Merge branch 'develop' into uif2-initial

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

@@ -53,35 +53,35 @@ __device__ void gathernd(const T& data_t, const U& indices_t, const V& output_t,
     auto indices_shape_lens = indices_shape.lens;
     auto data_shape_lens    = data_shape.lens;
     auto num_slice_dims     = indices_shape_lens.back();
-    std::size_t num_slices =
+    size_t num_slices =
         accumulate(indices_shape_lens.begin(), indices_shape_lens.end() - 1, 1, op::product{});
-    std::size_t slice_size = accumulate(data_shape_lens.begin() + num_slice_dims + batch_dims,
-                                        data_shape_lens.end(),
-                                        1,
-                                        op::product{});
-    const std::size_t num_batches =
+    size_t slice_size = accumulate(data_shape_lens.begin() + num_slice_dims + batch_dims,
+                                   data_shape_lens.end(),
+                                   1,
+                                   op::product{});
+    const size_t num_batches =
         accumulate(data_shape_lens.begin(), data_shape_lens.begin() + batch_dims, 1, op::product{});
-    const std::size_t data_batch_stride =
+    const size_t data_batch_stride =
         accumulate(data_shape_lens.begin() + batch_dims, data_shape_lens.end(), 1, op::product{});
     const auto num_slices_per_batch = num_slices / num_batches;
 
     ind.global_stride(output_shape.elements(), [&](auto i) {
         const auto* indices_ptr     = indices_t.data();
-        const std::size_t j         = i / slice_size;
-        const std::size_t batch_idx = j / num_slices_per_batch;
+        const size_t j              = i / slice_size;
+        const size_t batch_idx      = j / num_slices_per_batch;
 
         auto* slice_indices               = indices_ptr + (j * num_slice_dims);
-        std::size_t relative_slice_offset = 0;
-        for(std::size_t idx = 0; idx < num_slice_dims; ++idx)
+        size_t relative_slice_offset      = 0;
+        for(size_t idx = 0; idx < num_slice_dims; ++idx)
         {
             int64_t index                   = slice_indices[idx];
-            const std::size_t input_dim_idx = batch_dims + idx;
+            const size_t input_dim_idx      = batch_dims + idx;
             const auto input_dim            = data_shape_lens[input_dim_idx];
             MIGRAPHX_ASSERT(index >= -static_cast<int64_t>(input_dim) and
                             index < static_cast<int64_t>(input_dim));
             if(index < 0)
                 index += input_dim;
-            std::size_t size_from_slice_dims =
+            size_t size_from_slice_dims =
                 accumulate(data_shape_lens.begin() + batch_dims + idx + 1,
                            data_shape_lens.begin() + batch_dims + num_slice_dims,
                            slice_size,

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

@@ -52,22 +52,25 @@ __device__ void generic_binary_layernorm(
     block::template run<reduce_output>([&](auto, auto r) {
         auto input       = r.inner([&](auto x1, auto x2) { return op(x1, x2); })(input1, input2);
         using value_type = typename Input1::type;
+        using vec_value_type       = vec_type<value_type>;
         constexpr auto relements   = r.template elements<Input1>();
-        constexpr auto relements_r = vec_type<value_type>{1.0 / relements};
+        constexpr auto relements_r = vec_value_type{1.0 / relements};
         auto relements_rsqrt       = sqrt(relements_r);
 
-        auto means = r.reduce(op::sum{}, make_array<vec_type<value_type>>(0, 0), [&](auto x) {
-            auto x_out = x * relements_r;
-            // dividing x by sqrt(relements) before squaring allows computing higher values
-            // before overflow in low precision
-            auto x2_sqrt = x * relements_rsqrt;
-            return make_array(x_out, x2_sqrt * x2_sqrt);
-        })(input);
+        auto means = r.reduce(op::sum{},
+                              make_array<vec_value_type>(vec_value_type{0}, vec_value_type{0}),
+                              [&](auto x) {
+                                  auto x_out = x * relements_r;
+                                  // dividing x by sqrt(relements) before squaring allows computing
+                                  // higher values before overflow in low precision
+                                  auto x2_sqrt = x * relements_rsqrt;
+                                  return make_array(x_out, x2_sqrt * x2_sqrt);
+                              })(input);
 
         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
+        value_type eps_val = implicit_conversion(eps);
 
         r.inner([&](auto& y, auto x, auto... xs) {
             auto m = x - mean_x;

