros((num_thresholds, ))
_num_pred_buckets = num_thresholds + 1
self._stat_pos = [0] * _num_pred_buckets
self._stat_neg = [0] * _num_pred_buckets
def update(self, preds, labels):
if not _is_numpy_(labels):
......@@ -590,41 +587,32 @@ class Auc(MetricBase):
if not _is_numpy_(preds):
raise ValueError("The 'predictions' must be a numpy ndarray.")
kepsilon = 1e-7 # to account for floating point imprecisions
thresholds = [(i + 1) * 1.0 / (self._num_thresholds - 1)
for i in range(self._num_thresholds - 2)]
thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
# calculate TP, FN, TN, FP count
for idx_thresh, thresh in enumerate(thresholds):
tp, fn, tn, fp = 0, 0, 0, 0
for i, lbl in enumerate(labels):
if lbl:
if preds[i, 1] >= thresh:
tp += 1
else:
fn += 1
else:
if preds[i, 1] >= thresh:
fp += 1
else:
tn += 1
self.tp_list[idx_thresh] += tp
self.fn_list[idx_thresh] += fn
self.tn_list[idx_thresh] += tn
self.fp_list[idx_thresh] += fp
for i, lbl in enumerate(labels):
value = preds[i, 1]
bin_idx = int(value * self._num_thresholds)
assert bin_idx <= self._num_thresholds
if lbl:
self._stat_pos[bin_idx] += 1.0
else:
self._stat_neg[bin_idx] += 1.0
@staticmethod
def trapezoid_area(x1, x2, y1, y2):
return abs(x1 - x2) * (y1 + y2) / 2.0
def eval(self):
epsilon = self._epsilon
num_thresholds = self._num_thresholds
tpr = (self.tp_list.astype("float32") + epsilon) / (
self.tp_list + self.fn_list + epsilon)
fpr = self.fp_list.astype("float32") / (
self.fp_list + self.tn_list + epsilon)
rec = (self.tp_list.astype("float32") + epsilon) / (
self.tp_list + self.fp_list + epsilon)
x = fpr[:num_thresholds - 1] - fpr[1:]
y = (tpr[:num_thresholds - 1] + tpr[1:]) / 2.0
auc_value = np.sum(x * y)
return auc_value
tot_pos = 0.0
tot_neg = 0.0
auc = 0.0
idx = self._num_thresholds
while idx >= 0:
tot_pos_prev = tot_pos
tot_neg_prev = tot_neg
tot_pos += self._stat_pos[idx]
tot_neg += self._stat_neg[idx]
auc += self.trapezoid_area(tot_neg, tot_neg_prev, tot_pos,
tot_pos_prev)
idx -= 1
return auc / tot_pos / tot_neg if tot_pos > 0.0 and tot_neg > 0.0 else 0.0
python/paddle/fluid/optimizer.py
浏览文件 @ a121c898
......@@ -897,7 +897,20 @@ class RMSPropOptimizer(Optimizer):
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{v(w,t) +
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) +
\\epsilon}} \\nabla Q_{i}(w)
w & = w - v(w, t)
if centered is True:
.. math::
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
g(w, t) & = \\rho g(w, t-1) + (1 - \\rho)\\nabla Q_{i}(w)
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) - (g(w, t))^2 +
\\epsilon}} \\nabla Q_{i}(w)
w & = w - v(w, t)
......@@ -915,6 +928,10 @@ class RMSPropOptimizer(Optimizer):
avoid division by zero, set 1e-6 by default.
momentum(float): :math:`\\beta` in equation is the momentum term,
set 0.0 by default.
centered(bool): If True, gradients are normalized by the estimated variance of
the gradient; if False, by the uncentered second moment. Setting this to
True may help with training, but is slightly more expensive in terms of
computation and memory. Defaults to False.
Raises:
ValueError: If learning_rate, rho, epsilon, momentum are None.
