Merge pull request #1782 from jacquesqiao/support-remote-updater

support distribute training in python v2 API

demo/word2vec/train_v2.py → demo/word2vec/api_train_v2.py

+import gzip
 import math
 
 import paddle.v2 as paddle
 
-dictsize = 1953
 embsize = 32
 hiddensize = 256
 N = 5
 
 
 def wordemb(inlayer):
-    wordemb = paddle.layer.table_projection(
+    wordemb = paddle.layer.embedding(
         input=inlayer,
         size=embsize,
         param_attr=paddle.attr.Param(
             name="_proj",
             initial_std=0.001,
             learning_rate=1,
-            l2_rate=0, ))
+            l2_rate=0,
+            sparse_update=True))
     return wordemb
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    # for local training
+    cluster_train = False
+
+    if not cluster_train:
+        paddle.init(use_gpu=False, trainer_count=1)
+    else:
+        paddle.init(
+            use_gpu=False,
+            trainer_count=2,
+            port=7164,
+            ports_num=1,
+            ports_num_for_sparse=1,
+            num_gradient_servers=1)
     word_dict = paddle.dataset.imikolov.build_dict()
     dict_size = len(word_dict)
     firstword = paddle.layer.data(
@@ -57,6 +70,9 @@ def main():
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id % 100 == 0:
+                with gzip.open("batch-" + str(event.batch_id) + ".tar.gz",
+                               'w') as f:
+                    trainer.save_parameter_to_tar(f)
                 result = trainer.test(
                     paddle.batch(
                         paddle.dataset.imikolov.test(word_dict, N), 32))
@@ -65,11 +81,15 @@ def main():
                     result.metrics)
 
     cost = paddle.layer.classification_cost(input=predictword, label=nextword)
+
     parameters = paddle.parameters.create(cost)
-    adam_optimizer = paddle.optimizer.Adam(
+    adagrad = paddle.optimizer.AdaGrad(
         learning_rate=3e-3,
         regularization=paddle.optimizer.L2Regularization(8e-4))
-    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+    trainer = paddle.trainer.SGD(cost,
+                                 parameters,
+                                 adagrad,
+                                 is_local=not cluster_train)
     trainer.train(
         paddle.batch(paddle.dataset.imikolov.train(word_dict, N), 32),
         num_passes=30,

paddle/api/PaddleAPI.h

@@ -19,6 +19,7 @@ limitations under the License. */
 #include <stdexcept>
 #include <string>
 #include <vector>
+#include "paddle/gserver/gradientmachines/GradientMachine.h"
 #include "paddle/utils/Common.h"
 #include "paddle/utils/GlobalConstants.h"
 
@@ -468,8 +469,10 @@ private:
 };
 
 enum GradientMatchineCreateMode {
-  CREATE_MODE_NORMAL = 0,
-  CREATE_MODE_TESTING = 4
+  CREATE_MODE_NORMAL = paddle::GradientMachine::kNormal,
+  CREATE_MODE_SGD_SPARSE_CPU_TRAINING =
+      paddle::GradientMachine::kSgdSparseCpuTraining,
+  CREATE_MODE_TESTING = paddle::GradientMachine::kTesting
 };
 
 struct ParameterConfigPrivate;
@@ -817,7 +820,8 @@ private:
 public:
   static ParameterUpdater* createLocalUpdater(OptimizationConfig* config);
   static ParameterUpdater* createRemoteUpdater(OptimizationConfig* config,
-                                               int passCount);
+                                               int passCount,
+                                               bool useSparseUpdater);
   ~ParameterUpdater();
 
   /**
@@ -855,6 +859,13 @@ public:
    */
   void update(Parameter* param);
 
+  /**
+   * @breif only get required sparse rows by default.
+   * @param fullSize: get full matrix parameter if *fullSize* set
+   * @param apply: get PARAMETER_APPLY on pserver if *apply* set
+   */
+  void getParametersRemote(bool fullSize = false, bool apply = false);
+
   /**
    * @brief restore the average parameter.
    * @note It is only used in AverageOptimizer. Restore will get the current

paddle/api/ParameterUpdater.cpp

@@ -29,10 +29,22 @@ ParameterUpdater *ParameterUpdater::createLocalUpdater(
 }
 
 ParameterUpdater *ParameterUpdater::createRemoteUpdater(
-    OptimizationConfig *config, int passCount) {
+    OptimizationConfig *config, int passCount, bool useSparseUpdater) {
   auto updater = new ParameterUpdater();
-  updater->m->updater.reset(new paddle::RemoteParameterUpdater(
-      config->m->getConfig(), passCount, nullptr));
+  auto remoteUpdater = new paddle::RemoteParameterUpdater(
+      config->m->getConfig(), passCount, nullptr);
+  if (useSparseUpdater) {
+    std::unique_ptr<paddle::ParameterUpdater> remoteUpdaterPtr(remoteUpdater);
+    auto sparseRemoteUpdater =
+        new paddle::SparseRemoteParameterUpdaterComposite(
+            config->m->getConfig(),
+            passCount,
+            false,
+            std::move(remoteUpdaterPtr));
+    updater->m->updater.reset(sparseRemoteUpdater);
+  } else {
+    updater->m->updater.reset(remoteUpdater);
+  }
   return updater;
 }
 
