Merge branch 'matmul' into 'master'

matmul tiling impl.

See merge request !283

mace/kernels/matmul.h

@@ -5,14 +5,128 @@
 #ifndef MACE_KERNELS_MATMUL_H_
 #define MACE_KERNELS_MATMUL_H_
 
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+#include <arm_neon.h>
+#endif
+
+#include <string>
+#include <vector>
+#include <algorithm>
+
 #include "mace/core/future.h"
 #include "mace/core/runtime/opencl/cl2_header.h"
 #include "mace/core/tensor.h"
+#include "mace/utils/utils.h"
 
 namespace mace {
 namespace kernels {
 
-template <DeviceType D, typename T>
+namespace {
+template<typename T,
+  int register_tile_size,
+  int h_count,
+  int w_count,
+  int k_count>
+inline void MatMulKernelFunc(const T *A,
+                             const T *B,
+                             T *C,
+                             index_t offset_h,
+                             index_t offset_w,
+                             index_t offset_k,
+                             index_t stride_h,
+                             index_t stride_w,
+                             index_t stride_k) {
+  T a_tmp[register_tile_size][register_tile_size] = {0};
+  T b_tmp[register_tile_size][register_tile_size] = {0};
+  T c_tmp[register_tile_size][register_tile_size] = {0};
+
+  for (int h = 0; h < h_count; ++h) {
+    for (int k = 0; k < k_count; ++k) {
+      a_tmp[h][k] = A[(offset_h + h) * stride_k + (offset_k + k)];
+    }
+  }
+  for (int k = 0; k < k_count; ++k) {
+    for (int w = 0; w < w_count; ++w) {
+      b_tmp[k][w] = B[(offset_k + k) * stride_w + (offset_w + w)];
+    }
+  }
+
+#if defined(MACE_ENABLE_NEON) && defined(__aarch64__)
+  static_assert(register_tile_size == 4, "register tile size must be 4");
+  float32x4_t a_dup;
+  float32x4_t b_vec[4] =
+    {vld1q_f32(b_tmp[0]), vld1q_f32(b_tmp[1]), vld1q_f32(b_tmp[2]),
+     vld1q_f32(b_tmp[3])};
+  float32x4_t
+    c_vec[4] = {vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0), vdupq_n_f32(0)};
+
+  for (int h = 0; h < register_tile_size; ++h) {
+    for (int k = 0; k < register_tile_size; ++k) {
+      a_dup = vdupq_n_f32(a_tmp[h][k]);
+      c_vec[h] = vfmaq_f32(c_vec[h], a_dup, b_vec[k]);
+    }
+  }
+
+  for (int h = 0; h < register_tile_size; ++h) {
+    vst1q_f32(c_tmp[h], c_vec[h]);
+  }
+
+#else
+  for (int h = 0; h < register_tile_size; ++h) {
+    for (int w = 0; w < register_tile_size; ++w) {
+      for (int k = 0; k < register_tile_size; ++k) {
+        c_tmp[h][w] += a_tmp[h][k] * b_tmp[k][w];
+      }
+    }
+  }
+#endif
+
+  for (int h = 0; h < h_count; ++h) {
+    for (int w = 0; w < w_count; ++w) {
+      C[(offset_h + h) * stride_w + (offset_w + w)] += c_tmp[h][w];
+    }
+  }
+}
+}  // namespace
+
+#define CASE_K_MATMUL(HC, WC, KC) \
+  case KC: \
+    MatMulKernelFunc<T, register_tile_size, HC, WC, KC>(a_ptr_batch_base, \
+                  b_ptr_batch_base, \
+                  c_ptr_batch_base, \
+                  ih, \
+                  iw, \
+                  ik, \
+                  height, \
+                  width, \
+                  K); \
+  break;
+
+#define CASE_W_MATMUL(HC, WC) \
+  case WC: \
+    switch (k_count) { \
+      CASE_K_MATMUL(HC, WC, 1); \
+      CASE_K_MATMUL(HC, WC, 2); \
+      CASE_K_MATMUL(HC, WC, 3); \
+      CASE_K_MATMUL(HC, WC, 4); \
+      default: \
+        LOG(FATAL) << "Unsupported k tile: " << k_count; \
+    } \
+    break;
+
+#define CASE_H_MATMUL(HC) \
+  case HC: \
+    switch (w_count) { \
+      CASE_W_MATMUL(HC, 1); \
+      CASE_W_MATMUL(HC, 2); \
+      CASE_W_MATMUL(HC, 3); \
+      CASE_W_MATMUL(HC, 4); \
+      default: \
+        LOG(FATAL) << "Unsupported w tile: " << k_count; \
+    } \
+    break;
+
+template<DeviceType D, typename T>
 struct MatMulFunctor {
   void operator()(const Tensor *A,
                   const Tensor *B,
@@ -20,37 +134,87 @@ struct MatMulFunctor {
                   StatsFuture *future) {
     std::vector<index_t> c_shape = {A->dim(0), A->dim(1), B->dim(2), 1};
     C->Resize(c_shape);
-    const index_t N = C->dim(0);
-    const index_t height = C->dim(1);
-    const index_t width = C->dim(2);
-    const index_t K = A->dim(2);
+
     Tensor::MappingGuard guarda(A);
     Tensor::MappingGuard guardb(B);
     Tensor::MappingGuard guardc(C);
     const T *a_ptr_base = A->data<T>();
     const T *b_ptr_base = B->data<T>();
