Speedup exhaustive_L2sqr_blas for AVX2, ARM NEON and AVX512 (facebookresearch#2568)

Summary:
Pull Request resolved: facebookresearch#2568

Add a fused kernel for exhaustive_L2sqr_blas() call that combines a computation of dot product and the search for the nearest centroid. As a result, no temporary dot product values are written and read in RAM.

Speeds up the training of PQx[1] indices for dsub = 1, 2, 4, 8, and the effect is higher for higher values of [1]. AVX512 version provides additional overloads for dsub = 12, 16.

The speedup is also beneficial for higher values of pq.cp.max_points_per_centroid (which is 256 by default).

Speeds up IVFPQ training as well.

AVX512 kernel is not enabled, but I've seen it speeding up the training TWICE versus AVX2 version. So, please feel free to use it by enabling AVX512 manually.

Reviewed By: mdouze

Differential Revision: D41166766

fbshipit-source-id: f6e81fe3db9647a761ac1552fba620155e316f36

Author: Alexandr Guzhva

faiss/CMakeLists.txt

@@ -86,6 +86,9 @@ set(FAISS_SRC
   utils/quantize_lut.cpp
   utils/random.cpp
   utils/utils.cpp
+  utils/distances_fused/avx512.cpp
+  utils/distances_fused/distances_fused.cpp
+  utils/distances_fused/simdlib_based.cpp
 )
 
 set(FAISS_HEADERS
@@ -187,6 +190,9 @@ set(FAISS_HEADERS
   utils/simdlib_emulated.h
   utils/simdlib_neon.h
   utils/utils.h
+  utils/distances_fused/avx512.h
+  utils/distances_fused/distances_fused.h
+  utils/distances_fused/simdlib_based.h
 )
 
 if(NOT WIN32)

faiss/utils/distances.cpp

@@ -26,6 +26,8 @@
 #include <faiss/impl/IDSelector.h>
 #include <faiss/impl/ResultHandler.h>
 
+#include <faiss/utils/distances_fused/distances_fused.h>
+
 #ifndef FINTEGER
 #define FINTEGER long
 #endif
@@ -229,7 +231,7 @@ void exhaustive_inner_product_blas(
 // distance correction is an operator that can be applied to transform
 // the distances
 template <class ResultHandler>
-void exhaustive_L2sqr_blas(
+void exhaustive_L2sqr_blas_default_impl(
         const float* x,
         const float* y,
         size_t d,
@@ -311,10 +313,20 @@ void exhaustive_L2sqr_blas(
     }
 }
 
+template <class ResultHandler>
+void exhaustive_L2sqr_blas(
+        const float* x,
+        const float* y,
+        size_t d,
+        size_t nx,
+        size_t ny,
+        ResultHandler& res,
+        const float* y_norms = nullptr) {
+    exhaustive_L2sqr_blas_default_impl(x, y, d, nx, ny, res);
+}
+
 #ifdef __AVX2__
-// an override for AVX2 if only a single closest point is needed.
-template <>
-void exhaustive_L2sqr_blas<SingleBestResultHandler<CMax<float, int64_t>>>(
+void exhaustive_L2sqr_blas_cmax_avx2(
         const float* x,
         const float* y,
         size_t d,
@@ -513,11 +525,53 @@ void exhaustive_L2sqr_blas<SingleBestResultHandler<CMax<float, int64_t>>>(
                 res.add_result(i, current_min_distance, current_min_index);
             }
         }
+        // Does nothing for SingleBestResultHandler, but
+        // keeping the call for the consistency.
+        res.end_multiple();
         InterruptCallback::check();
     }
 }
 #endif
 
+// an override if only a single closest point is needed
+template <>
+void exhaustive_L2sqr_blas<SingleBestResultHandler<CMax<float, int64_t>>>(
+        const float* x,
+        const float* y,
+        size_t d,
+        size_t nx,
+        size_t ny,
+        SingleBestResultHandler<CMax<float, int64_t>>& res,
+        const float* y_norms) {
+#if defined(__AVX2__)
+    // use a faster fused kernel if available
+    if (exhaustive_L2sqr_fused_cmax(x, y, d, nx, ny, res, y_norms)) {
+        // the kernel is available and it is complete, we're done.
+        return;
+    }
+
+    // run the specialized AVX2 implementation
+    exhaustive_L2sqr_blas_cmax_avx2(x, y, d, nx, ny, res, y_norms);
+
+#elif defined(__aarch64__)
+    // use a faster fused kernel if available
+    if (exhaustive_L2sqr_fused_cmax(x, y, d, nx, ny, res, y_norms)) {
+        // the kernel is available and it is complete, we're done.
+        return;
+    }
+
+    // run the default implementation
+    exhaustive_L2sqr_blas_default_impl<
+            SingleBestResultHandler<CMax<float, int64_t>>>(
+            x, y, d, nx, ny, res, y_norms);
+#else
+    // run the default implementation
+    exhaustive_L2sqr_blas_default_impl<
+            SingleBestResultHandler<CMax<float, int64_t>>>(
+            x, y, d, nx, ny, res, y_norms);
+#endif
+}
+
 template <class ResultHandler>
 void knn_L2sqr_select(
         const float* x,