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

@@ -29,11 +29,15 @@
 #include <migraphx/kernels/functional.hpp>
 #include <migraphx/kernels/type_traits.hpp>
 #include <migraphx/kernels/hip.hpp>
+#include <migraphx/kernels/float8.hpp>
 
 namespace migraphx {
 
 namespace math {
 constexpr float as_float(migraphx::half x) { return x; }
+
+constexpr float as_float(migraphx::fp8::fp8e4m3fnuz x) { return x; }
+
 template <class T>
 constexpr T as_float(T x)
 {
@@ -57,14 +61,14 @@ constexpr T as_float(T x)
 // NOLINTNEXTLINE
 #define MIGRAPHX_DEVICE_MATH_FOR(type, name, fname)                    \
     template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())> \
-    auto __device__ name(type x, Ts... xs)->type                       \
+    auto __device__ name(type x, Ts... xs) -> type                     \
     {                                                                  \
         return fname(x, xs...);                                        \
     }
 
 // NOLINTNEXTLINE
 #define MIGRAPHX_DEVICE_MATH_BINARY_FOR(type, name, fname) \
-    inline auto __device__ name(type x, type y)->type { return fname(x, y); }
+    inline auto __device__ name(type x, type y) -> type { return fname(x, y); }
 
 // NOLINTNEXTLINE
 #define MIGRAPHX_DEVICE_MATH_HALF(name, fname)                         \
@@ -72,6 +76,12 @@ constexpr T as_float(T x)
     auto __device__ name(migraphx::half x, Ts... xs)                   \
         MIGRAPHX_RETURNS(fname(math::as_float(x), math::as_float(xs)...))
 
+// NOLINTNEXTLINE
+#define MIGRAPHX_DEVICE_MATH_FP8(name, fname)                                      \
+    template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())>             \
+    auto __device__ name(migraphx::fp8::fp8e4m3fnuz x, Ts... xs) MIGRAPHX_RETURNS( \
+        migraphx::fp8::fp8e4m3fnuz(fname(math::as_float(x), math::as_float(xs)...)))
+
 // Template with two overloads for math functions, one for half2 type and one for more generic
 // <half, N> vectorization where N is 4 or another even number.
 
@@ -162,6 +172,33 @@ MIGRAPHX_DEVICE_MATH_HALF(tan, ::tan)
 MIGRAPHX_DEVICE_MATH_HALF(tanh, ::tanh)
 MIGRAPHX_DEVICE_MATH_HALF(fmod, ::fmod)
 
+// use float to compute fp8 overload
+MIGRAPHX_DEVICE_MATH_FP8(abs, ::abs)
+MIGRAPHX_DEVICE_MATH_FP8(acos, ::acos)
+MIGRAPHX_DEVICE_MATH_FP8(acosh, ::acosh)
+MIGRAPHX_DEVICE_MATH_FP8(asin, ::asin)
+MIGRAPHX_DEVICE_MATH_FP8(asinh, ::asinh)
+MIGRAPHX_DEVICE_MATH_FP8(atan, ::atan)
+MIGRAPHX_DEVICE_MATH_FP8(atanh, ::atanh)
+MIGRAPHX_DEVICE_MATH_FP8(ceil, ::ceil)
+MIGRAPHX_DEVICE_MATH_FP8(cos, ::cos)
+MIGRAPHX_DEVICE_MATH_FP8(cosh, ::cosh)
+MIGRAPHX_DEVICE_MATH_FP8(erf, ::erf)
+MIGRAPHX_DEVICE_MATH_FP8(exp, ::exp)
+MIGRAPHX_DEVICE_MATH_FP8(floor, ::floor)
+MIGRAPHX_DEVICE_MATH_FP8(isnan, ::isnan)
+MIGRAPHX_DEVICE_MATH_FP8(log, ::log)
+MIGRAPHX_DEVICE_MATH_FP8(pow, ::pow)
+MIGRAPHX_DEVICE_MATH_FP8(remainder, ::remainder)
+MIGRAPHX_DEVICE_MATH_FP8(round, ::round)
+MIGRAPHX_DEVICE_MATH_FP8(rsqrt, ::rsqrt)
+MIGRAPHX_DEVICE_MATH_FP8(sin, ::sin)
+MIGRAPHX_DEVICE_MATH_FP8(sinh, ::sinh)
+MIGRAPHX_DEVICE_MATH_FP8(sqrt, ::sqrt)
+MIGRAPHX_DEVICE_MATH_FP8(tan, ::tan)
+MIGRAPHX_DEVICE_MATH_FP8(tanh, ::tanh)
+MIGRAPHX_DEVICE_MATH_FP8(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
 // packed into a 32-bit number.  See include/hip/amd_detail/hip_fp16_math_fwd.h for the HIP names
@@ -253,7 +290,7 @@ MIGRAPHX_DEVICE_MATH_VEC(where)
 template <class T, class U>
 constexpr auto convert(U v)
 {
-    return vec_transform(v)([](auto x) -> T { return x; });
+    return vec_transform(v)([](auto x) -> T { return static_cast<T>(x); });
 }
 
