use fast rcpf

include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_one_pass.hpp

@@ -90,7 +90,7 @@ struct Layernorm2dFwdPipelineOnePass
         // compute inv-std
         auto inv_std = tile_elementwise_in(
             [&](const auto& v_) {
-                return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ + epsilon));
+                return type_convert<ComputeDataType>(1.0f) * __builtin_amdgcn_rcpf(sqrt(v_ + epsilon));
             },
             var);
 

include/ck_tile/ops/welford/block/block_welford.hpp

@@ -356,7 +356,7 @@ CK_TILE_DEVICE constexpr void block_tile_welford_post_scale_var(VarDistributedTe
                                                                 int count)
 {
     using DataType = typename VarDistributedTensor_::DataType;
-    tile_elementwise_inout([&count](auto& x) { x = x / type_convert<DataType>(count); },
+    tile_elementwise_inout([&count](auto& x) { x = x * __builtin_amdgcn_rcpf(type_convert<DataType>(count)); },
                            var_tensor);
 }
 } // namespace ck_tile

include/ck_tile/ops/welford/thread/thread_welford.hpp

@@ -12,7 +12,7 @@ CK_TILE_DEVICE void welford_update(T& mean, T& var, T x, int count)
 {
     // TODO: check nan? maybe no
     T delta = x - mean;
-    mean += delta / count;
+    mean += delta * __builtin_amdgcn_rcpf(count);
     T delta2 = x - mean;
     var += delta * delta2;
 }
@@ -25,7 +25,7 @@ welford_merge(T& mean_a, T& var_a, int& count_a, T mean_b, T var_b, int count_b)
     T count_             = type_convert<T>(count);
     T count_a_           = type_convert<T>(count_a);
     T count_b_           = type_convert<T>(count_b);
-    T count_b_over_count = count == 0 ? type_convert<T>(0) : count_b_ / count_;
+    T count_b_over_count = count == 0 ? type_convert<T>(0) : count_b_ * __builtin_amdgcn_rcpf(count_);
 
     T delta = mean_b - mean_a;
     mean_a += delta * count_b_over_count;