Merge remote-tracking branch 'origin/fastsoftmax' into bert-perf

src/targets/gpu/kernels/include/migraphx/kernels/reduce.hpp

@@ -196,11 +196,11 @@ struct block
     struct reducer
     {
         index idx;
-        Slicer slicer;
+        Slicer slice;
         template <class Op, class T, class Read>
         __device__ auto reduce(Op op, T init, Read read) const
         {
-            return sliced(slicer, [=](auto x, auto... xs) {
+            return sliced(slice, [=](auto x, auto... xs) {
                 return vec_reduce(block_reduce(idx,
                                                op,
                                                init,
@@ -220,7 +220,7 @@ struct block
         template <class F>
         __device__ auto inner(F f) const
         {
-            return sliced(slicer, [=](auto x, auto... xs) {
+            return sliced(slice, [=](auto x, auto... xs) {
                 idx.local_stride(x.get_shape().elements(), [&](auto j) { f(x[j], xs[j]...); });
             });
         }
@@ -228,7 +228,7 @@ struct block
         template <class Input>
         constexpr auto elements() const
         {
-            using reduce_type        = decltype(slicer(Input{}));
+            using reduce_type        = decltype(slice(Input{}));
             using value_type         = typename Input::type;
             constexpr auto relements = get_shape_c<reduce_type>{}.elements();
             if constexpr(vec_size<value_type>() > 1)
@@ -262,11 +262,11 @@ struct lane
     struct reducer
     {
         index idx;
-        Slicer slicer;
+        Slicer slice;
         template <class Op, class T, class Read>
         __device__ auto reduce(Op op, T init, Read read) const
         {
-            return sliced(slicer, [=](auto x, auto... xs) {
+            return sliced(slice, [=](auto x, auto... xs) {
                 using type = typename decltype(x)::type;
                 type r     = init;
                 for(index_int j = 0; j < x.get_shape().elements(); j++)
@@ -286,7 +286,7 @@ struct lane
         template <class F>
         __device__ auto inner(F f) const
         {
-            return sliced(slicer, [=](auto x, auto... xs) {
+            return sliced(slice, [=](auto x, auto... xs) {
                 for(index_int j = 0; j < x.get_shape().elements(); j++)
                 {
                     f(x[j], xs[j]...);
@@ -297,7 +297,7 @@ struct lane
         template <class Input>
         constexpr auto elements() const
         {
-            using reduce_type = decltype(slicer(Input{}));
+            using reduce_type = decltype(slice(Input{}));
             return get_shape_c<reduce_type>{}.elements();
         }
     };

src/targets/gpu/kernels/include/migraphx/kernels/softmax.hpp

@@ -33,11 +33,11 @@ template <index_int Axis, class Input, class Output>
 __device__ void softmax(Input input, Output output)
 {
     reduce::block::run<reduce::with_axis<Input, Axis>>([&](auto, auto r) {
-        auto batch_max = r.reduce(op::max{}, lowest{}, op::id{})(input);
-        auto batch_sum =
-            r.reduce(op::sum{}, 0, [&](auto x) { return migraphx::exp(x - batch_max); })(input);
-        r.inner([&](auto& y, auto x) { y = migraphx::exp(x - batch_max) / batch_sum; })(output,
-                                                                                        input);
+        const auto c   = vec_at(r.slice(input)[0], 0);
+        auto batch_sum = r.reduce(op::sum{}, 0, [&](auto x) {
+            return migraphx::convert<float>(migraphx::exp(x - c));
+        })(input);
+        r.inner([&](auto& y, auto x) { y = migraphx::exp(x - c) / batch_sum; })(output, input);
     });
 }