 } // namespace migraphx

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

@@ -28,6 +28,7 @@
 #include <migraphx/kernels/index.hpp>
 #include <migraphx/kernels/algorithm.hpp>
 #include <migraphx/kernels/ranges.hpp>
+#include <migraphx/kernels/vec.hpp>
 
 namespace migraphx {
 
@@ -53,9 +54,9 @@ __device__ void pad(const index& idx,
         if(any_of(range_multi.begin(), range_multi.end(), [&](auto j) {
                return multi[j] < offsets[j] or input_idx[j] >= input_bounds[j];
            }))
-            output[multi] = pad_val;
+            output[multi] = implicit_conversion(pad_val);
         else
-            output[multi] = input[input_idx];
+            output[multi] = implicit_conversion(input[input_idx]);
     });
 }
 

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

@@ -45,7 +45,10 @@ __device__ void dpp_reduce(T& in, Op op)
     in  = op(in, out);
     out = dpp_mov<dpp_row_shr(8), 0xf, 0xc>(in);
     in  = op(in, out);
-#if __AMDGCN_WAVEFRONT_SIZE == 64
+#if __AMDGCN_WAVEFRONT_SIZE == 32
+    out = dpp_swizzle<0x1e0>(in);
+    in  = op(in, out);
+#else
     out = dpp_mov<dpp_row_bcast(15), 0xa>(in);
     in  = op(in, out);
     out = dpp_mov<dpp_row_bcast(31), 0xc>(in);
@@ -54,9 +57,11 @@ __device__ void dpp_reduce(T& in, Op op)
 }
 #if defined(MIGRAPHX_USE_CLANG_TIDY) || defined(CPPCHECK)
 // NOLINTNEXTLINE
-#define MIGRAPHX_DPP_REDUCE_ASM(x, ins) x = 1
+#define MIGRAPHX_DPP_REDUCE_ASM(x, ins, f) \
+    (void)f;                               \
+    x = 1
 #elif __AMDGCN_WAVEFRONT_SIZE == 64
-#define MIGRAPHX_DPP_REDUCE_ASM(x, ins)                                       \
+#define MIGRAPHX_DPP_REDUCE_ASM(x, ins, f)                                    \
     __asm__ volatile("s_nop 4\n" #ins " %0 %0 %0 row_shr:1\n"                 \
                      "s_nop 1\n" #ins " %0 %0 %0 row_shr:2\n"                 \
                      "s_nop 1\n" #ins " %0 %0 %0 row_shr:4 bank_mask:0xe\n"   \
@@ -65,29 +70,42 @@ __device__ void dpp_reduce(T& in, Op op)
                      "s_nop 1\n" #ins " %0 %0 %0 row_bcast:31 row_mask:0xc\n" \
                      "s_nop 1\n"                                              \
                      : "=v"(x)                                                \
-                     : "0"(x))
+                     : "0"(x));                                               \
+    (void)f
 #else
-#define MIGRAPHX_DPP_REDUCE_ASM(x, ins)                                     \
+#define MIGRAPHX_DPP_REDUCE_ASM(x, ins, f)                                  \
     __asm__ volatile("s_nop 4\n" #ins " %0 %0 %0 row_shr:1\n"               \
                      "s_nop 1\n" #ins " %0 %0 %0 row_shr:2\n"               \
                      "s_nop 1\n" #ins " %0 %0 %0 row_shr:4 bank_mask:0xe\n" \
                      "s_nop 1\n" #ins " %0 %0 %0 row_shr:8 bank_mask:0xc\n" \
-                     "s_nop 1\n"                                            \
-                     "s_nop 1\n"                                            \
                      : "=v"(x)                                              \
-                     : "0"(x))
+                     : "0"(x));                                             \
+    auto y = dpp_swizzle<0x1e0>(x);                                         \
+    x      = f(x, y)
 #endif
 
