Merge branch 'develop' into layout-nhwc

src/targets/gpu/include/migraphx/gpu/sub.hpp

-/*
- * 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.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_SUB_HPP
-#define MIGRAPHX_GUARD_RTGLIB_SUB_HPP
-
-#include <migraphx/gpu/oper.hpp>
-#include <migraphx/gpu/device/sub.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct hip_sub : binary_device<hip_sub, device::sub>
-{
-};
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

src/targets/gpu/include/migraphx/gpu/tan.hpp

-/*
- * 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.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_TAN_HPP
-#define MIGRAPHX_GUARD_RTGLIB_TAN_HPP
-
-#include <migraphx/gpu/oper.hpp>
-#include <migraphx/gpu/device/tan.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct hip_tan : unary_device<hip_tan, device::tan>
-{
-};
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

src/targets/gpu/include/migraphx/gpu/tanh.hpp

-/*
- * 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.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_TANH_HPP
-#define MIGRAPHX_GUARD_RTGLIB_TANH_HPP
-
-#include <migraphx/gpu/oper.hpp>
-#include <migraphx/gpu/device/tanh.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct hip_tanh : unary_device<hip_tanh, device::tanh>
-{
-};
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

src/targets/gpu/include/migraphx/gpu/unary_not.hpp

-/*
- * 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.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_UNARY_NOT_HPP
-#define MIGRAPHX_GUARD_RTGLIB_UNARY_NOT_HPP
-
-#include <migraphx/gpu/oper.hpp>
-#include <migraphx/gpu/device/unary_not.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct hip_unary_not : unary_device<hip_unary_not, device::unary_not>
-{
-    std::string name() const { return "gpu::not"; }
-};
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

src/targets/gpu/include/migraphx/gpu/where.hpp

-/*
- * 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.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_WHERE_HPP
-#define MIGRAPHX_GUARD_RTGLIB_WHERE_HPP
-
-#include <migraphx/gpu/oper.hpp>
-#include <migraphx/gpu/device/where.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct hip_where : ternary_device<hip_where, device::where>
-{
-    shape compute_shape(const std::vector<shape>& inputs) const
-    {
-        check_shapes{inputs, *this}.has(4).same_dims();
-        auto s1 = inputs.at(1);
-        auto s2 = inputs.at(2);
-        if(s1 == s2 and s1.packed())
-        {
-            return s1;
-        }
-        else if(s1.packed() != s2.packed())
-        {
-            return s1.packed() ? s1 : s2;
-        }
-        else if(s1.broadcasted() != s2.broadcasted())
-        {
-            return s1.broadcasted() ? s2.with_lens(s1.lens()) : s1.with_lens(s1.lens());
-        }
-        else
-        {
-            return {s1.type(), s1.lens()};
-        }
-    }
-};
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

src/targets/gpu/jit/concat.cpp

@@ -74,7 +74,7 @@ struct concat_compiler : compiler<concat_compiler>
         options.output      = inputs.back();
         options.params      = "-Wno-float-equal";
         auto axis           = find_fast_axis(options.inputs);
-        auto vec            = vectorize::elements(axis, options.inputs);
+        auto vec            = vectorize::elements(ctx, axis, options.inputs);
         options.kernel_name = v.get("kernel", "concat_kernel");
         options.set_launch_params(
             v, compute_global_for(ctx, get_concat_elements(options.inputs) / vec.size, 256));

src/targets/gpu/jit/gathernd.cpp

@@ -65,7 +65,7 @@ struct gathernd_compiler : compiler<gathernd_compiler>
     operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
     {
         hip_compile_options options;
-        auto out_s = inputs.back();
+        const auto& out_s = inputs.back();
         options.set_launch_params(v, compute_global_for(ctx, out_s.elements()));
         options.inputs         = inputs;
         options.output         = out_s;

src/targets/gpu/jit/layernorm.cpp

@@ -50,9 +50,8 @@ ${preamble}
 extern "C" {
 __global__ void ${kernel}(${params}) 
 {
-    auto idx = make_index();
     transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto... xs) {
-        ${layernorm}<${axis}>(${post}, xs...);
+        ${layernorm}<${axis}>(${post}, ${eps}, xs...);
     });
 }
     