......@@ -928,12 +945,14 @@ class RMSPropOptimizer(Optimizer):
_momentum_acc_str = "momentum"
_mean_square_acc_str = "mean_square"
_mean_grad_acc_str = "mean_grad"
def __init__(self,
learning_rate,
rho=0.95,
epsilon=1.0e-6,
momentum=0.0,
centered=False,
**kwargs):
super(RMSPropOptimizer, self).__init__(
learning_rate=learning_rate, **kwargs)
......@@ -950,6 +969,7 @@ class RMSPropOptimizer(Optimizer):
self._rho = rho
self._epsilon = epsilon
self._momentum = momentum
self._centered = centered
def _create_accumulators(self, block, parameters):
if not isinstance(block, framework.Block):
......@@ -958,6 +978,7 @@ class RMSPropOptimizer(Optimizer):
for p in parameters:
self._add_accumulator(self._momentum_acc_str, p)
self._add_accumulator(self._mean_square_acc_str, p)
self._add_accumulator(self._mean_grad_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
if not isinstance(block, framework.Block):
......@@ -967,6 +988,8 @@ class RMSPropOptimizer(Optimizer):
param_and_grad[0])
mean_square_acc = self._get_accumulator(self._mean_square_acc_str,
param_and_grad[0])
mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str,
param_and_grad[0])
rmsprop_op = block.append_op(
type=self.type,
inputs={
......@@ -974,17 +997,20 @@ class RMSPropOptimizer(Optimizer):
"Grad": param_and_grad[1],
"Moment": momentum_acc,
"MeanSquare": mean_square_acc,
"MeanGrad": mean_grad_acc,
"LearningRate": self._create_param_lr(param_and_grad),
},
outputs={
"ParamOut": param_and_grad[0],
"MomentOut": momentum_acc,
"MeanSquareOut": mean_square_acc
"MeanSquareOut": mean_square_acc,
"MeanGradOut": mean_grad_acc
},
attrs={
"epsilon": self._epsilon,
"decay": self._rho,
"momentum": self._momentum
"momentum": self._momentum,
"centered": self._centered
})
return rmsprop_op
......
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
浏览文件 @ a121c898
......@@ -47,14 +47,14 @@ def train_program():
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
return avg_loss
return [avg_loss, y_predict]
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname):
def train(use_cuda, train_program, params_dirname, inference_model_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
......@@ -74,6 +74,8 @@ def train(use_cuda, train_program, params_dirname):
'''
if params_dirname is not None:
trainer.save_params(params_dirname)
trainer.save_inference_model(inference_model_dirname,
['x'], [1])
trainer.stop()
trainer.train(
......@@ -99,15 +101,55 @@ def infer(use_cuda, inference_program, params_dirname=None):
print("infer results: ", results[0])
def infer_by_saved_model(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
test_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=batch_size)
test_data = next(test_reader())
test_feat = numpy.array(
[data[0] for data in test_data]).astype("float32")
test_label = numpy.array(
[data[1] for data in test_data]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: numpy.array(test_feat)},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
print("ground truth: ", test_label)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
params_dirname = "fit_a_line.inference.model"
params_dirname = "fit_a_line.model"
inference_model_dirname = "fit_a_line.inference_model"
train(use_cuda, train_program, params_dirname)
train(use_cuda, train_program, params_dirname, inference_model_dirname)
infer(use_cuda, inference_program, params_dirname)
infer_by_saved_model(use_cuda, inference_model_dirname)
class TestFitALine(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ a121c898
......@@ -36,6 +36,7 @@ import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from test_dist_base import TestDistRunnerBase, runtime_main
import paddle.compat as cpt
from paddle.compat import long_type
import hashlib
......@@ -315,8 +316,9 @@ def pad_batch_data(insts,
"""
return_list = []
max_len = max(len(inst) for inst in insts)