 // NOLINTNEXTLINE
-#define MIGRAPHX_DPP_REDUCE(op, prefix, sign)                                                      \
-    __device__ inline void dpp_reduce(double& x, op) { MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_f64); } \
-    __device__ inline void dpp_reduce(float& x, op) { MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_f32); }  \
-    __device__ inline void dpp_reduce(half& x, op) { MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_f16); }   \
-    __device__ inline void dpp_reduce(int32_t& x, op)                                              \
-    {                                                                                              \
-        MIGRAPHX_DPP_REDUCE_ASM(x, prefix##sign##32);                                              \
-    }                                                                                              \
-    __device__ inline void dpp_reduce(uint32_t& x, op) { MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_u32); }
+#define MIGRAPHX_DPP_REDUCE(op, prefix, sign)            \
+    __device__ inline void dpp_reduce(double& x, op f)   \
+    {                                                    \
+        MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_f64, f);     \
+    }                                                    \
+    __device__ inline void dpp_reduce(float& x, op f)    \
+    {                                                    \
+        MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_f32, f);     \
+    }                                                    \
+    __device__ inline void dpp_reduce(half& x, op f)     \
+    {                                                    \
+        MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_f16, f);     \
+    }                                                    \
+    __device__ inline void dpp_reduce(int32_t& x, op f)  \
+    {                                                    \
+        MIGRAPHX_DPP_REDUCE_ASM(x, prefix##sign##32, f); \
+    }                                                    \
+    __device__ inline void dpp_reduce(uint32_t& x, op f) \
+    {                                                    \
+        MIGRAPHX_DPP_REDUCE_ASM(x, prefix##_u32, f);     \
+    }
 
 // Note: when max and min are in int32_t, signed version of instruction needs to be used.
 MIGRAPHX_DPP_REDUCE(op::sum, v_add, _u)
@@ -99,14 +117,10 @@ 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
+    constexpr index_int lanes_per_thread = __AMDGCN_WAVEFRONT_SIZE;
     using type = decltype(index::invoke_loop(f, 0, _c<0>));
     __shared__ type buffer[idx.max_nlocal() / lanes_per_thread];
-    type x = init;
+    type x = type(init);
     idx.local_stride(n, [&](auto i, auto d) { x = op(x, index::invoke_loop(f, i, d)); });
     dpp_reduce(x, op);
 
@@ -117,7 +131,7 @@ __device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
     }
     __syncthreads();
 
-    type y = init;
+    type y = type(init);
     for(index_int i = 0; i < idx.nlocal() / lanes_per_thread; i++)
     {
         y = op(y, buffer[i]);
@@ -244,9 +258,8 @@ struct reducer_base
         {
             auto&& derived = static_cast<const Derived&>(*this);
             auto t         = derived.slice(x);
-            return make_storage_access<typename decltype(t)::type>([=](auto i, auto...) -> auto& {
-                return t[i];
-            });
+            return make_storage_access<typename decltype(t)::type>(
+                [=](auto i, auto...) -> auto& { return t[i]; });
         }
     }
 
@@ -393,7 +406,7 @@ struct block
         {
             using max_iterations = decltype(idx.max_local_stride_iterations(n));
             inner_storage<R, max_iterations{}, N> storage;
-            idx.local_stride(n, [&](auto j, auto d) { storage(j, d) = f(xs(j, d)...); });
+            idx.local_stride(n, [&](auto j, auto d) { storage(j, d) = R{f(xs(j, d)...)}; });
             return storage;
         }
     };
@@ -482,7 +495,7 @@ struct lane
         __device__ auto reduce_impl(Op op, T init, Read read, N n, U&& x, Us&&... xs) const
         {
             using type = remove_reference_t<decltype(x(0, _c<0>))>;
-            type r     = init;
+            type r     = type(init);
             for(index_int j = 0; j < n; j++)
             {
                 r = op(r, read(x(j, _c<0>), xs(j, _c<0>)...));

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

@@ -62,7 +62,7 @@ struct avg_pool
     template <class T>
     MIGRAPHX_DEVICE_CONSTEXPR T final(T x, index_int y)
     {
-        return (y == 0) ? 0.0 : (x / y);
+        return (y == 0) ? T{0.0} : T{x / y};
     }
 };
 
@@ -76,7 +76,7 @@ MIGRAPHX_DEVICE_CONSTEXPR typename Iterator::value_type bilinear_interpolate(
     {
         if(xy[ii] < -1.0f or xy[ii] > dims[ii])
         {
-            return 0;
+            return implicit_conversion(0);
         }
 
         xy[ii]   = migraphx::max(xy[ii], 0.0f);
@@ -92,15 +92,16 @@ MIGRAPHX_DEVICE_CONSTEXPR typename Iterator::value_type bilinear_interpolate(
                                 high[0] * dims[1] + low[1],
                                 high[0] * dims[1] + high[1]};
 