@@ -59,6 +71,10 @@ void ParameterUpdater::update(Parameter *param) {
   m->updater->update(paddleParam);
 }
 
+void ParameterUpdater::getParametersRemote(bool fullSize, bool apply) {
+  m->updater->getParametersRemote(fullSize, apply);
+}
+
 void ParameterUpdater::restore() { m->updater->restore(); }
 
 void ParameterUpdater::apply() { m->updater->apply(); }

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -518,7 +518,7 @@ void TrainerThread::computeThread() {
         backward();
         break;
       case MultiGradientMachine::TASK_COPY_IN_ARGS:
-        copyInArgs();
+        batchSize_ = copyInArgs();
         inArgsCopied_ = true;
         multiMachine_->waitForCopyInArgs();
         break;

python/paddle/v2/optimizer.py

@@ -38,12 +38,35 @@ class Optimizer(object):
         assert isinstance(tmp, swig_api.ParameterOptimizer)
         return tmp.getParameterTypes()
 
-    def create_local_updater(self):
+    def __create_local_updater__(self):
         return swig_api.ParameterUpdater.createLocalUpdater(self.__opt_conf__)
 
-    def create_remote_updater(self, pass_num):
-        return swig_api.ParameterUpdater.createRemoteUpdater(self.__opt_conf__,
-                                                             pass_num)
+    def __create_remote_updater__(self, pass_num, use_sparse_updater):
+        return swig_api.ParameterUpdater.createRemoteUpdater(
+            self.__opt_conf__, pass_num, use_sparse_updater)
+
+    def create_updater(self, is_local, num_passes, use_sparse_updater):
+        """
+        create proper parameter_updater by configuration.
+        :param is_local: create local or remote parameter updater
+        :param num_passes: remote parameter updater will use this to config
+        parameter server.
+        :param use_sparse_updater: when use remote updater, if some parameter is
+        sparse, updater should do some extra thing:
+
+        ..  code-block:: python
+
+            if use_sparse_remote_updater:
+                        gradient_machine.prefetch(in_args)
+                        parameter_updater.getParametersRemote()
+        :return: parameter_updater
+        """
+        if is_local:
+            parameter_updater = self.__create_local_updater__()
+        else:
+            parameter_updater = self.__create_remote_updater__(
+                num_passes, use_sparse_updater)
+        return parameter_updater
 
 
 class Momentum(Optimizer):

python/paddle/v2/topology.py

@@ -73,6 +73,18 @@ class Topology(object):
 
         assert isinstance(self.__model_config__, ModelConfig)
 
+    def use_sparse_updater(self):
+        """
+        check if any parameter require to use sparse_update
+        :return:
+        """
+        use_sparse = False
+        for parameter in self.__model_config__.parameters:
+            if parameter.sparse_update or parameter.sparse_remote_update:
+                use_sparse = True
+                break
+        return use_sparse
+
     def proto(self):
         return self.__model_config__
 

python/paddle/v2/trainer.py

@@ -2,6 +2,8 @@
 Module Trainer
 """
 import collections
+import gzip
+import os
 
 import py_paddle.swig_paddle as api
 
@@ -42,7 +44,12 @@ class SGD(object):
     :type extra_layers: paddle.v2.config_base.Layer
     """
 
-    def __init__(self, cost, parameters, update_equation, extra_layers=None):
+    def __init__(self,
+                 cost,
+                 parameters,
+                 update_equation,
+                 extra_layers=None,
+                 is_local=True):
 
         if not isinstance(parameters, v2_parameters.Parameters):
             raise TypeError('parameters should be parameters')
@@ -55,20 +62,48 @@ class SGD(object):
         self.__topology__ = topology
         self.__parameters__ = parameters
         self.__topology_in_proto__ = topology.proto()
+        self.__is_local__ = is_local
 
