Improve layernorm and reductions performance (#1348)

Compute mean and variance in same reduction
Set block size to numbers divisible by 32 instead powers of 2
Global is also set exactly instead of being divisible by block size
More exact matching of global/local can help get rid of branching/loops
Reduce vectors first before doing dpp_reduce
Explicitly vectorize array operators since the compiler doesnt always vectorize them
Still uses old for loop when its computing at compile-time since the reinterpret_cast nor the all the vector types is supported

src/targets/gpu/compile_hip_code_object.cpp

@@ -138,16 +138,16 @@ compute_global_for(context& ctx, std::size_t n, std::size_t over)
         std::size_t groups     = (n + local - 1) / local;
         std::size_t max_blocks = max_global / local;
         std::size_t nglobal    = std::min(max_blocks * over, groups) * local;
-        return nglobal;
+        return std::min(nglobal, n);
     };
 }
 
 std::size_t compute_block_size(std::size_t n, std::size_t max_block_size)
 {
-    size_t block_size = 128;
-    while(block_size <= max_block_size and block_size <= n)
-        block_size *= 2;
-    return block_size / 2;
+    const std::size_t min_block_size  = 64;
+    const std::size_t base_block_size = 32;
+    auto block_size                   = (((n - 1) / base_block_size + 1)) * base_block_size;
+    return std::min(std::max(min_block_size, block_size), max_block_size);
 }
 
 operation compile_hip_code_object(const std::string& content, hip_compile_options options)

src/targets/gpu/fuse_mlir.cpp

@@ -61,13 +61,25 @@ struct mlir_conv
 MIGRAPHX_REGISTER_OP(mlir_conv);
 
 namespace {
+
+MIGRAPHX_PRED_MATCHER(is_mlir_conv, instruction_ref ins)
+{
+    if(ins->name() != "convolution")
+        return false;
+    value v    = ins->get_operator().to_value();
+    auto group = v.at("group").to<int>();
+    if(group != 1)
+        return false;
+    return true;
+}
+
 struct find_conv_pointwise
 {
     // Find a convolution followed by a pointwise operation.
     auto matcher() const
     {
         auto convolution =
-            match::skip(match::name("contiguous"))(match::name("convolution").bind("convolution"));
+            match::skip(match::name("contiguous"))(is_mlir_conv().bind("convolution"));
         return match::name("pointwise")(match::any_of[match::inputs()](convolution.bind("x")));
     }
 

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

@@ -37,45 +37,91 @@ namespace migraphx {
     template <class U>                                                                       \
     constexpr array& operator op(const array<U, N>& x)                                       \
     {                                                                                        \
-        for(index_int i = 0; i < N; i++)                                           \
-            d[i] op x[i];                                                          \
+        array_detail::array_for_each(*this, x)([](auto& sy, auto sx) { sy op sx; });         \
         return *this;                                                                        \
     }                                                                                        \
     template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                            \
     constexpr array& operator op(const U& x)                                                 \
     {                                                                                        \
-        for(index_int i = 0; i < N; i++)                                           \
-            d[i] op x;                                                             \
+        array_detail::array_for_each (*this)([&](auto& sy) { sy op x; });                    \
         return *this;                                                                        \
     }                                                                                        \
     template <class U>                                                                       \
     friend constexpr auto operator binary_op(const array& x, const array<U, N>& y)           \
     {                                                                                        \
         array<decltype(T {} binary_op U{}), N> z{};                                          \
-        for(index_int i = 0; i < N; i++)                                           \
-            z[i] = x[i] binary_op y[i];                                            \
+        array_detail::array_for_each(z, x, y)(                                               \
+            [&](auto& sz, auto sx, auto sy) { sz = sx binary_op sy; });                      \
         return z;                                                                            \
     }                                                                                        \
     template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                            \
     friend constexpr auto operator binary_op(const array& x, const U& y)                     \
     {                                                                                        \
         array<decltype(T {} binary_op U{}), N> z{};                                          \
-        for(index_int i = 0; i < N; i++)                                           \
-            z[i] = x[i] binary_op y;                                               \
+        array_detail::array_for_each(z, x)([&](auto& sz, auto sx) { sz = sx binary_op y; }); \
         return z;                                                                            \
     }                                                                                        \
     template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                            \
     friend constexpr auto operator binary_op(const U& x, const array& y)                     \
     {                                                                                        \
         array<decltype(T {} binary_op U{}), N> z{};                                          \
-        for(index_int i = 0; i < N; i++)                                           \
-            z[i] = x binary_op y[i];                                               \
+        array_detail::array_for_each(z, y)([&](auto& sz, auto sy) { sz = x binary_op sy; }); \
         return z;                                                                            \
     }
 
