Merge pull request #368 from leondavi/add_tiles_dist_sys

[NERLPLANNER] add distributed type tiles

src_cpp/common/worker_definitions_ag.h

@@ -17,6 +17,6 @@ enum ModelTypeEnum{MODEL_TYPE_NN=0,MODEL_TYPE_APPROXIMATION=1,MODEL_TYPE_CLASSIF
 enum OptimizerEnum{OPTIMIZER_GD=0,OPTIMIZER_CGD=1,OPTIMIZER_SGD=2,OPTIMIZER_QUASINEUTON=3,OPTIMIZER_LVM=4,OPTIMIZER_ADAM=5};
 enum LossMethodEnum{LOSS_METHOD_SSE=1,LOSS_METHOD_MSE=2,LOSS_METHOD_NSE=3,LOSS_METHOD_MINKOWSKIE=4,LOSS_METHOD_WSE=5,LOSS_METHOD_CEE=6};
 enum InfraTypeEnum{INFRA_TYPE_OPENNN=0,INFRA_TYPE_WOLFENGINE=1};
-enum WorkerDistributedSystemTypeEnum{WORKER_DISTRIBUTED_SYSTEM_TYPE_NONE=0,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAVG=1,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAVG=2,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTWEIGHTEDAVGCLASSIFICATION=3,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERWEIGHTEDAVGCLASSIFICATION=4,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAE=5,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAE=6};
+enum WorkerDistributedSystemTypeEnum{WORKER_DISTRIBUTED_SYSTEM_TYPE_NONE=0,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAVG=1,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAVG=2,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTWEIGHTEDAVGCLASSIFICATION=3,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERWEIGHTEDAVGCLASSIFICATION=4,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAE=5,WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAE=6,WORKER_DISTRIBUTED_SYSTEM_TYPE_TILES=7};
 
 } // namespace nerlnet

src_erl/NerlnetApp/src/Bridge/nerlTests.erl

@@ -90,9 +90,9 @@ run_tests()->
       NerlworkerTestFunc = fun(_Rounds) ->  Performance = 0, nerlworker_test(NeuralNetworkTestingModelList, Performance) end, 
       NerlworkerTestName = "nerlworker_test",
       test_envelope_nif_performance(NerlworkerTestFunc, NerlworkerTestName, length(NeuralNetworkTestingModelList) ),
-      nerltest_print("count label test"),
+
       CountLabelTestName = "test_count_label",
-      CountLabelTestFunc = fun(_Rounds) ->   test_count_label_nif() end, 
+      CountLabelTestFunc = fun(_Rounds) ->   count_label_nif_test() end, 
       test_envelope(CountLabelTestFunc, CountLabelTestName, 1 ),
       nerltest_print("Tests Completed"),
       ok.
@@ -305,7 +305,10 @@ nerlworker_test_generate_data(LayersSizes, LayerTypes, NumOfSamples) -> %% Ask D
       {NerlTensor , Type} = nerlNIF:nerltensor_conversion({ErlDataTensor,erl_float} , float),
       {NerlTensor , Type , ErlDataTensor , erl_float , NumOfFeatures , NumOfLabels}.
 
-test_count_label_nif() -> 
+count_label_nif_test() -> 
+      % TODO - Ori please move the network configuration to neural_networks_testing_models.hrl
+      % Please add performance evaluation - Run this test 10-20 rounds and accumulate performance of nif
+      % By adding tic toc before and after the NIF
       ModelId  = erlang:unique_integer([positive]),
       ModelType = "0",
       ModelArgs = "",
@@ -315,7 +318,7 @@ test_count_label_nif() ->
       OptimizerType = "2",
       OptimizerArgs = "",
       LossMethod = "2",
-      DistributedSystemType = "3",
+      DistributedSystemType = "3", % TODO this should be derived from AG macro
       DistributedSystemArg = "",
       DimMaxDimX = ?NERLWORKER_DISTRIBUTED_FED_WEIGHTED_AVG_CLASSIFIER_DATA_DIM_X,
       DimMinDimX = ?NERLWORKER_DISTRIBUTED_FED_WEIGHTED_AVG_CLASSIFIER_DATA_MIN_DIM_X,
@@ -333,8 +336,8 @@ test_count_label_nif() ->
                 LenActualDataIf  =  LenActualData
       end,
       DataRand = generate_nerltensor(float,?TEST_LABEL_COUNT_NUMOF_SAMPLES,LenActualDataIf,1),
-      LayersSizes = nerl:string_format("~p,~p",[LenActualDataIf-LenActualLabels,LenActualLabels]),
-      LayersTypes = "1,3",
+      LayersSizes = nerl:string_format("~p,~p",[LenActualDataIf-LenActualLabels,LenActualLabels]), 
+      LayersTypes = "1,3",% Please move it to neural_networks_testing_models.hrl as part of NN configuration
       nerlNIF:test_nerlworker_nif(ModelId,ModelType,ModelArgs,LayersSizes, LayersTypes, 
       LayersFunctionalityCodes, LearningRate, Epochs, OptimizerType, 
       OptimizerArgs, LossMethod, DistributedSystemType, DistributedSystemArg),
@@ -346,11 +349,11 @@ test_count_label_nif() ->
       {_,DataRandRes} = lists:split(3, DataRand),
       Sum = get_label_count(LenActualLabels,LenActualDataIf,SumInit,DataRandRes,0) ,
       {_,LabelCountRes} = lists:split(3, LabelCountFloat),
+      nerlNIF:remove_nerlworker_nif(ModelId),
       if 
             (Sum == LabelCountRes) -> nerltest_print("Label count test passed");
             true -> throw(nerl:string_format("Label count test failed ~n Sum: ~p ~n LabelCount: ~p",[Sum,LabelCountRes]))
-      end,
-      nerlNIF:remove_nerlworker_nif(ModelId).
+      end.
 