num_token = reduce(lambda x, y: x + y,
[len(inst) for inst in insts]) if return_num_token else 0
num_token = six.moves.reduce(
lambda x, y: x + y,
[len(inst) for inst in insts]) if return_num_token else 0
# Any token included in dict can be used to pad, since the paddings' loss
# will be masked out by weights and make no effect on parameter gradients.
inst_data = np.array(
......@@ -328,7 +330,7 @@ def pad_batch_data(insts,
return_list += [inst_weight.astype("float32").reshape([-1, 1])]
else: # position data
inst_pos = np.array([
range(1, len(inst) + 1) + [0] * (max_len - len(inst))
list(range(1, len(inst) + 1)) + [0] * (max_len - len(inst))
for inst in insts
])
return_list += [inst_pos.astype("int64").reshape([-1, 1])]
......@@ -385,10 +387,11 @@ def prepare_batch_input(insts, data_input_names, src_pad_idx, trg_pad_idx,
return_num_token=True)
data_input_dict = dict(
zip(data_input_names, [
src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
]))
list(
zip(data_input_names, [
src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
])))
return data_input_dict, np.asarray([num_token], dtype="float32")
......@@ -561,7 +564,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
np.log(TrainTaskConfig.label_smooth_eps / (
ModelHyperParams.trg_vocab_size - 1) + 1e-20))
init = False
for pass_id in xrange(TrainTaskConfig.pass_num):
for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
if batch_id >= 5:
......@@ -587,11 +590,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
ModelHyperParams.eos_idx, ModelHyperParams.n_head,
ModelHyperParams.d_model)
total_num_token += num_token
feed_kv_pairs = data_input_dict.items()
feed_kv_pairs = list(data_input_dict.items())
if TrainTaskConfig.local:
feed_kv_pairs += {
feed_kv_pairs += list({
lr_scheduler.learning_rate.name: lr_rate
}.items()
}.items())
feed_list.append(dict(feed_kv_pairs))
if not init:
......@@ -873,6 +876,7 @@ class DataReader(object):
f = tarfile.open(fpaths[0], "r")
for line in f.extractfile(tar_fname):
line = cpt.to_text(line)
fields = line.strip("\n").split(self._field_delimiter)
if (not self._only_src and len(fields) == 2) or (
self._only_src and len(fields) == 1):
......@@ -882,8 +886,9 @@ class DataReader(object):
if not os.path.isfile(fpath):
raise IOError("Invalid file: %s" % fpath)
with open(fpath, "r") as f:
with open(fpath, "rb") as f:
for line in f:
line = cpt.to_text(line)
fields = line.strip("\n").split(self._field_delimiter)
if (not self._only_src and len(fields) == 2) or (
self._only_src and len(fields) == 1):
......@@ -892,8 +897,9 @@ class DataReader(object):
@staticmethod
def load_dict(dict_path, reverse=False):
word_dict = {}
with open(dict_path, "r") as fdict:
with open(dict_path, "rb") as fdict:
for idx, line in enumerate(fdict):
line = cpt.to_text(line)
if reverse:
word_dict[idx] = line.strip("\n")
else:
......@@ -1034,7 +1040,7 @@ def multi_head_attention(queries,
# size of the input as the output dimension size.
return layers.reshape(
x=trans_x,
shape=map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]]))
shape=list(map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]])))
def scaled_dot_product_attention(q, k, v, attn_bias, d_model, dropout_rate):
"""
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ a121c898
......@@ -249,7 +249,7 @@ class OpTest(unittest.TestCase):
outs, _ = self._calc_output(place)
return outs
def _calc_output(self, place, parallel=False):
def _calc_output(self, place, parallel=False, no_check_set=None):
program = Program()
block = program.global_block()
......@@ -273,6 +273,8 @@ class OpTest(unittest.TestCase):