+namespace array_detail {
+template <class T>
+constexpr auto is_vectorizable()
+{
+    return not is_same<T, bool>{} and (is_fundamental<T>{} or is_same<T, half>{});
+}
+
+template <class T>
+__device__ auto& array2vec(T& x)
+{
+    using value_type    = typename T::value_type;
+    constexpr auto size = decltype(x.size()){};
+    using type          = vec<value_type, size>;
+    if constexpr(is_const<T>{})
+        return reinterpret_cast<const type&>(x);
+    else
+        return reinterpret_cast<type&>(x);
+}
+
+template <class T, class... Ts>
+constexpr auto array_for_each(T& x, Ts&... xs)
+{
+    MIGRAPHX_ASSERT(((x.size() == xs.size()) and ...));
+    return [&](auto f) {
+        constexpr auto size = decltype(x.size()){};
+        if constexpr((is_vectorizable<typename T::value_type>() or
+                      (is_vectorizable<typename Ts::value_type>() or ...)) and
+                     size <= 8 and size > 1 and (size % 2 == 0))
+        {
+            if(__builtin_is_constant_evaluated())
+            {
+                for(index_int i = 0; i < size; i++)
+                    f(x[i], xs[i]...);
+            }
+            else
+            {
+                using vec_type = std::remove_reference_t<decltype(array2vec(x))>;
+                f(array2vec(x), __builtin_convertvector(array2vec(xs), vec_type)...);
+            }
+        }
+        else
+        {
+            for(index_int i = 0; i < size; i++)
+                f(x[i], xs[i]...);
+        }
+    };
+}
+} // namespace array_detail
+
 template <class T, index_int N>
 struct array
 {
+    using value_type = T;
     T d[N];
     constexpr T& operator[](index_int i)
     {
@@ -108,18 +154,13 @@ struct array
 
     constexpr T dot(const array& x) const
     {
-        T result = 0;
-        for(index_int i = 0; i < N; i++)
-            result += x[i] * d[i];
-        return result;
+        auto r = x * (*this);
+        return r.reduce([](auto a, auto b) { return a + b; }, 0);
     }
 
     constexpr T product() const
     {
-        T result = 1;
-        for(index_int i = 0; i < N; i++)
-            result *= d[i];
-        return result;
+        return reduce([](auto x, auto y) { return x * y; }, 1);
     }
 
     constexpr T single(index_int width = 100) const
@@ -134,6 +175,24 @@ struct array
         return result;
     }
 
+    template <class F>
+    constexpr auto apply(F f) const
+    {
+        array<decltype(f(d[0])), N> result;
+        for(index_int i = 0; i < N; i++)
+            result[i] = f(d[i]);
+        return result;
+    }
+
+    template <class F>
+    constexpr auto reduce(F f, T init) const
+    {
+        T result = init;
+        for(index_int i = 0; i < N; i++)
+            result = f(result, d[i]);
+        return result;
+    }
+
     MIGRAPHX_DEVICE_ARRAY_OP(+=, +)
     MIGRAPHX_DEVICE_ARRAY_OP(-=, -)
     MIGRAPHX_DEVICE_ARRAY_OP(*=, *)
@@ -201,6 +260,11 @@ struct array
     }
 };
 
+template <class T, class... Ts>
+constexpr array<T, sizeof...(Ts) + 1> make_array(T x, Ts... xs)
+{
+    return {x, static_cast<T>(xs)...};
+}
 template <class T, T... Xs>
 struct integral_const_array : array<T, sizeof...(Xs)>
 {

