sparse update support ready

Source/Common/Include/MultiversoWrapper.h

@@ -4,6 +4,7 @@
 // the header files in ..\Multiverso\include
 #include <multiverso/multiverso.h>
 #include <multiverso/table/matrix_table.h>
+#include <multiverso/util/configure.h>
 #pragma comment(lib, "IMultiverso.lib")
 
 #ifndef CPUONLY
@@ -225,8 +226,8 @@ namespace Microsoft {
 							// waiting copy from GPU to CPU has finished
 							CudaErrorCheck(cudaStreamSynchronize(_commStream));
 
-							// calculate delta
-							std::transform(m_deltaArray, m_deltaArray + m_totalModelSize, m_cpuAsyncBuffer[t_cacheIdx], m_deltaArray, std::minus<ElemType>());
+							// delta =  gradient * learning_rate
+              std::transform(m_cpuAsyncBuffer[t_cacheIdx], m_cpuAsyncBuffer[t_cacheIdx] + m_totalModelSize, m_deltaArray, m_deltaArray, std::minus<ElemType>());
 
 							// lr decay
 							std::transform(m_deltaArray, m_deltaArray + m_totalModelSize, m_deltaArray, std::bind1st(std::multiplies<ElemType>(), factor));
@@ -239,9 +240,6 @@ namespace Microsoft {
 								multiversoMatrix->Get(py, m_tableLength[widx]);
 							}
 
-							//m_matrixArray->Add(m_deltaArray, m_totalModelSize);
-							//m_matrixArray->Get(m_cpuAsyncBuffer[t_cacheIdx], m_totalModelSize);
-
 							// copy parameters from CPU buffer to GPU buffer
 							for (int widx = 0; widx < m_tableCount; widx++)
 							{
@@ -262,7 +260,7 @@ namespace Microsoft {
 							float factor = DecayCoefficient();
 							int t_cacheIdx = m_bufferInUse;
 
-							std::transform(m_deltaArray, m_deltaArray + m_totalModelSize, m_cpuAsyncBuffer[t_cacheIdx], m_deltaArray, std::minus<ElemType>());
+              std::transform(m_cpuAsyncBuffer[t_cacheIdx], m_cpuAsyncBuffer[t_cacheIdx] + m_totalModelSize, m_deltaArray, m_deltaArray, std::minus<ElemType>());
 							std::transform(m_deltaArray, m_deltaArray + m_totalModelSize, m_deltaArray, std::bind1st(std::multiplies<ElemType>(), factor));
 							for (int widx = 0; widx < m_tableCount; widx++)
 							{
@@ -291,7 +289,7 @@ namespace Microsoft {
 							mat.CopyToArray(px, m_tableLength[i]);
 						}
 
-						std::transform(m_deltaArray, m_deltaArray + m_totalModelSize, m_cpuAsyncBuffer[0], m_deltaArray, std::minus<ElemType>());
+            std::transform(m_cpuAsyncBuffer[0], m_cpuAsyncBuffer[0] + m_totalModelSize, m_deltaArray, m_deltaArray, std::minus<ElemType>());
 
 						// lr decay
 						std::transform(m_deltaArray, m_deltaArray + m_totalModelSize, m_deltaArray, std::bind1st(std::multiplies<ElemType>(), factor));
@@ -303,8 +301,6 @@ namespace Microsoft {
 							multiversoMatrix->Add(px, m_tableLength[widx]);
 							multiversoMatrix->Get(py, m_tableLength[widx]);
 						}
-						//m_matrixArray->Add(m_deltaArray, m_totalModelSize);
-						//m_matrixArray->Get(m_cpuAsyncBuffer[0], m_totalModelSize);
 
 						i = 0;
 						for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++, i++)
@@ -349,6 +345,7 @@ namespace Microsoft {
 					m_isInitialized = true;
 
 					multiverso::MV_Init();
+          multiverso::SetCMDFlag<std::string>(std::string("updater_type"), std::string("sgd"));
 
 					m_matrixArray = new std::vector< multiverso::MatrixWorkerTable<ElemType>*>();
 					m_serverArray = new std::vector< multiverso::MatrixServerTable<ElemType>*>();

Source/Multiverso

@@ -1 +1 @@
-Subproject commit 6f6cf3d44407cfdb15a4152b7d73faef7eafea29
+Subproject commit f9217b4909609425db9bb5a5889b20357fc14fce