Add MF fixes to release branch#3292

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Add MF fixes to release branch #3292

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7d842d8
Fix marshalling of bool flags in MF (#3210)
wschin Apr 5, 2019
fb0b69c
Polish marshalling of MF model and MF problem and enable 32-bit tests…
wschin Apr 8, 2019
85f1f56
Ensable the uses of SSE and OpenMP in LIBMF (#3265)
wschin Apr 10, 2019
3668b3e
Update description about key type and add one more reference (#3170)
wschin Apr 5, 2019
e73749d
Try buid LIBMF with OpenMP again (#3297)
wschin Apr 11, 2019
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24 changes: 11 additions & 13 deletions src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
/// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
/// </para>
/// <code language="csharp">
/// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
/// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
/// // Similarly, for 1-based indices, firstRowIndex could be 1.
/// const int firstRowIndex = 1;
/// const int firstColumnIndex = 1;
/// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
/// // is 0-based.
/// const int m = 60;
/// const int n = 100;
///
/// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
/// class MatrixElement
/// {
/// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
/// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
/// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
/// // public uint MatrixColumnIndex;
/// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
/// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
/// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
/// // Matrix column index starts from 0 and is at most n-1.
/// [KeyType(n)]
/// public uint MatrixColumnIndex;
/// // Matrix row index starts from 0 and is at most m-1.
/// [KeyType(m)]
/// public uint MatrixRowIndex;
/// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
/// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
/// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
/// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
/// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
/// <para>For users interested in the mathematical details, please see the references below.</para>
/// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
/// <list type = 'bullet'>
/// <item>
/// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
/// <item>
/// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
/// </item>
/// <item>
/// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
/// </item>
/// </list>
/// </remarks>
/// <example>
Expand Down
84 changes: 45 additions & 39 deletions src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
/// </summary>
internal sealed class SafeTrainingAndModelBuffer : IDisposable
{
[StructLayout(LayoutKind.Explicit)]
[StructLayout(LayoutKind.Sequential)]
private struct MFNode
{
[FieldOffset(0)]
/// <summary>
/// Row index.
/// </summary>
public int U;
[FieldOffset(4)]

/// <summary>
/// Column index;
/// </summary>
public int V;
[FieldOffset(8)]

/// <summary>
/// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
/// </summary>
public float R;
}

[StructLayout(LayoutKind.Explicit)]
[StructLayout(LayoutKind.Sequential)]
private unsafe struct MFProblem
{
[FieldOffset(0)]
/// <summary>
/// Number of rows.
/// </summary>
public int M;
[FieldOffset(4)]

/// <summary>
/// Number of columns.
/// </summary>
public int N;
[FieldOffset(8)]

/// <summary>
/// Number of specified matrix elements in <see cref="R"/>.
/// </summary>
public long Nnz;
[FieldOffset(16)]

/// <summary>
/// Specified matrix elements.
/// </summary>
public MFNode* R;
}

[StructLayout(LayoutKind.Explicit)]
[StructLayout(LayoutKind.Sequential)]
private struct MFParameter
{
/// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
/// Fun 12 is solved by a coordinate descent method while other functions invoke
/// a stochastic gradient method.
/// </summary>
[FieldOffset(0)]
public int Fun;

/// <summary>
/// Rank of factor matrices.
/// </summary>
[FieldOffset(4)]
public int K;

/// <summary>
/// Number of threads which can be used for training.
/// </summary>
[FieldOffset(8)]
public int NrThreads;

/// <summary>
/// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
/// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
/// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
/// </summary>
[FieldOffset(12)]
public int NrBins;

/// <summary>
/// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
/// </summary>
[FieldOffset(16)]
public int NrIters;

/// <summary>
/// L1-norm regularization coefficient of left factor matrix.
/// </summary>
[FieldOffset(20)]
public float LambdaP1;

/// <summary>
/// L2-norm regularization coefficient of left factor matrix.
/// </summary>
[FieldOffset(24)]
public float LambdaP2;

/// <summary>
/// L1-norm regularization coefficient of right factor matrix.
/// </summary>
[FieldOffset(28)]
public float LambdaQ1;