# if not, fill the fetch_list by the user configured outputs in test.
if len(fetch_list) == 0:
for var_name, var in six.iteritems(outputs):
if no_check_set is not None and var_name in no_check_set:
continue
if isinstance(var, list):
for v in var:
fetch_list.append(v)
......@@ -291,11 +293,17 @@ class OpTest(unittest.TestCase):
return_numpy=False)
return outs, fetch_list
def check_output_with_place(self, place, atol):
outs, fetch_list = self._calc_output(place)
def check_output_with_place(self,
place,
atol,
no_check_set=None,
equal_nan=False):
outs, fetch_list = self._calc_output(place, no_check_set=no_check_set)
for out_name, out_dup in Operator.get_op_outputs(self.op_type):
if out_name not in self.outputs:
continue
if no_check_set is not None and out_name in no_check_set:
continue
def find_actual(target_name, fetch_list):
found = [
......@@ -321,7 +329,7 @@ class OpTest(unittest.TestCase):
if isinstance(expect, tuple) else expect
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + sub_out_name + ") has diff at " +
str(place))
if isinstance(expect, tuple):
......@@ -337,7 +345,7 @@ class OpTest(unittest.TestCase):
expect_t = expect[0] if isinstance(expect, tuple) else expect
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t))
......@@ -360,10 +368,10 @@ class OpTest(unittest.TestCase):
places.append(core.CUDAPlace(0))
return places
def check_output(self, atol=1e-5):
def check_output(self, atol=1e-5, no_check_set=None, equal_nan=False):
places = self._get_places()
for place in places:
self.check_output_with_place(place, atol)
self.check_output_with_place(place, atol, no_check_set, equal_nan)
def check_output_customized(self, checker):
places = self._get_places()
......
python/paddle/fluid/tests/unittests/test_auc_op.py
浏览文件 @ a121c898
......@@ -26,18 +26,15 @@ class TestAucOp(OpTest):
pred = np.random.random((128, 2)).astype("float32")
labels = np.random.randint(0, 2, (128, 1))
num_thresholds = 200
tp = np.zeros((num_thresholds, )).astype("int64")
tn = np.zeros((num_thresholds, )).astype("int64")
fp = np.zeros((num_thresholds, )).astype("int64")
fn = np.zeros((num_thresholds, )).astype("int64")
stat_pos = np.zeros((num_thresholds + 1, )).astype("int64")
stat_neg = np.zeros((num_thresholds + 1, )).astype("int64")
self.inputs = {
'Predict': pred,
'Label': labels,
'TP': tp,
'TN': tn,
'FP': fp,
'FN': fn
"StatPos": stat_pos,
"StatNeg": stat_neg
}
self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
......@@ -47,11 +44,10 @@ class TestAucOp(OpTest):
python_auc.update(pred, labels)
self.outputs = {
'AUC': python_auc.eval(),
'TPOut': python_auc.tp_list,
'FNOut': python_auc.fn_list,
'TNOut': python_auc.tn_list,
'FPOut': python_auc.fp_list
'AUC': np.array(python_auc.eval()),
'BatchAUC': np.array(python_auc.eval()),
'StatPosOut': np.array(python_auc._stat_pos),
'StatNegOut': np.array(python_auc._stat_neg)
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ a121c898
......@@ -55,6 +55,7 @@ class TestDistRunnerBase(object):
pserver_prog = t.get_pserver_program(args.current_endpoint)
startup_prog = t.get_startup_program(args.current_endpoint,
pserver_prog)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
......@@ -147,6 +148,8 @@ def runtime_main(test_class):
import paddle.compat as cpt
import socket
from contextlib import closing
class TestDistBase(unittest.TestCase):
......@@ -156,13 +159,19 @@ class TestDistBase(unittest.TestCase):
def setUp(self):
self._trainers = 2
self._pservers = 2
self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._sync_mode = True
self._mem_opt = False
self._use_reduce = False
self._setup_config()
def _find_free_port(self):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(('', 0))
return s.getsockname()[1]
def start_pserver(self, model_file, check_error_log):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
......
python/paddle/fluid/tests/unittests/test_flatten_op.py
浏览文件 @ a121c898
......@@ -22,14 +22,17 @@ from op_test import OpTest
class TestFlattenOp(OpTest):
def setUp(self):
self.op_type = "flatten"
self.op_type = "flatten2"
self.init_test_case()
self.inputs = {"X": np.random.random(self.in_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.in_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=["XShape"])
def test_check_grad(self):
self.check_grad(["X"], "Out")
......