-    T *c_ptr = C->mutable_data<T>();
-    for (int i = 0; i < N; ++i) {
-      for (int h = 0; h < height; ++h) {
-        for (int w = 0; w < width; ++w) {
-          const T *a_ptr = a_ptr_base + h * K;
-          const T *b_ptr = b_ptr_base + w;
-          *c_ptr = 0;
-          for (int k = 0; k < K; ++k) {
-            *c_ptr += *a_ptr * *b_ptr;
-            a_ptr++;
-            b_ptr += width;
-          }
-          c_ptr++;
-        }
-      }
-      a_ptr_base += height * K;
-      b_ptr_base += K * width;
-    }
+    T *c_ptr_base = C->mutable_data<T>();
+
+    const index_t batch = C->dim(0);
+    const index_t height = C->dim(1);
+    const index_t width = C->dim(2);
+    const index_t K = A->dim(2);
+    // It is better to use large block size if it fits for fast cache.
+    // Assume l1 cache size is 32k, we load three blocks at a time (A, B, C),
+    // the block size should be sqrt(32k / sizeof(T) / 3).
+    const index_t block_size = 48;
+    const index_t block_tile_height = RoundUpDiv(height, block_size);
+    const index_t block_tile_width = RoundUpDiv(width, block_size);
+    const index_t block_tile_k = RoundUpDiv(K, block_size);
+    const index_t remain_height = height % block_size;
+    const index_t remain_width = width % block_size;
+    const index_t remain_k = K % block_size;
+    constexpr index_t register_tile_size = 4;
+    memset(c_ptr_base, 0, batch * height * width * sizeof(T));
+
+#pragma omp parallel for collapse(3)
+    for (index_t n = 0; n < batch; ++n) {
+      // handle block
+      for (index_t bh = 0; bh < block_tile_height; ++bh) {
+        for (index_t bw = 0; bw < block_tile_width; ++bw) {
+          const T *a_ptr_batch_base = a_ptr_base + n * height * K;
+          const T *b_ptr_batch_base = b_ptr_base + n * K * width;
+          T *c_ptr_batch_base = c_ptr_base + n * height * width;
+          const index_t ih_begin = bh * block_size;
+          const index_t ih_end =
+            bh * block_size + (bh == block_tile_height - 1 && remain_height > 0
+                               ? remain_height : block_size);
+          const index_t iw_begin = bw * block_size;
+          const index_t iw_end =
+            bw * block_size
+              + (bw == block_tile_width - 1 && remain_width > 0 ? remain_width
+                                                                : block_size);
+
+          for (index_t bk = 0; bk < block_tile_k; ++bk) {
+            const index_t ik_begin = bk * block_size;
+            const index_t ik_end =
+              bk * block_size
+                + (bk == block_tile_k - 1 && remain_k > 0 ? remain_k
+                                                          : block_size);
+
+            // inside block:
+            // calculate C[bh, bw] += A[bh, bk] * B[bk, bw] for one k
+            for (index_t ih = ih_begin; ih < ih_end;
+                 ih += register_tile_size) {
+              for (index_t iw = iw_begin; iw < iw_end;
+                   iw += register_tile_size) {
+                for (index_t ik = ik_begin; ik < ik_end;
+                     ik += register_tile_size) {
+                  const int h_count = std::min(register_tile_size, ih_end - ih);
+                  const int w_count = std::min(register_tile_size, iw_end - iw);
+                  const int k_count = std::min(register_tile_size, ik_end - ik);
+
+                  switch (h_count) {
+                    CASE_H_MATMUL(1);
+                    CASE_H_MATMUL(2);
+                    CASE_H_MATMUL(3);
+                    CASE_H_MATMUL(4);
+                    default:LOG(FATAL) << "Unsupported height tile: "
+                                       << h_count;
+                  }
+                }  // ik
+              }  // iw
+            }  // ih
+          }  // bk
+        }  // bw
+      }  // bh
+    }  // n
   }
 };
 
-template <typename T>
+template<typename T>
 struct MatMulFunctor<DeviceType::OPENCL, T> {
   void operator()(const Tensor *A,
                   const Tensor *B,

mace/ops/matmul_benchmark.cc

@@ -68,4 +68,7 @@ static void MatMulBenchmark(
 BM_MATMUL(16, 32, 128, 49);
 BM_MATMUL(16, 32, 128, 961);
 BM_MATMUL(16, 32, 128, 3969);
+BM_MATMUL(16, 128, 128, 49);
+BM_MATMUL(16, 128, 128, 961);
+BM_MATMUL(16, 128, 128, 3969);
 }  // namespace mace