@@ -78,9 +77,8 @@ struct layernorm_compiler : compiler<layernorm_compiler>
         // Vectorize if the axis is a reduction axis
         if(axis == faxis)
         {
-            vec = vectorize::elements(faxis, inputs);
+            vec = vectorize::elements(ctx, faxis, inputs);
         }
-        auto preloads   = preload::broadcasts(axis, inputs);
         auto relements  = inputs[0].lens()[axis] / vec.size;
         auto nelements  = (inputs.back().elements() / inputs[0].lens()[axis]);
         auto block_size = compute_block_size(relements, 256);
@@ -90,16 +88,18 @@ struct layernorm_compiler : compiler<layernorm_compiler>
         options.output      = inputs.back();
         options.inputs      = inputs;
         options.kernel_name = v.get("kernel", "layernorm_kernel");
+        auto eps            = v.get("epsilon", 1e-12f);
 
         auto src = interpolate_string(layernorm_kernel,
                                       {{"kernel", options.kernel_name},
                                        {"params", enum_params(inputs.size(), "void * private_p")},
                                        {"args", enum_params(inputs.size(), "private_p")},
-                                       {"transformers", make_transformer_args(preloads, vec)},
+                                       {"transformers", make_transformer_args(vec)},
                                        {"post", v.get("post", std::string{"op::id{}"})},
                                        {"preamble", v.get("preamble", std::string{})},
                                        {"layernorm", v.get("layernorm", std::string{"layernorm"})},
-                                       {"axis", to_string(axis)}});
+                                       {"axis", to_string(axis)},
+                                       {"eps", to_string(eps)}});
 
         return compile_hip_code_object(src, options);
     }

src/targets/gpu/jit/pointwise.cpp

@@ -75,20 +75,16 @@ struct pointwise_compiler : compiler<pointwise_compiler>
         options.virtual_inputs = reduce_dims(inputs);
         options.params         = "-Wno-float-equal";
         auto axis              = find_fast_axis(options.virtual_inputs);
-        auto vec               = vectorize::elements(axis, options.virtual_inputs);
-        auto preloads          = preload::broadcasts(axis, options.virtual_inputs);
+        auto vec               = vectorize::elements(ctx, axis, options.virtual_inputs);
         options.kernel_name    = v.get("kernel", "kernel");
         options.set_launch_params(
-            v,
-            compute_global_for(ctx,
-                               options.output.elements() / vec.size,
-                               oversubscribe_if(not preloads.is_preloading())));
+            v, compute_global_for(ctx, options.output.elements() / vec.size, 256));
         auto src = interpolate_string(pointwise_kernel,
                                       {{"kernel", options.kernel_name},
                                        {"params", enum_params(inputs.size(), "void * private_p")},
                                        {"args", enum_params(inputs.size(), "private_p")},
                                        {"lambda", v.at("lambda").to<std::string>()},
-                                       {"transformers", make_transformer_args(preloads, vec)},
+                                       {"transformers", make_transformer_args(vec)},
                                        {"preamble", v.get("preamble", std::string{})}});
         return compile_hip_code_object(src, options);
     }

src/targets/gpu/jit/reduce.cpp

@@ -121,7 +121,7 @@ struct reduce_compiler : compiler<reduce_compiler>
         // Vectorize if the axis is a reduction axis
         if(options.virtual_inputs.back().lens()[faxis] == 1)
         {
-            vec = vectorize::elements(faxis, options.virtual_inputs);
+            vec = vectorize::elements(ctx, faxis, options.virtual_inputs);
         }
         auto relements = get_reduce_elements(options.virtual_inputs) / vec.size;
         auto nelements = options.virtual_inputs.back().elements();