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
浏览文件 @ a121c898
......@@ -58,6 +58,7 @@ class TestFusionLSTMOp(OpTest):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.use_seq = False
self.set_conf()
T = sum(self.lod[0])
......@@ -107,6 +108,7 @@ class TestFusionLSTMOp(OpTest):
}
self.attrs = {
'use_peepholes': self.use_peepholes,
'use_seq': self.use_seq,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
......@@ -159,5 +161,68 @@ class TestFusionLSTMOpBS1(TestFusionLSTMOp):
self.D = 16
class TestFusionLSTMOpPeepholes(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
class TestFusionLSTMOpPeepholesInit(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
self.has_initial_state = True
class TestFusionLSTMOpPeepholesReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
self.is_reverse = True
class TestFusionLSTMOpPoopholesBS1(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
self.lod = [[3]]
self.D = 16
class TestFusionLSTMOpSeqInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
class TestFusionLSTMOpSeqReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.is_reverse = True
class TestFusionLSTMOpSeqInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
self.is_reverse = True
class TestFusionLSTMOpSeqPeepholes(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
class TestFusionLSTMOpSeqPeepholesInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.has_initial_state = True
class TestFusionLSTMOpSeqPeepholesReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.is_reverse = True
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
浏览文件 @ a121c898
......@@ -85,6 +85,7 @@ class TestFetchOp(unittest.TestCase):
assert not math.isnan(np.sum(ret[i])) and \
not math.isinf(np.sum(ret[i]))
@unittest.skip(reason="CI timeout")
def test_fetch_op(self):
tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
tst_reader_iter = tst_reader()
......@@ -139,6 +140,7 @@ class TestFeedParallel(unittest.TestCase):
if batch_id == 2:
break
@unittest.skip(reason="CI timeout")
def test_feed_op(self):
os.environ['CPU_NUM'] = str(4)
if core.is_compiled_with_cuda():
......
python/paddle/fluid/tests/unittests/test_prelu_op.py
浏览文件 @ a121c898
......@@ -16,6 +16,7 @@ from __future__ import print_function
import unittest
import numpy as np
import six
from op_test import OpTest
......@@ -62,17 +63,20 @@ class PReluTest(OpTest):
# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
# class TestCase1(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "all"}
if six.PY2:
# class TestCase2(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "channel"}
class TestCase1(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "all"}
class TestCase2(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "channel"}
class TestCase3(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "element"}
# class TestCase3(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "element"}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_reshape_op.py
浏览文件 @ a121c898
......@@ -22,106 +22,39 @@ from op_test import OpTest
class TestReshapeOp(OpTest):
def setUp(self):
ori_shape = (2, 25)
new_shape = (5, 10)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer1(OpTest):
def setUp(self):
ori_shape = (5, 10)
new_shape = (5, -1, 5)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(self.attrs["shape"])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer2(OpTest):
def setUp(self):
ori_shape = (2, 2, 6)
new_shape = (2, 0, 3, -1)
infered_shape = (2, 2, 3, -1)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpInplace(OpTest):
def setUp(self):
ori_shape = (2, 25)
new_shape = (5, 10)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInferInplace1(OpTest):
def setUp(self):
ori_shape = (5, 10)
new_shape = (5, -1, 5)
self.init_data()