-    float ly                                   = xy[0] - low[0];
-    float lx                                   = xy[1] - low[1];
-    float hy                                   = 1.0f - ly;
-    float hx                                   = 1.0f - lx;
-    array<typename Iterator::value_type, 4> ws = {hy * hx, hy * lx, ly * hx, ly * lx};
+    float ly = xy[0] - low[0];
+    float lx = xy[1] - low[1];
+    float hy = 1.0f - ly;
+    float hx = 1.0f - lx;
+    // do calculations in floating point and convert final result to required type
+    array<float, 4> ws = {hy * hx, hy * lx, ly * hx, ly * lx};
 
     auto v01 = pooling(data[locs[0]] * ws[0], data[locs[1]] * ws[1]);
     auto v23 = pooling(data[locs[2]] * ws[2], data[locs[3]] * ws[3]);
-    return pooling(v01, v23);
+    return implicit_conversion(pooling(v01, v23));
 }
 
 template <class Iterator, class Op>
@@ -113,8 +114,9 @@ MIGRAPHX_DEVICE_CONSTEXPR auto calc_pooling(const Iterator& data,
                                             float roi_offset,
                                             Op op)
 {
-    typename Iterator::value_type output_val = op.init();
-    const int64_t count                      = bin_grid_size[0] * bin_grid_size[1];
+    using in_dtype      = typename Iterator::value_type;
+    in_dtype output_val = in_dtype{op.init()};
+    const int64_t count = bin_grid_size[0] * bin_grid_size[1];
     dfor(bin_grid_size[0], bin_grid_size[1])([&](auto iy, auto ix) {
         array<index_int, 2> id = {iy, ix};
         array<float, 2> locs =
@@ -148,7 +150,6 @@ __device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, W& y_t,
     const auto x    = x_t.begin();
     const auto rois = rois_t.begin();
     const auto ind  = ind_t.begin();
-
     // input shape
     auto x_lens      = x_t.get_shape().lens;
     auto channel_num = x_lens[1];
@@ -176,10 +177,12 @@ __device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, W& y_t,
         const auto offset_rois = rois + (n * roi_column_num);
         const int batch_ind    = ind[n];
 
-        array<float, 2> roi_starts = {offset_rois[1] * s.spatial_scale,
-                                      offset_rois[0] * s.spatial_scale};
-        array<float, 2> roi_ends   = {offset_rois[3] * s.spatial_scale,
-                                    offset_rois[2] * s.spatial_scale};
+        array<float, 2> roi_starts = {
+            static_cast<float>(offset_rois[1]) * static_cast<float>(s.spatial_scale),
+            static_cast<float>(offset_rois[0]) * static_cast<float>(s.spatial_scale)};
+        array<float, 2> roi_ends = {
+            static_cast<float>(offset_rois[3]) * static_cast<float>(s.spatial_scale),
+            static_cast<float>(offset_rois[2]) * static_cast<float>(s.spatial_scale)};
 
         array<float, 2> roi_size{};
         array<float, 2> bin_size{};

src/targets/gpu/include/migraphx/gpu/gather.hpp → src/targets/gpu/kernels/include/migraphx/kernels/scatter_reduction_modes.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -21,42 +21,63 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_GATHER_HPP
-#define MIGRAPHX_GUARD_RTGLIB_GATHER_HPP
+#ifndef MIGRAPHX_GUARD_KERNELS_SCATTER_REDUCTION_MODES_HPP
+#define MIGRAPHX_GUARD_KERNELS_SCATTER_REDUCTION_MODES_HPP
 
-#include <migraphx/argument.hpp>
-#include <migraphx/reflect.hpp>
-#include <migraphx/op/gather.hpp>
-#include <migraphx/gpu/context.hpp>
+#include <migraphx/kernels/types.hpp>
 
 namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
 
-struct context;
+struct assign_none
+{
+    template <class T, class U>
+    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
+    {
+        x = y;
+    }
+};
+
+struct assign_add
+{
+    template <class T, class U>
+    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
+    {
+        atomicAdd(&x, y);
+    }
+};
 
-struct hip_gather
+struct assign_mul
 {
-    op::gather op;
+    template <class T, class U>
+    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
+    {
+        T old = x;
+        T assumed;
+        do
+        {
+            assumed = old;
+            old     = atomicCAS(&x, assumed, assumed * y);
+        } while(assumed != old);
+    }
+};
 
-    template <class Self, class F>
-    static auto reflect(Self& self, F f)
+struct assign_max
+{
+    template <typename T, typename U>
+    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
     {
-        return migraphx::reflect(self.op, f);
+        atomicMax(&x, y);
     }
+};
 
-    std::string name() const { return "gpu::gather"; }
-    shape compute_shape(std::vector<shape> inputs) const;
-    argument
-    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
+struct assign_min
+{
+    template <typename T, typename U>
+    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
     {
-        return shapes.size() - 1;
+        atomicMin(&x, y);
     }
 };
 
