Set best or user selected IntSimdMatrix

Signed-off-by: Stefan Weil <sw@weilnetz.de>

Author: stweil

src/arch/intsimdmatrix.cpp

@@ -23,25 +23,10 @@
 
 namespace tesseract {
 
+const IntSimdMatrix* IntSimdMatrix::intSimdMatrix = nullptr;
 const IntSimdMatrix IntSimdMatrix::IntSimdMatrixNative =
   IntSimdMatrix(1, 1, 1, 1, 1, {});
 
-// Factory makes and returns an IntSimdMatrix (sub)class of the best
-// available type for the current architecture.
-/* static */
-const IntSimdMatrix* IntSimdMatrix::GetFastestMultiplier() {
-  const IntSimdMatrix* multiplier;
-  if (SIMDDetect::IsAVX2Available()) {
-    multiplier = &IntSimdMatrixAVX2;
-  } else if (SIMDDetect::IsSSEAvailable()) {
-    multiplier = &IntSimdMatrixSSE;
-  } else {
-    // Default c++ implementation.
-    multiplier = &IntSimdMatrixNative;
-  }
-  return multiplier;
-}
-
 // Computes a reshaped copy of the weight matrix w. If there are no
 // partial_funcs_, it does nothing.
 void IntSimdMatrix::Init(const GENERIC_2D_ARRAY<int8_t>& w, std::vector<int8_t>& shaped_w) const {

src/arch/intsimdmatrix.h

@@ -85,10 +85,6 @@ class IntSimdMatrix {
   partial_funcs_(partial_funcs)
   {}
 
-  // Factory makes and returns an IntSimdMatrix (sub)class of the best
-  // available type for the current architecture.
-  static const IntSimdMatrix* GetFastestMultiplier();
-
   // Computes a reshaped copy of the weight matrix w. If there are no
   // partial_funcs_, it does nothing.
   void Init(const GENERIC_2D_ARRAY<int8_t>& w, std::vector<int8_t>& shaped_w) const;
@@ -115,6 +111,7 @@ class IntSimdMatrix {
                        const GenericVector<double>& scales, const int8_t* u,
                        double* v) const;
 
+  static const IntSimdMatrix* intSimdMatrix;
   static const IntSimdMatrix IntSimdMatrixAVX2;
   static const IntSimdMatrix IntSimdMatrixSSE;
   static const IntSimdMatrix IntSimdMatrixNative;

src/arch/simddetect.cpp

@@ -19,6 +19,7 @@
 #include "dotproduct.h"
 #include "dotproductavx.h"
 #include "dotproductsse.h"
+#include "intsimdmatrix.h"   // for IntSimdMatrix
 #include "params.h"   // for STRING_VAR
 #include "tprintf.h"  // for tprintf
 
@@ -68,8 +69,9 @@ static double DotProductGeneric(const double* u, const double* v, int n) {
   return total;
 }
 
-static void SetDotProduct(DotProductFunction function) {
-  DotProduct = function;
+static void SetDotProduct(DotProductFunction f, const IntSimdMatrix* m = nullptr) {
+  DotProduct = f;
+  IntSimdMatrix::intSimdMatrix = m;
 }
 
 // Constructor.
@@ -126,12 +128,12 @@ SIMDDetect::SIMDDetect() {
 #if defined(AVX)
   } else if (avx_available_) {
     // AVX detected.
-    SetDotProduct(DotProductAVX);
+    SetDotProduct(DotProductAVX, &IntSimdMatrix::IntSimdMatrixAVX2);
 #endif
 #if defined(SSE4_1)
   } else if (sse_available_) {
     // SSE detected.
-    SetDotProduct(DotProductSSE);
+    SetDotProduct(DotProductSSE, &IntSimdMatrix::IntSimdMatrixSSE);
 #endif
   }
 }
@@ -153,13 +155,13 @@ void SIMDDetect::Update() {
 #if defined(AVX)
   } else if (!strcmp(dotproduct.string(), "avx")) {
     // AVX selected by config variable.
-    SetDotProduct(DotProductAVX);
+    SetDotProduct(DotProductAVX, &IntSimdMatrix::IntSimdMatrixAVX2);
     dotproduct_method = "avx";
 #endif
 #if defined(SSE4_1)
   } else if (!strcmp(dotproduct.string(), "sse")) {
     // SSE selected by config variable.
-    SetDotProduct(DotProductSSE);
+    SetDotProduct(DotProductSSE, &IntSimdMatrix::IntSimdMatrixSSE);
     dotproduct_method = "sse";
 #endif
   } else {

src/lstm/networkio.cpp

@@ -31,11 +31,6 @@ const float kMinCertainty = -20.0f;
 // Probability corresponding to kMinCertainty.
 const float kMinProb = exp(kMinCertainty);
 