self.op_type = "reshape2"
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.attrs = {"shape": self.new_shape}
self.outputs = {
"Out": self.inputs["X"].reshape(self.infered_shape),
'XShape': np.random.random(self.ori_shape).astype("float32")
}
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def init_data(self):
self.ori_shape = (2, 25)
self.new_shape = (5, 10)
self.infered_shape = (5, 10)
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInferInplace2(OpTest):
def setUp(self):
ori_shape = (2, 2, 6)
new_shape = (2, 0, 3, -1)
infered_shape = (2, 2, 3, -1)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
class TestReshapeOpDimInfer1(TestReshapeOp):
def init_data(self):
self.ori_shape = (5, 10)
self.new_shape = (5, -1, 5)
self.infered_shape = (5, -1, 5)
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer2(TestReshapeOp):
def init_data(self):
self.ori_shape = (2, 2, 6)
self.new_shape = (2, 0, 3, -1)
self.infered_shape = (2, 2, 3, -1)
class TestReshapeOpWithInputShape(OpTest):
......@@ -130,20 +63,23 @@ class TestReshapeOpWithInputShape(OpTest):
new_shape = (0, -1, 5)
actual_shape = (2, 3, 5)
self.op_type = "reshape"
self.op_type = "reshape2"
self.inputs = {
"X": np.random.random(ori_shape).astype("float32"),
"Shape": np.array(
actual_shape, dtype="int32")
}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(actual_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(actual_shape),
'XShape': np.random.random(ori_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(["X"], "Out")
self.check_grad(["X"], "Out", sum_outputs=["Out"])
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_rmsprop_op.py
浏览文件 @ a121c898
......@@ -15,90 +15,164 @@
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
class TestRmspropOp1(OpTest):
''' Test RMSProp with explicit inputs
'''
def setUp(self):
self.op_type = "rmsprop"
param = np.random.random((123, 321)).astype("float32")
mean_square = np.random.random((123, 321)).astype("float32")
learning_rate = np.array([0.01]).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
moment = np.zeros((123, 321)).astype("float32")
epsilon = 1e-6
decay = 0.9
momentum = 0.0
self.inputs = {
'Param': param,
'MeanSquare': mean_square,
'LearningRate': learning_rate,
'Grad': grad,
'Moment': moment,
}
self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum}
ms_out = decay * mean_square + (1 - decay) * grad * grad
moment_out = momentum * moment + \
learning_rate * grad / np.sqrt(ms_out + epsilon)
param_out = param - moment_out
self.outputs = {
'ParamOut': param_out,
'MomentOut': moment_out,
'MeanSquareOut': ms_out
}
def test_check_output(self):
self.check_output()
class TestRmspropOp2(OpTest):
'''Test RMSProp with default values for attributes
'''
def setUp(self):
self.op_type = "rmsprop"
param = np.random.random((123, 321)).astype("float32")
mean_square = np.random.random((123, 321)).astype("float32")
learning_rate = np.array([0.01]).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
moment = np.zeros((123, 321)).astype("float32")
epsilon = 1.0e-10
decay = 0.9
momentum = 0.0
self.inputs = {
'Param': param,
'MeanSquare': mean_square,
'LearningRate': learning_rate,
'Grad': grad,
'Moment': moment,
}
ms_out = decay * mean_square + (1 - decay) * grad * grad
moment_out = momentum * moment + \
learning_rate * grad / np.sqrt(ms_out + epsilon)
param_out = param - moment_out
self.outputs = {
'ParamOut': param_out,
'MomentOut': moment_out,
'MeanSquareOut': ms_out
}
def test_check_output(self):
self.check_output()
import paddle.fluid.core as core
from paddle.fluid.op import Operator
class TestBase(unittest.TestCase):
def setup(self, centered, epsilon=1e-6):
np.random.seed(5) # fix seed
self.param_name = "param"
self.param = np.random.random((123, 321)).astype("float32")