/// <summary>
/// L2-norm regularization coefficient of right factor matrix.
/// </summary>
[FieldOffset(32)]
public float LambdaQ2;

/// <summary>
/// Learning rate of LIBMF's stochastic gradient method.
/// </summary>
[FieldOffset(36)]
public float Eta;

/// <summary>
/// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
/// </summary>
[FieldOffset(40)]
public float Alpha;

/// <summary>
/// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
/// </summary>
[FieldOffset(44)]
public float C;

/// <summary>
/// Specify if the factor matrices should be non-negative.
/// </summary>
[FieldOffset(48)]
public int DoNmf;
public byte DoNmf;

/// <summary>
/// Set to true so that LIBMF may produce less information to STDOUT.
/// </summary>
[FieldOffset(52)]
public int Quiet;
public byte Quiet;

/// <summary>
/// Set to false so that LIBMF may reuse and modifiy the data passed in.
/// </summary>
[FieldOffset(56)]
public int CopyData;
public byte CopyData;
}

[StructLayout(LayoutKind.Explicit)]
[StructLayout(LayoutKind.Sequential)]
private unsafe struct MFModel
{
[FieldOffset(0)]
/// <summary>
/// See <see cref="MFParameter.Fun"/>.
/// </summary>
public int Fun;

/// <summary>
/// Number of rows in the training matrix.
/// </summary>
[FieldOffset(4)]
public int M;

/// <summary>
/// Number of columns in the training matrix.
/// </summary>
[FieldOffset(8)]
public int N;

/// <summary>
/// Rank of factor matrices.
/// </summary>
[FieldOffset(12)]
public int K;

/// <summary>
/// Average value in the training matrix.
/// </summary>
[FieldOffset(16)]
public float B;

/// <summary>
/// Left factor matrix. Its shape is M-by-K stored in row-major format.
/// </summary>
[FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
public float* P;

/// <summary>
/// Right factor matrix. Its shape is N-by-K stored in row-major format.
/// </summary>
[FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
public float* Q;
}

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
_mfParam.Eta = (float)eta;
_mfParam.Alpha = (float)alpha;
_mfParam.C = (float)c;
_mfParam.DoNmf = doNmf ? 1 : 0;
_mfParam.Quiet = quiet ? 1 : 0;
_mfParam.CopyData = copyData ? 1 : 0;
_mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
_mfParam.Quiet = quiet ? (byte)1 : (byte)0;
_mfParam.CopyData = copyData ? (byte)1 : (byte)0;
}

~SafeTrainingAndModelBuffer()
Expand Down
21 changes: 17 additions & 4 deletions src/Native/MatrixFactorizationNative/CMakeLists.txt
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
UnmanagedMemory.cpp
libmf/mf.cpp
)
if(UNIX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
if (APPLE)
include_directories("/usr/local/opt/libomp/include")
link_directories("/usr/local/opt/libomp/lib")
endif()
endif()

if(WIN32)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Add MF fixes to release branch#3292

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wschin
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from

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
Expand Down

21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Add MF fixes to release branch#3292

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
Expand Down

21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

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Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Add MF fixes to release branch#3292

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 Closed

wschin
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commits into

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

File filter

Filter by extension
Filter by extension
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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
Expand Down

21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
Expand Down

21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Add MF fixes to release branch#3292

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 Closed

wschin
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from

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

File filter

Filter by extension
Filter by extension
Conversations

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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
Expand Down

21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
Expand Down

21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

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Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Add MF fixes to release branch#3292

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 Closed

wschin
wants to merge
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commits into

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Add MF fixes to release branch
#3292
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7d842d8