 
 get_label_count(LenLabel,LenData,Sum,Data,N) -> 

src_erl/NerlnetApp/src/Client/clientWorkersFunctions.erl

@@ -10,6 +10,9 @@
 
 get_distributed_worker_behavior(ClientEtsRef, DistributedSystemType , WorkerName , DistributedSystemArgs , DistributedSystemToken) ->
 case DistributedSystemType of
+      ?DC_DISTRIBUTED_SYSTEM_TYPE_TILES_IDX_STR ->
+        DistributedBehaviorFunc = fun workerNN:controller/2,
+        DistributedWorkerData = {tiles, DistributedSystemArgs, DistributedSystemToken};
       ?DC_DISTRIBUTED_SYSTEM_TYPE_NONE_IDX_STR ->
         DistributedBehaviorFunc = fun workerNN:controller/2,
         DistributedWorkerData = none;

src_erl/NerlnetApp/src/worker_definitions_ag.hrl

@@ -38,6 +38,7 @@
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERWEIGHTEDAVGCLASSIFICATION_KEY_ATOM,fedServerWeightedAvgClassification).
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAE_KEY_ATOM,fedClientAE).
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAE_KEY_ATOM,fedServerAE).
+-define(DC_DISTRIBUTED_SYSTEM_TYPE_TILES_KEY_ATOM,tiles).
 
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_NONE_IDX_STR,"0").
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAVG_IDX_STR,"1").
@@ -46,6 +47,7 @@
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERWEIGHTEDAVGCLASSIFICATION_IDX_STR,"4").
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAE_IDX_STR,"5").
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAE_IDX_STR,"6").
+-define(DC_DISTRIBUTED_SYSTEM_TYPE_TILES_IDX_STR,"7").
 
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_NONE_IDX,0).
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAVG_IDX,1).
@@ -54,6 +56,7 @@
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERWEIGHTEDAVGCLASSIFICATION_IDX,4).
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTAE_IDX,5).
 -define(DC_DISTRIBUTED_SYSTEM_TYPE_FEDSERVERAE_IDX,6).
+-define(DC_DISTRIBUTED_SYSTEM_TYPE_TILES_IDX,7).
 
 -define(DC_INFRA_TYPE_OPENNN_KEY_ATOM,opennn).
 -define(DC_INFRA_TYPE_WOLFENGINE_KEY_ATOM,wolfengine).

src_py/nerlPlanner/JsonElementWorkerDefinitions.py

@@ -123,7 +123,8 @@
     ("FedClientWeightedAvgClassification" , "3"),
     ("FedServerWeightedAvgClassification" , "4"),
     ("FedClientAE" , "5"),
-    ("FedServerAE" , "6")
+    ("FedServerAE" , "6"),
+    ("tiles", "7")
 ])
 
 InfraTypeMapping = OrderedDict([

tests/NerlnetNifTest.sh

@@ -56,9 +56,10 @@ COMPILE_NERLTENSOR="compile:file(\"nerlTensor.erl\")"
 
 COMPILE_FILES="$COMPILE_NERL,$COMPILE_NERLNIF,$COMPILE_NERLTEST,$COMPILE_NERLTENSOR"
 
-erl -noshell -eval "$COMPILE_FILES, nerlTests:run_tests()." -s init stop > "$NERLNET_TEST_DIR/$LOG_FILE" #TODO fix 
+erl -noshell -eval "$COMPILE_FILES, nerlTests:run_tests()." -s init stop > "$NERLNET_TEST_DIR/$LOG_FILE"
 rc=$(echo $?)
 cat "$NERLNET_TEST_DIR/$LOG_FILE"
 cd -
 
+echo "Test returned: $rc"
 exit $rc