self.mean_square_name = "mean_square"
self.mean_square = np.random.random((123, 321)).astype("float32")
self.mean_grad_name = "mean_grad"
self.mean_grad = np.random.random((123, 321)).astype("float32")
self.lr_name = "lr"
self.learning_rate = np.array([0.01]).astype("float32")
self.grad_name = "grad"
self.grad = np.random.random((123, 321)).astype("float32")
self.moment_name = "moment"
self.moment = np.zeros((123, 321)).astype("float32")
self.epsilon = epsilon
self.decay = 0.9
self.momentum = 0.0
self.centered = centered
self.ms_out = self.decay * self.mean_square + (1 - self.decay
) * self.grad * self.grad
if centered:
self.mg_out = self.decay * self.mean_grad + (1 - self.decay
) * self.grad
self.moment_out = self.momentum * self.moment + \
self.learning_rate * self.grad / np.sqrt(self.ms_out - np.square(self.mg_out) + self.epsilon)
else:
self.moment_out = self.momentum * self.moment + \
self.learning_rate * self.grad / np.sqrt(self.ms_out + self.epsilon)
self.param_out = self.param - self.moment_out
def check(self,
actual_t,
expect_t,
place,
out_name,
atol=1e-5,
equal_nan=False):
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
+ str(expect_t) + "\n" + "But Got" + str(actual_t))
class TestRmspropOp(TestBase):
def check_with_place(self, place, centered, epsilon):
self.setup(centered, epsilon)
scope = core.Scope()
# create and initialize Param Variable
param = scope.var(self.param_name).get_tensor()
param.set(self.param, place)
mean_square = scope.var(self.mean_square_name).get_tensor()
mean_square.set(self.mean_square, place)
lr = scope.var(self.lr_name).get_tensor()
lr.set(self.learning_rate, place)
grad = scope.var(self.grad_name).get_tensor()
grad.set(self.grad, place)
moment = scope.var(self.moment_name).get_tensor()
moment.set(self.moment, place)
# create and run sgd operator
if self.centered:
mean_grad = scope.var(self.mean_grad_name).get_tensor()
mean_grad.set(self.mean_grad, place)
rmsprop_op = Operator(
"rmsprop",
Param=self.param_name,
Grad=self.grad_name,
MeanSquare=self.mean_square_name,
MeanGrad=self.mean_grad_name,
Moment=self.moment_name,
LearningRate=self.lr_name,
ParamOut=self.param_name,
MeanSquareOut=self.mean_square_name,
MomentOut=self.moment_name,
MeanGradOut=self.mean_grad_name,
epsilon=self.epsilon,
decay=self.decay,
momentum=self.momentum,
centered=True)
else:
rmsprop_op = Operator(
"rmsprop",
Param=self.param_name,
Grad=self.grad_name,
MeanSquare=self.mean_square_name,
Moment=self.moment_name,
LearningRate=self.lr_name,
ParamOut=self.param_name,
MeanSquareOut=self.mean_square_name,
MomentOut=self.moment_name,
epsilon=self.epsilon,
decay=self.decay,
momentum=self.momentum,
centered=False)
rmsprop_op.run(scope, place)
atol = 1e-5
equal_nan = False
if self.centered:
atol = 1e-3
equal_nan = True
self.check(
np.array(mean_square), self.ms_out, place, self.mean_square_name)
self.check(
np.array(moment),
self.moment_out,
place,
self.moment_name,
atol=atol,
equal_nan=equal_nan)
self.check(
np.array(param),
self.param_out,
place,
self.param_name,
atol=atol,
equal_nan=equal_nan)
if self.centered:
self.check(
np.array(mean_grad), self.mg_out, place, self.mean_grad_name)
def test_rmsprop(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place, False, 1e-6)
self.check_with_place(place, False, 1e-10)
self.check_with_place(place, True, 1e-6)
self.check_with_place(place, True, 1e-10)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_squeeze_op.py
浏览文件 @ a121c898
......@@ -23,14 +23,17 @@ from op_test import OpTest
# Correct: General.
class TestSqueezeOp(OpTest):
def setUp(self):
self.op_type = "squeeze"
self.op_type = "squeeze2"
self.init_test_case()
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.ori_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(["X"], "Out")
......