-        # In local mode, disable sparse_remote_update.
-        for param in self.__topology_in_proto__.parameters:
-            if param.sparse_remote_update:
-                param.sparse_remote_update = False
+        self.__use_sparse_updater__ = self.__topology__.use_sparse_updater()
+        # # In local mode, disable sparse_remote_update.
+        if is_local:
+            for param in self.__topology_in_proto__.parameters:
+                if param.sparse_remote_update:
+                    param.sparse_remote_update = False
 
+        self.__gm_create_mode__ = api.CREATE_MODE_NORMAL if not \
+            self.__use_sparse_updater__ else api.CREATE_MODE_SGD_SPARSE_CPU_TRAINING
         self.__data_types__ = topology.data_type()
         gm = api.GradientMachine.createFromConfigProto(
-            self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
+            self.__topology_in_proto__, self.__gm_create_mode__,
             self.__optimizer__.enable_types())
         assert isinstance(gm, api.GradientMachine)
         self.__gradient_machine__ = gm
         self.__gradient_machine__.randParameters()
-        parameters.append_gradient_machine(gm)
+        self.__parameters__.append_gradient_machine(gm)
+        self.__parameter_updater__ = None
+
+    def __use_remote_sparse_updater__(self):
+        return self.__use_sparse_updater__ and not self.__is_local__
+
+    def __prepare_parameter__(self, in_args):
+        """
+        prepare parameter before forward backward.
+        1. When use remote sparse updater, parameters should be got
+        from ps according to input arguments.
+        :param in_args: input arguments of this batch.
+        :return:
+        """
+        if self.__use_remote_sparse_updater__():
+            self.__gradient_machine__.prefetch(in_args)
+            self.__parameter_updater__.getParametersRemote()
+
+    def save_parameter_to_tar(self, f):
+        self.__parameter_updater__.catchUpWith()
+        self.__parameter_updater__.apply()
+        self.__parameter_updater__.getParametersRemote(True, True)
+        self.__parameters__.to_tar(f)
+        self.__parameter_updater__.restore()
 
     def train(self, reader, num_passes=1, event_handler=None, feeding=None):
         """
@@ -90,8 +125,9 @@ class SGD(object):
             event_handler = default_event_handler
         __check_train_args__(**locals())
 
-        updater = self.__optimizer__.create_local_updater()
-        updater.init(self.__gradient_machine__)
+        self.__parameter_updater__ = self.__optimizer__.create_updater(
+            self.__is_local__, num_passes, self.__use_sparse_updater__)
+        self.__parameter_updater__.init(self.__gradient_machine__)
 
         self.__gradient_machine__.start()
         batch_evaluator = self.__gradient_machine__.makeEvaluator()
@@ -103,23 +139,26 @@ class SGD(object):
         for pass_id in xrange(num_passes):
             event_handler(v2_event.BeginPass(pass_id))
             pass_evaluator.start()
-            updater.startPass()
+            self.__parameter_updater__.startPass()
             for batch_id, data_batch in enumerate(reader()):
                 batch_evaluator.start()
                 event_handler(
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
-                pass_type = updater.startBatch(len(data_batch))
-                self.__gradient_machine__.forwardBackward(
-                    feeder(data_batch), out_args, pass_type)
+                pass_type = self.__parameter_updater__.startBatch(
+                    len(data_batch))
+                in_args = feeder(data_batch)
+                self.__prepare_parameter__(in_args)
+                self.__gradient_machine__.forwardBackward(in_args, out_args,
+                                                          pass_type)
                 self.__gradient_machine__.eval(pass_evaluator)
                 self.__gradient_machine__.eval(batch_evaluator)
                 for each_param in self.__gradient_machine__.getNonStaticParameters(
                 ):
-                    updater.update(each_param)
+                    self.__parameter_updater__.update(each_param)
                 cost_sum = out_args.sum()
                 cost = cost_sum / len(data_batch)
-                updater.finishBatch(cost)
+                self.__parameter_updater__.finishBatch(cost)
                 batch_evaluator.finish()
                 event_handler(
                     v2_event.EndIteration(
@@ -128,7 +167,7 @@ class SGD(object):
                         cost=cost,
                         evaluator=batch_evaluator))
 
-            updater.finishPass()
+            self.__parameter_updater__.finishPass()
             pass_evaluator.finish()
             event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
         self.__gradient_machine__.finish()
@@ -152,8 +191,9 @@ class SGD(object):
         num_samples = 0.0
         for data_batch in reader():
             num_samples += len(data_batch)
-            self.__gradient_machine__.forward(
-                feeder(data_batch), out_args, api.PASS_TEST)
+            in_args = feeder(data_batch)
+            self.__prepare_parameter__(in_args)
+            self.__gradient_machine__.forward(in_args, out_args, api.PASS_TEST)
             total_cost += out_args.sum()
             self.__gradient_machine__.eval(evaluator)