src/targets/gpu/jit/softmax.cpp

@@ -32,6 +32,8 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_USE_FAST_SOFTMAX)
+
 using namespace migraphx::gpu::gen; // NOLINT
 
 static const char* const softmax_kernel = R"__migraphx__(
@@ -69,7 +71,7 @@ struct softmax_compiler : compiler<softmax_compiler>
         // Vectorize if the axis is a reduction axis
         if(faxis == axis)
         {
-            vec = vectorize::elements(faxis, inputs);
+            vec = vectorize::elements(ctx, faxis, inputs);
         }
         auto relements  = inputs[0].lens()[axis] / vec.size;
         auto nelements  = (inputs.back().elements() / inputs[0].lens()[axis]);
@@ -81,6 +83,9 @@ struct softmax_compiler : compiler<softmax_compiler>
         options.inputs      = inputs;
         options.kernel_name = "softmax_kernel";
 
+        if(enabled(MIGRAPHX_USE_FAST_SOFTMAX{}))
+            options.params = "-DMIGRAPHX_USE_FAST_SOFTMAX";
+
         auto src = interpolate_string(
             softmax_kernel,
             {{"transformers", make_transformer_args(vec)}, {"axis", to_string(axis)}});

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

@@ -33,49 +33,95 @@
 namespace migraphx {
 
 // NOLINTNEXTLINE
-#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op)                                    \
-    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];                                                          \
-        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;                                                             \
-        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];                                            \
-        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;                                               \
-        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];                                               \
-        return z;                                                                  \
+#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op)                                              \
+    template <class U>                                                                       \
+    constexpr array& operator op(const array<U, N>& x)                                       \
+    {                                                                                        \
+        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)                                                 \
+    {                                                                                        \
+        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{};                                          \
+        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{};                                          \
+        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{};                                          \
+        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,
@@ -37,46 +43,46 @@ template <index_int Axis,
           class Input2,
           class... Inputs>
 __device__ void generic_binary_layernorm(
-    F compute, BinOp op, Output output, Input1 input1, Input2 input2, Inputs... inputs)
+    F compute, BinOp op, float eps, Output output, Input1 input1, Input2 input2, Inputs... inputs)
 {
     using reduce_output = reduce::with_axis<Input1, Axis>;
     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);
+        value_type eps_val = eps; // implicit conversion for eps
 
         r.inner([&](auto& y, auto x1, auto x2, auto... xs) {
-            auto m = op(x1, x2) - mean_x;
-            // m * rsqrt(mean(m ^ 2) + 1e-12)
-            y = compute(m * rsqrt(mean_m2 + value_type{1e-12}), xs...);
+            auto x = op(x1, x2);
+            auto m = x - mean_x;
+
+            // m * rsqrt(mean(m ^ 2) + epsilon)
+            y = compute(m * rsqrt(variance + eps_val), xs...);
         })(output, input1, input2, inputs...);
     });
 }
 
 template <index_int Axis, class F, class Output, class Input, class... Inputs>
-__device__ void layernorm(F compute, Output output, Input input, Inputs... inputs)
+__device__ void layernorm(F compute, float eps, Output output, Input input, Inputs... inputs)
 {
     generic_binary_layernorm<Axis>(
-        compute, [](auto x, auto) { return x; }, output, input, input, inputs...);
+        compute, [](auto x, auto) { return x; }, eps, output, input, input, inputs...);
 }
 
 template <index_int Axis, class F, class Output, class Input1, class Input2, class... Inputs>
 __device__ void
-add_layernorm(F compute, Output output, Input1 input1, Input2 input2, Inputs... inputs)
+add_layernorm(F compute, float eps, Output output, Input1 input1, Input2 input2, Inputs... inputs)
 {
     generic_binary_layernorm<Axis>(
-        compute, [](auto x1, auto x2) { return x1 + x2; }, output, input1, input2, inputs...);
+        compute, [](auto x1, auto x2) { return x1 + x2; }, eps, output, input1, input2, inputs...);
 }
 