-// Holds the optimal integer multiplier for this machine.
-// This is a leaked, lazily initialized singleton, and is used for computing
-// padding to apply to i_ for SIMD use.
-const IntSimdMatrix* NetworkIO::multiplier_ = nullptr;
-
 // Resizes to a specific size as a 2-d temp buffer. No batches, no y-dim.
 void NetworkIO::Resize2d(bool int_mode, int width, int num_features) {
   stride_map_ = StrideMap();
@@ -985,9 +980,12 @@ void NetworkIO::ClipVector(int t, float range) {
 // for the SIMD operations to be safe.
 /* static */
 int NetworkIO::GetPadding(int num_features) {
-  if (multiplier_ == nullptr)
-    multiplier_ = IntSimdMatrix::GetFastestMultiplier();
-  return multiplier_->RoundInputs(num_features) - num_features;
+  int padding = 0;
+  if (IntSimdMatrix::intSimdMatrix) {
+    padding =
+      IntSimdMatrix::intSimdMatrix->RoundInputs(num_features) - num_features;
+  }
+  return padding;
 }
 
 }  // namespace tesseract.

src/lstm/networkio.h

@@ -338,10 +338,6 @@ class NetworkIO {
   bool int_mode_;
   // Stride for 2d input data.
   StrideMap stride_map_;
-  // Holds the optimal integer multiplier for this machine.
-  // This is a leaked, lazily initialized singleton, and is used for computing
-  // padding to apply to i_ for SIMD use.
-  static const IntSimdMatrix* multiplier_;
 };
 
 }  // namespace tesseract.

src/lstm/weightmatrix.cpp

@@ -143,8 +143,8 @@ void WeightMatrix::ConvertToInt() {
   }
   wf_.Resize(1, 1, 0.0);
   int_mode_ = true;
-  multiplier_ = IntSimdMatrix::GetFastestMultiplier();
-  multiplier_->Init(wi_, shaped_w_);
+  if (IntSimdMatrix::intSimdMatrix)
+    IntSimdMatrix::intSimdMatrix->Init(wi_, shaped_w_);
 }
 
 // Allocates any needed memory for running Backward, and zeroes the deltas,
@@ -196,8 +196,8 @@ bool WeightMatrix::DeSerialize(bool training, TFile* fp) {
   if (int_mode_) {
     if (!wi_.DeSerialize(fp)) return false;
     if (!scales_.DeSerialize(fp)) return false;
-    multiplier_ = IntSimdMatrix::GetFastestMultiplier();
-    multiplier_->Init(wi_, shaped_w_);
+    if (IntSimdMatrix::intSimdMatrix)
+      IntSimdMatrix::intSimdMatrix->Init(wi_, shaped_w_);
   } else {
     if (!wf_.DeSerialize(fp)) return false;
     if (training) {
@@ -245,8 +245,7 @@ void WeightMatrix::MatrixDotVector(const double* u, double* v) const {
 
 void WeightMatrix::MatrixDotVector(const int8_t* u, double* v) const {
   assert(int_mode_);
-  assert(multiplier_ != nullptr);
-  multiplier_->MatrixDotVector(wi_, shaped_w_, scales_, u, v);
+  IntSimdMatrix::intSimdMatrix->MatrixDotVector(wi_, shaped_w_, scales_, u, v);
 }
 
 // MatrixDotVector for peep weights, MultiplyAccumulate adds the

src/lstm/weightmatrix.h

@@ -64,7 +64,7 @@ class TransposedArray : public GENERIC_2D_ARRAY<double> {
 // backward steps with the matrix and updates to the weights.
 class WeightMatrix {
  public:
-  WeightMatrix() : int_mode_(false), use_adam_(false), multiplier_(nullptr) {}
+  WeightMatrix() : int_mode_(false), use_adam_(false) {}
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
   // Note the order is outputs, inputs, as this is the order of indices to
@@ -85,13 +85,13 @@ class WeightMatrix {
   // Scale so the max absolute value becomes INT8_MAX.
   // Round to integer.
   // Store a multiplicative scale factor (as a float) that will reproduce
-  //   the original value, subject to rounding errors.
+  // the original value, subject to rounding errors.
   void ConvertToInt();
   // Returns the size rounded up to an internal factor used by the SIMD
   // implementation for its input.
   int RoundInputs(int size) const {
-    if (multiplier_ == nullptr) return size;
-    return multiplier_->RoundInputs(size);
+    if (!int_mode_ || !IntSimdMatrix::intSimdMatrix) return size;
+    return IntSimdMatrix::intSimdMatrix->RoundInputs(size);
   }
 
   // Accessors.
@@ -178,8 +178,6 @@ class WeightMatrix {
   GENERIC_2D_ARRAY<double> dw_sq_sum_;
   // The weights matrix reorganized in whatever way suits this instance.
   std::vector<int8_t> shaped_w_;
-  // Holds the optimal integer multiplier for this machine.
-  const IntSimdMatrix* multiplier_;
 };
 
 }  // namespace tesseract.