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
-
 #endif

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

@@ -26,36 +26,10 @@
 
 #include <migraphx/kernels/index.hpp>
 #include <migraphx/kernels/algorithm.hpp>
+#include <migraphx/kernels/scatter_reduction_modes.hpp>
 
 namespace migraphx {
 
-struct assign_none
-{
-    template <class T, class U>
-    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
-    {
-        x = y;
-    }
-};
-
-struct assign_add
-{
-    template <class T, class U>
-    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
-    {
-        x += y;
-    }
-};
-
-struct assign_mul
-{
-    template <class T, class U>
-    MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
-    {
-        x *= y;
-    }
-};
-
 template <class T, class U, class V, class F>
 __device__ void scatternd(const T& indices_t, const U& updates_t, const V& output_t, F f)
 {

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

@@ -43,7 +43,7 @@ __device__ void softmax(Input input1, Output output)
         auto exp_in = r.inner([&](auto x) { return migraphx::exp(x - c); })(input);
         auto batch_sum =
             r.reduce(op::sum{}, 0, [](auto x) { return migraphx::convert<float>(x); })(exp_in);
-        r.inner([&](auto& y, auto x) { y = x / batch_sum; })(output, exp_in);
+        r.inner([&](auto& y, auto x) { y = implicit_conversion(x / batch_sum); })(output, exp_in);
     });
 }
 

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

@@ -27,6 +27,7 @@
 #include <migraphx/kernels/shape.hpp>
 #include <migraphx/kernels/debug.hpp>
 #include <migraphx/kernels/iota_iterator.hpp>
+#include <migraphx/kernels/float8.hpp>
 
 namespace migraphx {
 

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

@@ -251,7 +251,7 @@ constexpr T numeric_max()
 }
 
 template <class T>
-constexpr T numeric_lowest()
+constexpr auto numeric_lowest() -> decltype(numeric_max<T>())
 {
     if constexpr(is_integral<T>{})
     {

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

@@ -207,7 +207,7 @@ struct implicit_conversion_op
     template <class U>
     constexpr operator U() const
     {
-        return x;
+        return static_cast<U>(x);
     }
 };
 

src/targets/gpu/mlir.cpp

@@ -37,7 +37,7 @@
 #include <mlir-c/Pass.h>
 #include <mlir-c/Support.h>
 #include <mutex>
-#if !defined(MLIR_MIGRAPHX_DIALECT_API_VERSION) || MLIR_MIGRAPHX_DIALECT_API_VERSION != 3
+#if !defined(MLIR_MIGRAPHX_DIALECT_API_VERSION) || MLIR_MIGRAPHX_DIALECT_API_VERSION != 4
 #warning "Incompatible version of rocMLIR library used, disabling"
 // Only undefine when not using cppcheck
 #ifndef CPPCHECK
@@ -73,6 +73,7 @@ namespace gpu {
 
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_MLIR);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNE_EXHAUSTIVE);
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNE_LIMIT);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNING_DB);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNING_CFG);
 
@@ -318,31 +319,30 @@ struct mlir_program
         return result;
     }
 
-    MlirType make_tensor(const shape& s) const
+    MlirType make_mlir_shaped(const shape& s) const
     {
-        if(not s.standard())
-            MIGRAPHX_THROW("MLIR expects all tensors to be in standard shape");
         if(s.dynamic())
             MIGRAPHX_THROW("MLIR does not support dynamic shapes");
         std::vector<int64_t> lens(s.lens().begin(), s.lens().end());
-        return mlirRankedTensorTypeGet(
-            lens.size(), lens.data(), make_type(s.type()), mlirAttributeGetNull());
+        std::vector<int64_t> strides(s.strides().begin(), s.strides().end());
+        return rocmlirMIXRShapedTypeGet(
+            lens.size(), lens.data(), strides.data(), make_type(s.type()));
     }
 
     template <class Range>
-    std::vector<MlirType> make_tensors(const Range& r)
+    std::vector<MlirType> make_mlir_shapeds(const Range& r)
     {
         std::vector<MlirType> result;
         std::transform(r.begin(), r.end(), std::back_inserter(result), [&](const auto& s) {
-            return make_tensor(s);
+            return make_mlir_shaped(s);
         });
         return result;
     }
 
     MlirType make_function_type(const std::vector<shape>& inputs, const std::vector<shape>& outputs)
     {
-        auto in  = make_tensors(inputs);
-        auto out = make_tensors(outputs);
+        auto in  = make_mlir_shapeds(inputs);
+        auto out = make_mlir_shapeds(outputs);
         return mlirFunctionTypeGet(ctx.get(), in.size(), in.data(), out.size(), out.data());
     }
 