 } // namespace migraphx

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

@@ -104,6 +104,7 @@ MIGRAPHX_DEVICE_MATH(floor, ::floor)
 MIGRAPHX_DEVICE_MATH(isnan, ::isnan)
 MIGRAPHX_DEVICE_MATH(log, ::log)
 MIGRAPHX_DEVICE_MATH(pow, ::pow)
+MIGRAPHX_DEVICE_MATH(remainder, ::remainder)
 MIGRAPHX_DEVICE_MATH(round, ::round)
 MIGRAPHX_DEVICE_MATH(rsqrt, ::rsqrt)
 MIGRAPHX_DEVICE_MATH(sin, ::sin)
@@ -111,6 +112,7 @@ MIGRAPHX_DEVICE_MATH(sinh, ::sinh)
 MIGRAPHX_DEVICE_MATH(sqrt, ::sqrt)
 MIGRAPHX_DEVICE_MATH(tan, ::tan)
 MIGRAPHX_DEVICE_MATH(tanh, ::tanh)
+MIGRAPHX_DEVICE_MATH(fmod, ::fmod)
 
 // Float overloads
 MIGRAPHX_DEVICE_MATH_FOR(float, acos, ::acosf)
@@ -126,6 +128,7 @@ MIGRAPHX_DEVICE_MATH_FOR(float, sin, ::sinf)
 MIGRAPHX_DEVICE_MATH_FOR(float, sinh, ::sinhf)
 MIGRAPHX_DEVICE_MATH_FOR(float, tan, ::tanf)
 MIGRAPHX_DEVICE_MATH_FOR(float, tanh, ::tanhf)
+MIGRAPHX_DEVICE_MATH_FOR(float, fmod, ::fmodf)
 
 // Builtin half functions
 MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, abs, ::__habs)
@@ -148,11 +151,13 @@ MIGRAPHX_DEVICE_MATH_HALF(erf, ::erf)
 MIGRAPHX_DEVICE_MATH_HALF(floor, ::floor)
 MIGRAPHX_DEVICE_MATH_HALF(isnan, ::isnan)
 MIGRAPHX_DEVICE_MATH_HALF(pow, ::pow)
+MIGRAPHX_DEVICE_MATH_HALF(remainder, ::remainder)
 MIGRAPHX_DEVICE_MATH_HALF(round, ::round)
 MIGRAPHX_DEVICE_MATH_HALF(sin, ::sin)
 MIGRAPHX_DEVICE_MATH_HALF(sinh, ::sinh)
 MIGRAPHX_DEVICE_MATH_HALF(tan, ::tan)
 MIGRAPHX_DEVICE_MATH_HALF(tanh, ::tanh)
+MIGRAPHX_DEVICE_MATH_HALF(fmod, ::fmod)
 
 // Map math functions to hip half2 functions
 // The half2 type is defined in include/hip/amd_detail/hip_fp16_gcc.h and is 2 16-bit floats
@@ -226,11 +231,13 @@ MIGRAPHX_DEVICE_MATH_VEC(cosh)
 MIGRAPHX_DEVICE_MATH_VEC(erf)
 MIGRAPHX_DEVICE_MATH_VEC(exp)
 MIGRAPHX_DEVICE_MATH_VEC(floor)
+MIGRAPHX_DEVICE_MATH_VEC(fmod)
 MIGRAPHX_DEVICE_MATH_VEC(isnan)
 MIGRAPHX_DEVICE_MATH_VEC(log)
 MIGRAPHX_DEVICE_MATH_VEC(max)
 MIGRAPHX_DEVICE_MATH_VEC(min)
 MIGRAPHX_DEVICE_MATH_VEC(pow)
+MIGRAPHX_DEVICE_MATH_VEC(remainder)
 MIGRAPHX_DEVICE_MATH_VEC(round)
 MIGRAPHX_DEVICE_MATH_VEC(rsqrt)
 MIGRAPHX_DEVICE_MATH_VEC(sin)

