Merge pull request #1615 from luotao1/ml

remove with_metric_learning compile option
8 years ago · e9166edd13
parent 95b4e397dc 6492666d77
commit e9166edd13
13 changed files with 3 additions and 83 deletions
--- a/cmake/util.cmake
+++ b/cmake/util.cmake
@ -71,21 +71,10 @@ function(link_paddle_exe TARGET_NAME)
        generate_rdma_links()
    endif()

-    if(WITH_METRIC)
-        if(WITH_GPU)
-            set(METRIC_LIBS paddle_metric_learning paddle_dserver_lib metric metric_cpu)
-        else()
-            set(METRIC_LIBS paddle_metric_learning paddle_dserver_lib metric_cpu)
-        endif()
-    else()
-        set(METRIC_LIBS "")
-    endif()
-
    target_circle_link_libraries(${TARGET_NAME}
        ARCHIVE_START
        paddle_gserver
        paddle_function
-        ${METRIC_LIBS}
        ARCHIVE_END
        paddle_pserver
        paddle_trainer_lib
@ -95,7 +84,6 @@ function(link_paddle_exe TARGET_NAME)
        paddle_parameter
        paddle_proto
        paddle_cuda
-        ${METRIC_LIBS}
        ${EXTERNAL_LIBS}
        ${CMAKE_THREAD_LIBS_INIT}
        ${CMAKE_DL_LIBS}
--- a/doc/howto/usage/cmd_parameter/arguments_cn.md
+++ b/doc/howto/usage/cmd_parameter/arguments_cn.md
@ -228,16 +228,6 @@
 <td class="left"></td><td class="left"></td><td class="left">√</td><td class="left">√</td>
 </tr>

-<tr>
-<td class="left" rowspan = "2">度量学习(metric learning)</td><td class="left">external</td>
-<td class="left">√</td><td class="left">√</td><td class="left">√</td><td class="left">√</td>
-</tr>
-
-<tr>
-<td class="left">data_server_port</td>
-<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
-</tr>
-
 <tr>
 <td class="left" rowspan = "16">参数服务器(PServer)</td><td class="left">start_pserver</td>
 <td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
--- a/doc/howto/usage/cmd_parameter/arguments_en.md
+++ b/doc/howto/usage/cmd_parameter/arguments_en.md
@ -228,16 +228,6 @@ It looks like there are a lot of arguments. However, most of them are for develo
 <td class="left"></td><td class="left"></td><td class="left">√</td><td class="left">√</td>
 </tr>

-<tr>
-<td class="left" rowspan = "2">metric learning</td><td class="left">external</td>
-<td class="left">√</td><td class="left">√</td><td class="left">√</td><td class="left">√</td>
-</tr>
-
-<tr>
-<td class="left">data_server_port</td>
-<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
-</tr>
-
 <tr>
 <td class="left" rowspan = "16">PServer</td><td class="left">start_pserver</td>
 <td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
--- a/doc/howto/usage/cmd_parameter/detail_introduction_cn.md
+++ b/doc/howto/usage/cmd_parameter/detail_introduction_cn.md
@ -180,15 +180,6 @@
  - 用户可以自定义beam search的方法，编译成动态库，供PaddlePaddle加载。 该参数用于指定动态库路径.
  - 类型: string (默认: "", null).

-## 度量学习(Metric Learning)
-* `--external`
-   - 指示是否使用外部机器进行度量学习.
-   - 类型: bool (默认: 0).
-
-* `--data_server_port`
-  - 数据服务器(data server)的监听端口，主要用在度量学习中.
-  - 类型: int32 (默认: 21134).
-
 ## 数据支持(DataProvider)

 * `--memory_threshold_on_load_data`
--- a/doc/howto/usage/cmd_parameter/detail_introduction_en.md
+++ b/doc/howto/usage/cmd_parameter/detail_introduction_en.md
@ -184,15 +184,6 @@
  - Specify shared dynamic library. It can be defined out of paddle by user.
  - type: string (default: "", null).

-## Metric Learning
-* `--external`
-   - Whether to use external machine for metric learning.
-   - type: bool (default: 0).
-
-* `--data_server_port`
-  - Listening port for dserver (data server), dserver is mainly used in metric learning.
-  - type: int32 (default: 21134).
-
 ## DataProvider

 * `--memory_threshold_on_load_data`
--- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp
+++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp
@ -24,9 +24,6 @@ limitations under the License. */
 DEFINE_bool(allow_only_one_model_on_one_gpu,
            true,
            "If true, do not allow multiple models on one GPU device");
-#ifdef PADDLE_METRIC_LEARNING
-DECLARE_bool(external);
-#endif

 namespace paddle {

@ -45,11 +42,7 @@ MultiGradientMachine::MultiGradientMachine(const ModelConfig& config,
      trainerBarrier_(FLAGS_trainer_count),
      allBarrier_(FLAGS_trainer_count + 1),
      inArgsCopied_(false) {
-#ifdef PADDLE_METRIC_LEARNING
-  isPassGrad_ = FLAGS_external;
-#else
  isPassGrad_ = false;
-#endif
  numThreads_ = FLAGS_trainer_count;
  if (useGpu) {
    //! TODO(yuyang18): When useGpu=false && paddle is not compiled with gpu,
--- a/paddle/gserver/layers/Layer.cpp
+++ b/paddle/gserver/layers/Layer.cpp
@ -381,8 +381,7 @@ void Layer::backwardActivation() {
 void Layer::forwardDropOut() {
  auto& outV = getOutputValue();