@@ -504,11 +504,7 @@ struct mlir_program
 
         mlir_operation_state& add_results(const std::vector<shape>& outputs)
         {
-            std::vector<shape> reshaped(outputs.size());
-            std::transform(outputs.begin(), outputs.end(), reshaped.begin(), [](const shape& r) {
-                return shape{r.type(), r.lens()};
-            });
-            auto x = prog->make_tensors(reshaped);
+            auto x = prog->make_mlir_shapeds(outputs);
             if(not x.empty())
             {
                 mlirOperationStateAddResults(&op_state, x.size(), x.data());
@@ -581,7 +577,7 @@ struct mlir_program
         std::vector<shape> outputs = m.get_output_shapes();
 
         std::vector<MlirLocation> arg_locs(inputs.size(), location);
-        auto body_inputs   = make_tensors(inputs);
+        auto body_inputs   = make_mlir_shapeds(inputs);
         mlir_region region = mlirRegionCreate();
         mlir_block fbody = mlirBlockCreate(body_inputs.size(), body_inputs.data(), arg_locs.data());
         MlirBlock result = fbody.get();
@@ -607,7 +603,7 @@ struct mlir_program
             return "func.return";
         if(ins->name() == "@literal")
         {
-            return "tosa.const";
+            return "migraphx.literal";
         }
         return "migraphx." + ins->name();
     }
@@ -666,7 +662,8 @@ struct mlir_program
             if(ins->name() == "@literal")
             {
                 literal r            = ins->get_literal();
-                MlirType tensor_type = make_tensor(ins->get_shape());
+                MlirType shaped_type = make_mlir_shaped(ins->get_shape());
+                MlirType tensor_type = rocmlirMIXRShapedTypeAsTensor(shaped_type);
                 MlirAttribute mlir_value_attr =
                     mlirDenseElementsAttrRawBufferGet(tensor_type, r.get_shape().bytes(), r.data());
                 ops.add_attributes({{"value", mlir_value_attr}});
@@ -796,7 +793,9 @@ struct mlir_program
         if(enabled(MIGRAPHX_MLIR_TUNE_EXHAUSTIVE{}))
             tuning_mode = RocmlirTuningParamSetKindExhaustive;
         mlir_tuning_space params{mlirRockTuningSpaceCreate(mmodule.get(), tuning_mode)};
-        for(auto i : range(mlirRockTuningGetNumParams(params.get())))
+        const auto limit =
+            value_of(MIGRAPHX_MLIR_TUNE_LIMIT{}, std::numeric_limits<std::size_t>::max());
+        for(auto i : range(std::min<std::size_t>(limit, mlirRockTuningGetNumParams(params.get()))))
         {
             mlir_tuning_param param{mlirRockTuningParamCreate()};
             if(not mlirRockTuningParamGet(params.get(), i, param.get()))
@@ -942,35 +941,7 @@ void adjust_param_shapes(module& m, const std::vector<shape>& inputs)
         auto param        = m.get_parameter(name);
         if(input.standard())
             continue;
-        auto lens    = input.lens();
-        auto strides = input.strides();
-        std::vector<operation> ops;
-        if(input.transposed())
-        {
-            auto perm  = find_permutation(input);
-            auto iperm = invert_permutation(perm);
-            lens       = reorder_dims(lens, iperm);
-            strides    = reorder_dims(strides, iperm);
-            ops.push_back(make_op("transpose", {{"permutation", perm}}));
-        }
-        if(input.broadcasted())
-        {
-            std::transform(lens.begin(),
-                           lens.end(),
-                           strides.begin(),
-                           lens.begin(),
-                           [](auto len, auto stride) -> std::size_t {
-                               if(stride == 0)
-                                   return 1;
-                               return len;
-                           });
-            ops.push_back(make_op("multibroadcast", {{"out_lens", input.lens()}}));
-        }
-        auto new_param =
-            std::accumulate(ops.begin(),
-                            ops.end(),
-                            m.add_parameter(name + ".0", shape{input.type(), lens}),
-                            [&](auto x, auto op) { return m.insert_instruction(param, op, x); });
+        auto new_param = m.add_parameter(name + ".0", input);
         m.replace_instruction(param, new_param);
         m.remove_instruction(param);
     }
@@ -1032,6 +1003,15 @@ tuning_config get_tuning_config_mlir(const context& migraphx_ctx,
     mlir_program mp;
     mp.set_gpu_properties(migraphx_ctx);
     mp.parse(m);
+
+    const bool trace = enabled(MIGRAPHX_TRACE_MLIR{});
+    static std::mutex mutex;
+    if(trace)
+    {
+        const std::lock_guard<std::mutex> lock(mutex);
+        auto mod_op = mlirModuleGetOperation(mp.mmodule.get());
+        std::cout << mlir_print(&mlirOperationPrint, mod_op) << std::endl;
+    }
     return mp.get_tuning_config(exhaustive);
 }
 