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;
@@ -196,17 +197,14 @@ 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 vec_reduce(block_reduce(idx,
-                                               op,
-                                               init,
-                                               x.get_shape().elements(),
-                                               [&](auto j) { return read(x[j], xs[j]...); }),
-                                  op);
+            return sliced(slice, [=](auto x, auto... xs) {
+                return block_reduce(idx, op, init, x.get_shape().elements(), [&](auto j) {
+                    return vec_reduce(read(x[j], xs[j]...), op);
+                });
             });
         }
 
@@ -220,7 +218,7 @@ 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 +226,7 @@ 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 +260,11 @@ 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 +284,7 @@ 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 +295,7 @@ 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 +33,15 @@ 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);
+#ifdef MIGRAPHX_USE_FAST_SOFTMAX
+        const auto c = vec_at(r.slice(input)[0], 0);
+#else
+        const auto c = r.reduce(op::max{}, lowest{}, op::id{})(input);
+#endif
+        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);
     });
 }
 

src/targets/gpu/lowering.cpp

@@ -26,6 +26,8 @@
 #include <migraphx/manage_ptr.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/make_op.hpp>
+#include <migraphx/instruction_ref.hpp>
+#include <migraphx/stringutils.hpp>
 
 #include <migraphx/op/convolution.hpp>
 #include <migraphx/op/deconvolution.hpp>
@@ -81,77 +83,21 @@ struct miopen_apply
         (void)i;
     }
 
-    const std::unordered_set<std::string>& get_rocblas_fp32_archs()
-    {
-        static std::unordered_set<std::string> supported_archs{"gfx908", "gfx90a"};
-        return supported_archs;
-    }
-
     void init()
     {
         assert(mod != nullptr);
         assert(pass != nullptr);
 
-#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
-        auto& ctx              = get_context();
-        const auto device_name = trim(split_string(get_device_name(), ':').front());
-        if(contains(get_rocblas_fp32_archs(), device_name))
-            compute_fp32 = true;
-        rocblas_gemm_flags flag;
-        rocblas_query_int8_layout_flag(ctx.get_stream().get_rocblas(), &flag);
-        int8_x4_format = (flag == rocblas_gemm_flags_pack_int8x4);
-#endif
+        auto& ctx      = get_context();
+        int8_x4_format = get_int8_x4_format(ctx);
+        compute_fp32   = get_compute_fp32_flag();
 
         offload_copy = (mod->name() == "main") ? pass->offload_copy : false;
 
-        add_generic_op("acos");
-        add_generic_op("acosh");
-        add_generic_op("add");
-        add_generic_op("asin");
-        add_generic_op("asinh");
-        add_generic_op("atan");
-        add_generic_op("atanh");
-        add_generic_op("ceil");
         add_generic_op("contiguous");
-        add_generic_op("cos");
-        add_generic_op("cosh");
-        add_generic_op("div");
-        add_generic_op("equal");
-        add_generic_op("erf");
-        add_generic_op("exp");
-        add_generic_op("floor");
-        add_generic_op("greater");
-        add_generic_op("less");
-        add_generic_op("log");
-        add_generic_op("logical_and");
-        add_generic_op("logical_or");
-        add_generic_op("logical_xor");
-        add_generic_op("max");
-        add_generic_op("min");
-        add_generic_op("mul");
-        add_generic_op("not");
-        add_generic_op("pow");
-        add_generic_op("prelu");
-        add_generic_op("recip");
-        add_generic_op("relu");
-        add_generic_op("round");
-        add_generic_op("rsqrt");
-        add_generic_op("sigmoid");
-        add_generic_op("sign");
-        add_generic_op("sin");
-        add_generic_op("sinh");
-        add_generic_op("sqdiff");
-        add_generic_op("sqrt");
-        add_generic_op("sub");
-        add_generic_op("tan");
-        add_generic_op("tanh");
-        add_generic_op("where");
-
-        add_extend_op("abs");
+
         add_extend_op("argmax");
         add_extend_op("argmin");
-        add_extend_op("clip");
-        add_extend_op("convert");
         add_extend_op("elu");
         add_extend_op("gather");
         add_extend_op("leaky_relu");
@@ -227,7 +173,8 @@ struct miopen_apply
         init();
         for(auto it = mod->begin(); it != mod->end(); it++)
         {
-            auto s = it->get_shape();
+            auto s     = it->get_shape();
+            auto attrs = it->get_operator().attributes();
             if(apply_map.count(it->name()) > 0)
             {
                 check_shape(s, apply_map.at(it->name())(it));
@@ -236,11 +183,37 @@ struct miopen_apply
             {
                 check_shape(s, insert_precompile_op(it));
             }
+            else if(attrs.contains("target"))
+            {
+                check_shape(s, insert_custom_op(it, attrs));
+            }
         }
-
         copy_params();
     }
 