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

@@ -28,9 +28,60 @@
 #include <migraphx/kernels/types.hpp>
 #include <migraphx/kernels/integral_constant.hpp>
 #include <migraphx/kernels/type_traits.hpp>
+#include <migraphx/kernels/debug.hpp>
 
 namespace migraphx {
 
+#if defined(MIGRAPHX_NGLOBAL) && defined(MIGRAPHX_NLOCAL)
+#define MIGRAPHX_NGROUP ((MIGRAPHX_NGLOBAL + MIGRAPHX_NLOCAL - 1) / MIGRAPHX_NLOCAL)
+#endif
+
+inline __device__ __attribute__((const)) index_int compute_global_size()
+{
+#ifdef MIGRAPHX_NGLOBAL
+    return MIGRAPHX_NGLOBAL;
+#else
+    // This actualy works even when global is not divisible by local size.
+    // This doesnt actually do a multiplicatiosn. Instead it calls a device
+    // function to get the global size, which is why it works.
+    return blockDim.x * gridDim.x;  // NOLINT
+#endif
+}
+
+// We cant just use blockDim.x to get the local size since its broken on hip
+// when global is not divisible by local size. In this case, we calulate the
+// size for the last group.
+inline __device__ __attribute__((const)) index_int compute_local_size()
+{
+#ifdef MIGRAPHX_NLOCAL
+    const auto nlocal = MIGRAPHX_NLOCAL;
+#else
+    const auto nlocal = blockDim.x; // NOLINT
+#endif
+#ifdef MIGRAPHX_NGROUP
+    const auto ngroup = MIGRAPHX_NGROUP;
+#else
+    const auto ngroup = gridDim.x;  // NOLINT
+#endif
+    const auto group_id = blockIdx.x; // NOLINT
+    const auto nglobal  = compute_global_size();
+    if(group_id == ngroup - 1)
+    {
+        return 1 + (nglobal - 1) % nlocal;
+    }
+    else
+    {
+        return nlocal; // NOLINT
+    }
+}
+
+#ifdef MIGRAPHX_NGROUP
+// If global is divisible by local then local can be a const
+#if(MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL == 0) || (MIGRAPHX_NGROUP == 1)
+#define MIGRAPHX_HAS_CONST_LOCAL 1
+#endif
+#endif
+
 struct index
 {
     index_int global = 0;
@@ -38,20 +89,44 @@ struct index
     index_int group  = 0;
 
 #ifdef MIGRAPHX_NGLOBAL
-    constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const { return {}; }
+    constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const
+    {
+        static_assert(MIGRAPHX_NGLOBAL > 0, "Global size must be greater than 0");
+        return {};
+    }
 #else
     __device__ index_int nglobal() const
     {
-        return blockDim.x * gridDim.x; // NOLINT
+        MIGRAPHX_ASSERT(compute_global_size() > 0);
+        return compute_global_size(); // NOLINT
     }
 #endif
 
-#ifdef MIGRAPHX_NLOCAL
-    constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const { return {}; }
+#ifdef MIGRAPHX_HAS_CONST_LOCAL
+    constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const
+    {
+        static_assert(MIGRAPHX_NLOCAL > 0, "Local size must be greater than 0");
+        return {};
+    }
 #else
     __device__ index_int nlocal() const
     {
-        return blockDim.x; // NOLINT
+#ifdef MIGRAPHX_NGROUP
+        static_assert((MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL != 0) and (MIGRAPHX_NGROUP > 1),
+                      "Local size should be const");
+#endif
+        MIGRAPHX_ASSERT(compute_local_size() > 0);
+        return compute_local_size(); // NOLINT
+    }
+#endif
+
+#ifdef MIGRAPHX_NLOCAL
+    constexpr index_constant<MIGRAPHX_NLOCAL> max_nlocal() const { return {}; }
+#else
+    __device__ index_int max_nlocal() const
+    {
+        MIGRAPHX_ASSERT(blockDim.x > 0);
+        return blockDim.x;
     }
 #endif
     template <class N, class Stride>
@@ -63,6 +138,7 @@ struct index
     template <class F, class N, class Stride>
     static constexpr void for_stride(index_int start, N n, Stride stride, F f)
     {
+        MIGRAPHX_ASSERT(start < stride);
         if constexpr(not is_integral<N>{} and not is_integral<Stride>{} and
                      max_stride_iterations(n, stride) == 1)
         {