Fix marshalling of bool flags in MF (#3210)


wschin Apr 5, 2019
fb0b69c

Polish marshalling of MF model and MF problem and enable 32-bit tests…


wschin Apr 8, 2019
85f1f56

Ensable the uses of SSE and OpenMP in LIBMF (#3265)


wschin Apr 10, 2019
3668b3e

Update description about key type and add one more reference (#3170)


wschin Apr 5, 2019
e73749d

Try buid LIBMF with OpenMP again (#3297)


wschin Apr 11, 2019

File filter

Filter by extension
Filter by extension
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24 changes: 11 additions & 13 deletions
src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers
 /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use:
 /// </para>
 /// <code language="csharp">
 /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that
 /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0.
 /// // Similarly, for 1-based indices, firstRowIndex could be 1.
 /// const int firstRowIndex = 1;
 /// const int firstColumnIndex = 1;
 /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system
 /// // is 0-based.
 /// const int m = 60;
 /// const int n = 100;
 ///
 /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix.
 /// class MatrixElement
 /// {
 /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1.
 /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys.
 /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)]
 /// // public uint MatrixColumnIndex;
 /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1.
 /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys.
 /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)]
 /// // Matrix column index starts from 0 and is at most n-1.
 /// [KeyType(n)]
 /// public uint MatrixColumnIndex;
 /// // Matrix row index starts from 0 and is at most m-1.
 /// [KeyType(m)]
 /// public uint MatrixRowIndex;
 /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
 /// public float Value;
Expand All@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers
 /// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements
 /// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i>
 /// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>.
 /// <para>For users interested in the mathematical details, please see the references below.</para>
 /// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para>
 /// <list type = 'bullet'>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description>
Expand All@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description>
 /// </item>
 /// <item>
 /// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description>
 /// </item>
 /// </list>
 /// </remarks>
 /// <example>
Expand Down

84 changes: 45 additions & 39 deletions
src/Microsoft.ML.Recommender/SafeTrainingAndModelBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -17,31 +17,50 @@ namespace Microsoft.ML.Recommender.Internal
 /// </summary>
 internal sealed class SafeTrainingAndModelBuffer : IDisposable
 {
 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFNode
 {
 [FieldOffset(0)]
 /// <summary>
 /// Row index.
 /// </summary>
 public int U;
 [FieldOffset(4)]

 /// <summary>
 /// Column index;
 /// </summary>
 public int V;
 [FieldOffset(8)]

 /// <summary>
 /// Matrix element's value at <see cref="U"/>-th row and <see cref="V"/>-th column.
 /// </summary>
 public float R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFProblem
 {
 [FieldOffset(0)]
 /// <summary>
 /// Number of rows.
 /// </summary>
 public int M;
 [FieldOffset(4)]

 /// <summary>
 /// Number of columns.
 /// </summary>
 public int N;
 [FieldOffset(8)]

 /// <summary>
 /// Number of specified matrix elements in <see cref="R"/>.
 /// </summary>
 public long Nnz;
 [FieldOffset(16)]

 /// <summary>
 /// Specified matrix elements.
 /// </summary>
 public MFNode* R;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private struct MFParameter
 {
 /// <summary>
Expand All@@ -58,130 +77,117 @@ private struct MFParameter
 /// Fun 12 is solved by a coordinate descent method while other functions invoke
 /// a stochastic gradient method.
 /// </summary>
 [FieldOffset(0)]
 public int Fun;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(4)]
 public int K;

 /// <summary>
 /// Number of threads which can be used for training.
 /// </summary>
 [FieldOffset(8)]
 public int NrThreads;

 /// <summary>
 /// Number of blocks that the training matrix is divided into. The parallel stochastic gradient
 /// method in LIBMF processes assigns each thread a block at one time. The ratings in one block
 /// would be sequentially accessed (not randomaly accessed like standard stochastic gradient methods).
 /// </summary>
 [FieldOffset(12)]
 public int NrBins;

 /// <summary>
 /// Number of training iteration. At one iteration, all values in the training matrix are roughly accessed once.
 /// </summary>
 [FieldOffset(16)]
 public int NrIters;

 /// <summary>
 /// L1-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(20)]
 public float LambdaP1;

 /// <summary>
 /// L2-norm regularization coefficient of left factor matrix.
 /// </summary>
 [FieldOffset(24)]
 public float LambdaP2;

 /// <summary>
 /// L1-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(28)]
 public float LambdaQ1;

 /// <summary>
 /// L2-norm regularization coefficient of right factor matrix.
 /// </summary>
 [FieldOffset(32)]
 public float LambdaQ2;

