clang-format (#11)

Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/11

clang format of fbgemm

Reviewed By: dskhudia

Differential Revision: D13115202

fbshipit-source-id: 6dab29cb8b5f4fabcc165019663351567a2a2952

1 files changed, 34 insertions, 25 deletions

diff --git a/src/FbgemmFP16.cc b/src/FbgemmFP16.cc
index f54feb8..d819cdd 100644
--- a/src/FbgemmFP16.cc
+++ b/src/FbgemmFP16.cc
@@ -27,26 +27,34 @@ inline void PackA(int nrow, int ncol, const float* from, int ldim, float* to) {
   //     to[r + c * nrow] = from[r * ldim + c];
   //   }
   // }
-  transpose_simd( nrow, ncol, from, ldim, to, nrow );
+  transpose_simd(nrow, ncol, from, ldim, to, nrow);
 }
 
 struct KernelInfo {
   using knl_ptr = funcptr_fp16;
   // optimized kernels to cover all cases
-  static constexpr array<knl_ptr, 15> kernel = {{
-    nullptr,                        gemmkernel_1x1_AVX2_fA0fB0fC0,
-    gemmkernel_2x1_AVX2_fA0fB0fC0,  gemmkernel_3x1_AVX2_fA0fB0fC0,
-    gemmkernel_4x1_AVX2_fA0fB0fC0,  gemmkernel_5x1_AVX2_fA0fB0fC0,
-    gemmkernel_6x1_AVX2_fA0fB0fC0,  gemmkernel_7x1_AVX2_fA0fB0fC0,
-    gemmkernel_8x1_AVX2_fA0fB0fC0,  gemmkernel_9x1_AVX2_fA0fB0fC0,
-    gemmkernel_10x1_AVX2_fA0fB0fC0, gemmkernel_11x1_AVX2_fA0fB0fC0,
-    gemmkernel_12x1_AVX2_fA0fB0fC0, gemmkernel_13x1_AVX2_fA0fB0fC0,
-    gemmkernel_14x1_AVX2_fA0fB0fC0
-  }};
+  static constexpr array<knl_ptr, 15> kernel = {
+      {nullptr,
+       gemmkernel_1x1_AVX2_fA0fB0fC0,
+       gemmkernel_2x1_AVX2_fA0fB0fC0,
+       gemmkernel_3x1_AVX2_fA0fB0fC0,
+       gemmkernel_4x1_AVX2_fA0fB0fC0,
+       gemmkernel_5x1_AVX2_fA0fB0fC0,
+       gemmkernel_6x1_AVX2_fA0fB0fC0,
+       gemmkernel_7x1_AVX2_fA0fB0fC0,
+       gemmkernel_8x1_AVX2_fA0fB0fC0,
+       gemmkernel_9x1_AVX2_fA0fB0fC0,
+       gemmkernel_10x1_AVX2_fA0fB0fC0,
+       gemmkernel_11x1_AVX2_fA0fB0fC0,
+       gemmkernel_12x1_AVX2_fA0fB0fC0,
+       gemmkernel_13x1_AVX2_fA0fB0fC0,
+       gemmkernel_14x1_AVX2_fA0fB0fC0}};
 
   // autotuned kernel splits for various cases m = 1:mb_max
   // may need re-autotuning for new uarch
-  static constexpr array<array<array<int, 2>, 2>, 121 > partition = {
+  static constexpr array<array<array<int, 2>, 2>, 121> partition = {
+    // NOTE: clang-format wants to use a different formatting but the current
+    // formatting should be easier to read.
     {
       {{ { 0, 0 }, { 0, 0 } } },
       {{ { 1, 1 }, { 0, 0 } } },
@@ -173,13 +181,16 @@ struct KernelInfo {
   };
 };
 constexpr array<KernelInfo::knl_ptr, 15> KernelInfo::kernel;
-constexpr array<array<array<int, 2>, 2>, 121 > KernelInfo::partition;
+constexpr array<array<array<int, 2>, 2>, 121> KernelInfo::partition;
 
 // autotuned kernel splits for various cases m = 1:mb_max
-void
-cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
-                   const PackedGemmMatrixFP16 &Bp, const float beta,
-                   float *C) {
+void cblas_gemm_compute(
+    const matrix_op_t transa,
+    const int m,
+    const float* A,
+    const PackedGemmMatrixFP16& Bp,
+    const float beta,
+    float* C) {
   // ground truth
   assert(cpuinfo_initialize());
   assert(cpuinfo_has_x86_fma3());
@@ -194,7 +205,7 @@ cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
   constexpr int kernel_ncols = kernel_ncol_blocks * simd_width;
 
   // private scratchpad storage
-  static thread_local unique_ptr<std::array<float, 256 * 1024> > scratchpad(
+  static thread_local unique_ptr<std::array<float, 256 * 1024>> scratchpad(
       new std::array<float, 256 * 1024>());
 
   GemmParams gp;
@@ -202,7 +213,6 @@ cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
     int mb = std::min(mb_max, m - m0);
     assert(mb < KernelInfo::partition.size());
     for (auto k_ind = 0; k_ind < k; k_ind += Bp.blockRowSize()) {
-
       // set up proper accumulation to avoid "Nan" problem
       float beta_;
       uint64_t accum;
@@ -221,7 +231,6 @@ cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
 
       auto m1 = 0;
       for (auto c = 0; c < 2; c++) {
-
         auto kernel_nrows = KernelInfo::partition[mb][c][0];
         auto nkernel_nrows = KernelInfo::partition[mb][c][1];
 
@@ -261,7 +270,7 @@ cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
               KernelInfo::kernel[kernel_nrows](&gp);
             } else {
               // small temporary buffer
-              float c_tmp[16 * 24] = { 0 };
+              float c_tmp[16 * 24] = {0};
               assert((16 * 24) > kernel_nrows * kernel_ncols);
 
               gp.B = &(Bp(k_ind, last_blk_col));
@@ -272,13 +281,14 @@ cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
               for (int i = 0; i < kernel_nrows; i++) {
                 // Todo: use assembly
                 for (int j = last_blk_col; j < n; j++) {
-                  assert(i * 8 + (j - last_blk_col) <
-                         sizeof(c_tmp) / sizeof(c_tmp[0]));
+                  assert(
+                      i * 8 + (j - last_blk_col) <
+                      sizeof(c_tmp) / sizeof(c_tmp[0]));
                   if (accum == 0) {
                     C[(m2 + i) * ldc + j] = c_tmp[i * 8 + (j - last_blk_col)];
                   } else {
                     C[(m2 + i) * ldc + j] = beta_ * C[(m2 + i) * ldc + j] +
-                                            c_tmp[i * 8 + (j - last_blk_col)];
+                        c_tmp[i * 8 + (j - last_blk_col)];
                   }
                 }
               }
@@ -291,5 +301,4 @@ cblas_gemm_compute(const matrix_op_t transa, const int m, const float *A,
   }
 }
 
-
 } // namespace fbgemm