-  if (passType_ == PASS_TRAIN || passType_ == PASS_METRIC_TRAIN ||
-      passType_ == PASS_METRIC_TRAIN_WITH_NOERROR) {
+  if (passType_ == PASS_TRAIN) {
    // new dropOutMask_ if dropOutMask_ is null ptr
    Matrix::resizeOrCreate(dropOutMask_,
                           outV->getHeight(),
--- a/paddle/pserver/BaseClient.h
+++ b/paddle/pserver/BaseClient.h
@ -30,9 +30,6 @@ namespace paddle {
 * the first solution arms with sendThreads_/recvThreads_ and sendJobQueue_/
 * recvJobQueue_. the second solution use some shared thread pool to manage
 * connections.
- * In addition to pserver, metric learning also uses network to exchange
- * features within multi-machines, so this class just abstracts some basic
- * threads and queue buffer creation for them
 */
 class BaseClient {
 protected:
--- a/paddle/pserver/ParameterServer2.cpp
+++ b/paddle/pserver/ParameterServer2.cpp
@ -367,11 +367,8 @@ void ParameterServer2::addGradient(const SendParameterRequest& request,
                                   std::vector<Buffer>* outputBuffers) {
  VLOG(1) << "pserver: addGradient";

-/// forwardbackward delta from all trainers
-/// indicate the fluctuation caused by forwardbackward.
-#ifndef PADDLE_METRIC_LEARNING
-  // @TODO(yanfei):
-  // add support tuning forwardbackward balance for metric learning
+  // forwardbackward delta from all trainers
+  // indicate the fluctuation caused by forwardbackward.
  if (!numPassFinishClients_) {
    REGISTER_BARRIER_DELTA_SERVER_SET(
        *statSet_,
@ -381,7 +378,6 @@ void ParameterServer2::addGradient(const SendParameterRequest& request,
        request.forwardbackward_time(),
        isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
  }
-#endif

  {
    /// approximately pure network overhead
--- a/paddle/trainer/Trainer.h
+++ b/paddle/trainer/Trainer.h
@ -30,10 +30,6 @@ limitations under the License. */
 #include "TrainerConfigHelper.h"
 #include "TrainerInternal.h"

-#ifdef PADDLE_METRIC_LEARNING
-#include "paddle/internals/metric_learning/MetricTrainer.h"
-#endif
-
 DECLARE_int32(num_passes);

 namespace paddle {
@ -201,12 +197,8 @@ protected:
  // parameter util
  std::unique_ptr<ParameterUtil> paramUtil_;

-#ifdef PADDLE_METRIC_LEARNING
-  MetricTrainer trainerInternal_;
-#else
  // trainer Internal
  TrainerInternal trainerInternal_;
-#endif
 };

 }  // namespace paddle
--- a/paddle/utils/Flags.cpp
+++ b/paddle/utils/Flags.cpp
@ -30,7 +30,6 @@ DEFINE_bool(parallel_nn,
 DEFINE_int32(trainer_count, 1, "Defined how many trainers to train");
 DEFINE_int32(gpu_id, 0, "Which gpu core to use");
 DEFINE_int32(port, 20134, "Listening port for pserver");
-DEFINE_int32(data_server_port, 21134, "Listening port for dserver");
 DEFINE_int32(ports_num,
             1,
             "Number of ports for sending dense parameter,"
--- a/paddle/utils/Flags.h
+++ b/paddle/utils/Flags.h
@ -19,7 +19,6 @@ limitations under the License. */
 DECLARE_bool(parallel_nn);
 DECLARE_int32(async_count);
 DECLARE_int32(port);
-DECLARE_int32(data_server_port);
 DECLARE_bool(use_gpu);
 DECLARE_int32(gpu_id);
 DECLARE_int32(trainer_count);
--- a/paddle/utils/GlobalConstants.h
+++ b/paddle/utils/GlobalConstants.h
@ -23,11 +23,6 @@ enum PassType {
  PASS_TEST,    // Test pass
  PASS_GC,      // Gradient Check pass
  PASS_METRIC,  // pass for generate template output with no drop rate.
-  // pass for metric learning training with metric learning error, only used
-  // when we are doing KNN evaluation.
-  PASS_METRIC_TRAIN,
-  PASS_METRIC_TRAIN_WITH_NOERROR,  // Pass for metric learning training
-                                   // with no evaluation.
 };

 enum ParameterType {