Merge pull request #3486 from stweil/tfloat

Add TFloat data type for neural network

Author: amitdo

Diff for: src/arch/dotproductavx.cpp

@@ -29,6 +29,28 @@ namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
 // Uses Intel AVX intrinsics to access the SIMD instruction set.
+#if defined(FAST_FLOAT)
+float DotProductAVX(const float *u, const float *v, int n) {
+  const unsigned quot = n / 8;
+  const unsigned rem = n % 8;
+  __m256 t0 = _mm256_setzero_ps();
+  for (unsigned k = 0; k < quot; k++) {
+    __m256 f0 = _mm256_loadu_ps(u);
+    __m256 f1 = _mm256_loadu_ps(v);
+    f0 = _mm256_mul_ps(f0, f1);
+    t0 = _mm256_add_ps(t0, f0);
+    u += 8;
+    v += 8;
+  }
+  alignas(32) float tmp[8];
+  _mm256_store_ps(tmp, t0);
+  float result = tmp[0] + tmp[1] + tmp[2] + tmp[3] + tmp[4] + tmp[5] + tmp[6] + tmp[7];
+  for (unsigned k = 0; k < rem; k++) {
+    result += *u++ * *v++;
+  }
+  return result;
+}
+#else
 double DotProductAVX(const double *u, const double *v, int n) {
   const unsigned quot = n / 8;
   const unsigned rem = n % 8;
@@ -57,6 +79,7 @@ double DotProductAVX(const double *u, const double *v, int n) {
   }
   return result;
 }
+#endif
 
 } // namespace tesseract.
 

Diff for: src/arch/dotproductfma.cpp

@@ -29,6 +29,34 @@ namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
 // Uses Intel FMA intrinsics to access the SIMD instruction set.
+#if defined(FAST_FLOAT)
+float DotProductFMA(const float *u, const float *v, int n) {
+  const unsigned quot = n / 16;
+  const unsigned rem = n % 16;
+  __m256 t0 = _mm256_setzero_ps();
+  __m256 t1 = _mm256_setzero_ps();
+  for (unsigned k = 0; k < quot; k++) {
+    __m256 f0 = _mm256_loadu_ps(u);
+    __m256 f1 = _mm256_loadu_ps(v);
+    t0 = _mm256_fmadd_ps(f0, f1, t0);
+    u += 8;
+    v += 8;
+    __m256 f2 = _mm256_loadu_ps(u);
+    __m256 f3 = _mm256_loadu_ps(v);
+    t1 = _mm256_fmadd_ps(f2, f3, t1);
+    u += 8;
+    v += 8;
+  }
+  t0 = _mm256_hadd_ps(t0, t1);
+  alignas(32) float tmp[8];
+  _mm256_store_ps(tmp, t0);
+  float result = tmp[0] + tmp[1] + tmp[2] + tmp[3] + tmp[4] + tmp[5] + tmp[6] + tmp[7];
+  for (unsigned k = 0; k < rem; k++) {
+    result += *u++ * *v++;
+  }
+  return result;
+}
+#else
 double DotProductFMA(const double *u, const double *v, int n) {
   const unsigned quot = n / 8;
   const unsigned rem = n % 8;
@@ -55,6 +83,7 @@ double DotProductFMA(const double *u, const double *v, int n) {
   }
   return result;
 }
+#endif
 
 } // namespace tesseract.
 

Diff for: src/arch/dotproductsse.cpp

@@ -30,6 +30,66 @@ namespace tesseract {
 
 // Computes and returns the dot product of the n-vectors u and v.
 // Uses Intel SSE intrinsics to access the SIMD instruction set.
+#if defined(FAST_FLOAT)
+float DotProductSSE(const float *u, const float *v, int n) {
+  int max_offset = n - 4;
+  int offset = 0;
+  // Accumulate a set of 4 sums in sum, by loading pairs of 4 values from u and
+  // v, and multiplying them together in parallel.
+  __m128 sum = _mm_setzero_ps();
+  if (offset <= max_offset) {
+    offset = 4;
+    // Aligned load is reputedly faster but requires 16 byte aligned input.
+    if ((reinterpret_cast<uintptr_t>(u) & 15) == 0 &&
+        (reinterpret_cast<uintptr_t>(v) & 15) == 0) {
+      // Use aligned load.
+      sum = _mm_load_ps(u);
+      __m128 floats2 = _mm_load_ps(v);
+      // Multiply.
+      sum = _mm_mul_ps(sum, floats2);
+      while (offset <= max_offset) {
+        __m128 floats1 = _mm_load_ps(u + offset);
+        floats2 = _mm_load_ps(v + offset);
+        floats1 = _mm_mul_ps(floats1, floats2);
+        sum = _mm_add_ps(sum, floats1);
+        offset += 4;
+      }
+    } else {
+      // Use unaligned load.
+      sum = _mm_loadu_ps(u);
+      __m128 floats2 = _mm_loadu_ps(v);
+      // Multiply.
+      sum = _mm_mul_ps(sum, floats2);
+      while (offset <= max_offset) {
+        __m128 floats1 = _mm_loadu_ps(u + offset);
+        floats2 = _mm_loadu_ps(v + offset);
+        floats1 = _mm_mul_ps(floats1, floats2);
+        sum = _mm_add_ps(sum, floats1);
+        offset += 4;
+      }
+    }
+  }
+  // Add the 4 sums in sum horizontally.
+#if 0
+	alignas(32) float tmp[4];
+	_mm_store_ps(tmp, sum);
+	float result = tmp[0] + tmp[1] + tmp[2] + tmp[3];
+#else
+  __m128 zero = _mm_setzero_ps();
+  // https://www.felixcloutier.com/x86/haddps
+  sum = _mm_hadd_ps(sum, zero);
+  sum = _mm_hadd_ps(sum, zero);
+  // Extract the low result.
+  float result = _mm_cvtss_f32(sum);
+#endif
+  // Add on any left-over products.
+  while (offset < n) {
+    result += u[offset] * v[offset];
+    ++offset;
+  }
+  return result;
+}
+#else
 double DotProductSSE(const double *u, const double *v, int n) {
   int max_offset = n - 2;
   int offset = 0;
@@ -39,7 +99,8 @@ double DotProductSSE(const double *u, const double *v, int n) {
   if (offset <= max_offset) {
     offset = 2;
     // Aligned load is reputedly faster but requires 16 byte aligned input.
-    if ((reinterpret_cast<uintptr_t>(u) & 15) == 0 && (reinterpret_cast<uintptr_t>(v) & 15) == 0) {
+    if ((reinterpret_cast<uintptr_t>(u) & 15) == 0 &&
+        (reinterpret_cast<uintptr_t>(v) & 15) == 0) {
       // Use aligned load.
       sum = _mm_load_pd(u);
       __m128d floats2 = _mm_load_pd(v);
@@ -78,6 +139,7 @@ double DotProductSSE(const double *u, const double *v, int n) {
   }
   return result;
 }
+#endif
 
 } // namespace tesseract.
 

Diff for: src/arch/intsimdmatrix.h

@@ -115,7 +115,7 @@ struct TESS_API IntSimdMatrix {
   static const IntSimdMatrix *intSimdMatrix;
   // Only available with NEON.
   static const IntSimdMatrix intSimdMatrixNEON;
-  // Only available with AVX2 / SSE.
+  // Only available with AVX2 / AVX / FMA / SSE.
   static const IntSimdMatrix intSimdMatrixAVX2;
   static const IntSimdMatrix intSimdMatrixSSE;
 };