python/paddle/fluid/tests/unittests/test_transpose_op.py
浏览文件 @ a121c898
......@@ -22,16 +22,19 @@ from op_test import OpTest
class TestTransposeOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "transpose"
self.op_type = "transpose2"
self.inputs = {'X': np.random.random(self.shape).astype("float32")}
self.attrs = {'axis': list(self.axis)}
self.outputs = {'Out': self.inputs['X'].transpose(self.axis)}
self.outputs = {
'XShape': np.random.random(self.shape).astype("float32"),
'Out': self.inputs['X'].transpose(self.axis)
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(['X'], 'Out')
self.check_grad(['X'], 'Out', sum_outputs=['Out'])
def initTestCase(self):
self.shape = (3, 4)
......
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
浏览文件 @ a121c898
......@@ -24,13 +24,16 @@ from op_test import OpTest
class TestUnsqueezeOp(OpTest):
def setUp(self):
self.init_test_case()
self.op_type = "unsqueeze"
self.op_type = "unsqueeze2"
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.ori_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=["XShape"])
def test_check_grad(self):
self.check_grad(["X"], "Out")
......
python/paddle/fluid/trainer.py
浏览文件 @ a121c898
......@@ -431,6 +431,28 @@ class Trainer(object):
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, param_path, feeded_var_names,
target_var_indexes):
"""
Save model for cpp inference into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
feeded_var_names(list(str)): The name of the vars that you
need to feed in before run program.
target_var_indexes(list(int)): the index of target var that
you need to return in trainer.train_func.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
target_vars = [
self.train_func_outputs[index] for index in target_var_indexes
]
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
......
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ a121c898
......@@ -153,7 +153,7 @@ def block_to_code(block, block_idx):
indent += 1
# sort all vars
all_vars = sorted(block.vars.iteritems(), key=lambda x: x[0])
all_vars = sorted(six.iteritems(block.vars), key=lambda x: x[0])
for var in all_vars:
print("{}{}".format(get_indent_space(indent), variable_to_code(var[1])))
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ a121c898
......@@ -300,7 +300,7 @@ class DistributeTranspiler(object):
input_deps = grad_name_to_send_dummy_out.values()
program.global_block().append_op(
type="send_barrier",
inputs={"X": input_deps},
inputs={"X": list(input_deps)},
outputs={"Out": send_barrier_out},
attrs={
"endpoints": pserver_endpoints,
......@@ -401,7 +401,7 @@ class DistributeTranspiler(object):
Args:
recv_vars (list): Variable list to recv for current trainer_id
eplist (list): A list of strings indicating
eplist (list): A list of strings indicating
Returns:
Program: trainer side startup program.
......@@ -455,7 +455,7 @@ class DistributeTranspiler(object):
if len(splited_var) <= 1:
continue
# NOTE: if enable memory optimization, origin vars maybe removed.
if startup_program.global_block().vars.has_key(varname):
if varname in startup_program.global_block().vars:
orig_param = startup_program.global_block().vars[varname]
else:
origin_param_var = self.origin_program.global_block().vars[
......@@ -690,7 +690,7 @@ class DistributeTranspiler(object):
Args:
endpoint (str): current pserver endpoint.
Returns:
tuple: (main_program, startup_program), of type "Program"
"""
......@@ -713,7 +713,7 @@ class DistributeTranspiler(object):
endpoint (str): current pserver endpoint.
pserver_program (Program): deprecated, call get_pserver_program first.
startup_program (Program): deprecated, should pass startup_program
when initalizing
when initalizing
Returns:
Program: parameter server side startup program.
......
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