From 2791e0efa6c5114504c1c203cc0590340369e533 Mon Sep 17 00:00:00 2001
From: kswang <wangkaisheng2@huawei.com>
Date: Tue, 22 Feb 2022 16:34:43 +0800
Subject: [PATCH] update cpu adafactor code annotation

---
 .../kernel_compiler/cpu/fused_ada_factor_cpu_kernel.cc | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/fused_ada_factor_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/fused_ada_factor_cpu_kernel.cc
index 475bb0ee7f8..dbf0618e370 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/cpu/fused_ada_factor_cpu_kernel.cc
+++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/fused_ada_factor_cpu_kernel.cc
@@ -109,7 +109,7 @@ void FusedAdaFactorCPUKernel::FactorUpdate(T *update, const std::vector<AddressP
   size_t last_row_col_size = last_row_dim_size_ * last_col_dim_size_;
   size_t row_dim_size = last_row_dim_size_;
   size_t col_dim_size = last_col_dim_size_;
-  // exp_avg_sq_row = exp_avg_sq_row * beta2t + reduce_mean(update, -1) * one_minus_beta2t;
+  // step 1: exp_avg_sq_row = exp_avg_sq_row * beta2t + reduce_mean(update, -1) * one_minus_beta2t;
   task = [&](size_t start, size_t end) {
     for (size_t i = start; i < end; ++i) {
       float row_reduce = 0;
@@ -124,7 +124,7 @@ void FusedAdaFactorCPUKernel::FactorUpdate(T *update, const std::vector<AddressP
   };
   CPUKernelUtils::ParallelFor(task, exp_avg_sq_row_elem_num, kBatchSize);
 
-  // r_factor = sqrt(exp_avg_sq_row / reduce_mean(exp_avg_sq_row, -1))
+  // step 2: r_factor = sqrt(exp_avg_sq_row / reduce_mean(exp_avg_sq_row, -1))
   task = [&](size_t start, size_t end) {
     for (size_t i = start; i < end; ++i) {
       float col_reduce = 0;
@@ -142,8 +142,8 @@ void FusedAdaFactorCPUKernel::FactorUpdate(T *update, const std::vector<AddressP
   };
   CPUKernelUtils::ParallelFor(task, exp_avg_sq_row_elem_num / col_dim_size, kBatchSize);
 
-  // exp_avg_sq_col = exp_avg_sq_col * beta2t + reduce_mean(update, -2) * one_minus_beta2t;
-  // c_factor = sqrt(exp_avg_sq_col);
+  // step 3: exp_avg_sq_col = exp_avg_sq_col * beta2t + reduce_mean(update, -2) * one_minus_beta2t;
+  // step 4: c_factor = sqrt(exp_avg_sq_col);
   task = [&](size_t start, size_t end) {
     for (size_t i = start; i < end; ++i) {
       float row_reduce = 0;
@@ -160,7 +160,7 @@ void FusedAdaFactorCPUKernel::FactorUpdate(T *update, const std::vector<AddressP
   };
   CPUKernelUtils::ParallelFor(task, exp_avg_sq_col_elem_num, kBatchSize);
 
-  // update = grad / (r_factor * c_factor);
+  // step 5: update = grad / (r_factor * c_factor);
   task = [&](size_t start, size_t end) {
     for (size_t i = start; i < end; ++i) {
       size_t row_i = i % row_dim_size;