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

@@ -29,6 +29,12 @@
 
 namespace migraphx {
 
+template <class T, index_int N, class Op>
+constexpr auto vec_reduce(const array<T, N>& a, Op op)
+{
+    return a.apply([&](auto x) { return vec_reduce(x, op); });
+}
+
 template <index_int Axis,
           class F,
           class BinOp,
@@ -43,23 +49,21 @@ __device__ void generic_binary_layernorm(
     reduce::block::run<reduce_output>([&](auto, auto r) {
         using value_type         = typename Input1::type;
         constexpr auto relements = r.template elements<Input1>();
-        auto mean                = [&](auto f) {
-            return r.reduce(op::sum{}, 0, [&](auto x1, auto x2) {
-                return f(x1, x2) / value_type{relements};
+        auto means =
+            r.reduce(op::sum{}, make_array<vec_type<value_type>>(0, 0), [&](auto x1, auto x2) {
+                auto x = op(x1, x2);
+                return make_array(x, x * x) * vec_type<value_type>{1.0 / relements};
             })(input1, input2);
-        };
-        // mean(x)
-        auto mean_x = mean(op);
-        // mean(m ^ 2)
-        auto mean_m2 = mean([&](auto x1, auto x2) {
-            auto m = op(x1, x2) - mean_x;
-            return m * m;
-        });
+
+        auto mean_x   = means[0];
+        auto mean_x2  = means[1];
+        auto variance = mean_x2 - (mean_x * mean_x);
 
         r.inner([&](auto& y, auto x1, auto x2, auto... xs) {
-            auto m = op(x1, x2) - mean_x;
+            auto x = op(x1, x2);
+            auto m = x - mean_x;
             // m * rsqrt(mean(m ^ 2) + 1e-12)
-            y = compute(m * rsqrt(mean_m2 + value_type{1e-12}), xs...);
+            y = compute(m * rsqrt(variance + value_type{1e-12}), xs...);
         })(output, input1, input2, inputs...);
     });
 }

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

@@ -94,16 +94,17 @@ MIGRAPHX_DPP_REDUCE(op::max, v_max)
 MIGRAPHX_DPP_REDUCE(op::min, v_min)
 MIGRAPHX_DPP_REDUCE(op::product, v_mul)
 
-template <class Op, class T, class F>
-__device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
+template <class Op, class T, class Index, class F>
+__device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
 {
+    MIGRAPHX_ASSERT(idx.max_nlocal() == idx.nlocal());
 #if __AMDGCN_WAVEFRONT_SIZE == 32
     constexpr index_int lanes_per_thread = 16;
 #else
     constexpr index_int lanes_per_thread = 64;
 #endif
     using type = decltype(f(0));
-    __shared__ type buffer[idx.nlocal() / lanes_per_thread];
+    __shared__ type buffer[idx.max_nlocal() / lanes_per_thread];
     type x = init;
     idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
     dpp_reduce(x, op);
@@ -123,12 +124,12 @@ __device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
     return y;
 }
 #else
-template <class Op, class T, class F>
-__device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
+template <class Op, class T, class Index, class F>
+__device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
 {
-
+    MIGRAPHX_ASSERT(idx.max_nlocal() == idx.nlocal());
     using type = decltype(f(0));
-    __shared__ type buffer[idx.nlocal()];
+    __shared__ type buffer[idx.max_nlocal()];
     type x = init;
     idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
     buffer[idx.local] = x;
@@ -201,12 +202,9 @@ struct block
         __device__ auto reduce(Op op, T init, Read read) const
         {
             return sliced(slicer, [=](auto x, auto... xs) {
-                return vec_reduce(block_reduce(idx,
-                                               op,
-                                               init,
-                                               x.get_shape().elements(),
-                                               [&](auto j) { return read(x[j], xs[j]...); }),
-                                  op);
+                return block_reduce(idx, op, init, x.get_shape().elements(), [&](auto j) {
+                    return vec_reduce(read(x[j], xs[j]...), op);
+                });
             });
         }
 

test/verify/test_conv_group_add.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_conv_group_add : verify_program<test_conv_group_add>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        migraphx::shape s{migraphx::shape::float_type, {1, 68, 28, 28}};
+        auto x    = mm->add_parameter("x", s);
+        auto w    = mm->add_parameter("w", {migraphx::shape::float_type, {68, 17, 1, 1}});
+        auto b    = mm->add_parameter("b", {migraphx::shape::float_type, {68}});
+        auto conv = mm->add_instruction(migraphx::make_op("convolution", {{"group", 4}}), x, w);
+        auto bb   = mm->add_instruction(
+            migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", {1, 68, 28, 28}}}), b);
+        mm->add_instruction(migraphx::make_op("add"), conv, bb);
+        return p;
+    }
+};