 /// <summary>
 /// Learning rate of LIBMF's stochastic gradient method.
 /// </summary>
 [FieldOffset(36)]
 public float Eta;

 /// <summary>
 /// Coefficient of loss function on unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(40)]
 public float Alpha;

 /// <summary>
 /// Desired value of unobserved entries in the training matrix. It's used only with fun=12.
 /// </summary>
 [FieldOffset(44)]
 public float C;

 /// <summary>
 /// Specify if the factor matrices should be non-negative.
 /// </summary>
 [FieldOffset(48)]
 public int DoNmf;
 public byte DoNmf;

 /// <summary>
 /// Set to true so that LIBMF may produce less information to STDOUT.
 /// </summary>
 [FieldOffset(52)]
 public int Quiet;
 public byte Quiet;

 /// <summary>
 /// Set to false so that LIBMF may reuse and modifiy the data passed in.
 /// </summary>
 [FieldOffset(56)]
 public int CopyData;
 public byte CopyData;
 }

 [StructLayout(LayoutKind.Explicit)]
 [StructLayout(LayoutKind.Sequential)]
 private unsafe struct MFModel
 {
 [FieldOffset(0)]
 /// <summary>
 /// See <see cref="MFParameter.Fun"/>.
 /// </summary>
 public int Fun;

 /// <summary>
 /// Number of rows in the training matrix.
 /// </summary>
 [FieldOffset(4)]
 public int M;

 /// <summary>
 /// Number of columns in the training matrix.
 /// </summary>
 [FieldOffset(8)]
 public int N;

 /// <summary>
 /// Rank of factor matrices.
 /// </summary>
 [FieldOffset(12)]
 public int K;

 /// <summary>
 /// Average value in the training matrix.
 /// </summary>
 [FieldOffset(16)]
 public float B;

 /// <summary>
 /// Left factor matrix. Its shape is M-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(24)] // pointer is 8-byte on 64-bit machine.
 public float* P;

 /// <summary>
 /// Right factor matrix. Its shape is N-by-K stored in row-major format.
 /// </summary>
 [FieldOffset(32)] // pointer is 8-byte on 64-bit machine.
 public float* Q;
 }

Expand DownExpand Up@@ -223,9 +229,9 @@ public SafeTrainingAndModelBuffer(IHostEnvironment env, int fun, int k, int nrTh
 _mfParam.Eta = (float)eta;
 _mfParam.Alpha = (float)alpha;
 _mfParam.C = (float)c;
 _mfParam.DoNmf = doNmf ? 1 : 0;
 _mfParam.Quiet = quiet ? 1 : 0;
 _mfParam.CopyData = copyData ? 1 : 0;
 _mfParam.DoNmf = doNmf ? (byte)1 : (byte)0;
 _mfParam.Quiet = quiet ? (byte)1 : (byte)0;
 _mfParam.CopyData = copyData ? (byte)1 : (byte)0;
 }

 ~SafeTrainingAndModelBuffer()
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21 changes: 17 additions & 4 deletions
src/Native/MatrixFactorizationNative/CMakeLists.txt

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Original file line numberDiff line numberDiff line change
@@ -1,12 +1,25 @@
project (MatrixFactorizationNative)
add_definitions(-D_SCL_SECURE_NO_WARNINGS)
add_definitions(-DUSEOMP)
add_definitions(-DUSESSE)

include_directories(libmf)

set(SOURCES
 UnmanagedMemory.cpp
 libmf/mf.cpp
)
if(UNIX)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -pthread -std=c++0x -march=native")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fopenmp")
 if (APPLE)
 include_directories("/usr/local/opt/libomp/include")
 link_directories("/usr/local/opt/libomp/lib")
 endif()
endif()

if(WIN32)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /nologo /O2 /EHsc /D \"_CRT_SECURE_NO_DEPRECATE\" /openmp")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

set(SOURCES UnmanagedMemory.cpp libmf/mf.cpp)

if(NOT WIN32)
 list(APPEND SOURCES ${VERSION_FILE_PATH})
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