+    instruction_ref insert_custom_op(instruction_ref ins, const value& attrs) const
+    {
+        const auto& custom_op = ins->get_operator();
+        if(attrs.at("target") == "cpu")
+        {
+            auto s = ins->get_shape();
+            std::vector<instruction_ref> cpu_inputs;
+            auto inputs = ins->inputs();
+            auto output = inputs.back();
+            std::transform(
+                inputs.begin(), inputs.end(), std::back_inserter(cpu_inputs), [&](auto in) {
+                    return mod->insert_instruction(ins, make_op("hip::copy_from_gpu"), in);
+                });
+            cpu_inputs.front() =
+                mod->insert_instruction(ins, make_op("hip::sync_stream"), cpu_inputs);
+            auto cpu_out = mod->insert_instruction(ins, custom_op, cpu_inputs);
+            auto gpu_out =
+                mod->insert_instruction(ins, make_op("hip::copy_to_gpu"), cpu_out, output);
+            return mod->replace_instruction(ins, gpu_out);
+        }
+        return ins;
+    }
+
     instruction_ref insert_precompile_op(instruction_ref ins) const
     {
         auto output                       = insert_allocation(ins, ins->get_shape());

src/targets/gpu/prefuse_ops.cpp

@@ -35,6 +35,12 @@ namespace {
 template <class Derived, std::size_t N>
 struct layernorm_base
 {
+    float epsilon = 1e-12f;
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.epsilon, "epsilon"));
+    }
     shape compute_shape(std::vector<shape> inputs, std::vector<module_ref> mods) const
     {
         std::size_t nargs = 1;
@@ -62,6 +68,7 @@ struct layernorm_base
 
 struct layernorm : layernorm_base<layernorm, 0>
 {
+
     std::string name() const { return "gpu::prelayernorm"; }
 };
 MIGRAPHX_REGISTER_OP(layernorm);
@@ -80,8 +87,9 @@ struct find_layernorm
     {
         auto ins   = r.result;
         auto x_ins = r.instructions["x"];
+        auto eps   = r.instructions["eps"]->eval().at<float>();
 
-        m.replace_instruction(ins, layernorm{}, x_ins);
+        m.replace_instruction(ins, layernorm{eps}, x_ins);
     }
 };
 
@@ -96,8 +104,9 @@ struct find_add_layernorm
     {
         auto ins     = r.result;
         auto add_ins = r.instructions["add"];
+        auto eps     = r.instructions["eps"]->eval().at<float>();
 
-        m.replace_instruction(ins, add_layernorm{}, add_ins->inputs());
+        m.replace_instruction(ins, add_layernorm{eps}, add_ins->inputs());
     }
 };
 } // namespace

src/targets/gpu/quant_convolution.cpp

@@ -22,7 +22,6 @@
  * THE SOFTWARE.
  */
 #include <migraphx/gpu/quant_convolution.hpp>
-#include <migraphx/gpu/device/convert.hpp>
 #include <migraphx/gpu/context.hpp>
 #include <migraphx/generate.hpp>