src/targets/gpu/prefuse_ops.cpp

@@ -31,6 +31,7 @@
 #ifdef MIGRAPHX_USE_COMPOSABLEKERNEL
 #include <migraphx/gpu/ck.hpp>
 #endif
+#include <migraphx/gpu/fuse_mlir.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -124,34 +125,55 @@ struct find_add_layernorm
     }
 };
 
-#ifdef MIGRAPHX_USE_COMPOSABLEKERNEL
-
 struct pre_gemm_softmax_gemm : gemm_softmax_gemm
 {
     std::string name() const { return "gpu::pre_gemm_softmax_gemm"; }
 };
 MIGRAPHX_REGISTER_OP(pre_gemm_softmax_gemm);
 
-MIGRAPHX_PRED_MATCHER(is_ck_gemm, instruction_ref ins)
+auto is_ck_gemm()
 {
-    if(ins->name() != "dot")
-        return false;
-    if(not pre_gemm_softmax_gemm::is_ck_supported_type(ins->get_shape().type()))
+    return match::make_basic_pred_matcher([=](instruction_ref ins) {
+#ifdef MIGRAPHX_USE_COMPOSABLEKERNEL
+        if(not enabled(MIGRAPHX_ENABLE_CK{}))
+            return false;
+        if(ins->name() != "dot")
+            return false;
+        if(not pre_gemm_softmax_gemm::is_ck_supported_type(ins->get_shape().type()))
+            return false;
+        return true;
+#else
+        (void)ins;
         return false;
-    return true;
+#endif
+    });
+}
+
+auto is_mlir_gemm()
+{
+    return match::make_basic_pred_matcher([=](instruction_ref ins) {
+        if(not mlir_attention_enabled())
+            return false;
+        if(ins->name() != "dot")
+            return false;
+        return std::all_of(ins->inputs().begin(), ins->inputs().end(), [&](auto i) {
+            return pre_gemm_softmax_gemm::is_mlir_supported_type(i->get_shape().type());
+        });
+    });
 }
 
 struct find_gemm_softmax_gemm
 {
     auto matcher() const
     {
-        auto gemm1 =
-            match::skip(match::name("contiguous"))(match::name("dot")(is_ck_gemm().bind("gemm1")));
+        auto gemm1 = match::skip(match::name("contiguous"))(
+            match::name("dot")(match::any_of(is_ck_gemm(), is_mlir_gemm()).bind("gemm1")));
         auto mul = match::name("mul")(
             match::nargs(2), match::either_arg(0, 1)(match::is_constant().bind("scale"), gemm1));
         auto softmax = match::name("softmax")(match::arg(0)(mul)).bind("softmax");
 
-        return match::name("dot")(is_ck_gemm().bind("gemm2"))(match::arg(0)(softmax));
+        return match::name("dot")(match::any_of(is_ck_gemm(), is_mlir_gemm()).bind("gemm2"))(
+            match::arg(0)(softmax));
     }
 
     void apply(module_pass_manager& mpm, const match::matcher_result& r) const
@@ -179,8 +201,6 @@ struct find_gemm_softmax_gemm
     }
 };
 
-#endif
-
 } // namespace
 
 void prefuse_ops::apply(module_pass_manager& mpm) const
@@ -188,10 +208,7 @@ void prefuse_ops::apply(module_pass_manager& mpm) const
     match::find_matches(mpm.get_module(), find_layernorm{});
     mpm.run_pass(dead_code_elimination{});
     match::find_matches(mpm.get_module(), find_add_layernorm{});
-#ifdef MIHRAPHX_USE_COMPOSABLEKERNEL
-    if(enabled(MIGRAPHX_ENABLE_CK{}))
-        match::find_matches(mpm, find_gemm_softmax_gemm{});
-#endif
+    match::find_matches(mpm, find_gemm_softmax_gemm{});
 }
 
 } // namespace gpu

src/targets/gpu/rocblas.cpp

@@ -53,6 +53,16 @@ bool get_compute_fp32_flag()
     return (starts_with(device_name, "gfx9") and device_name >= "gfx908");
 }
 
+bool rocblas_fp8_available()
+{
+#ifndef MIGRAPHX_USE_ROCBLAS_FP8_API
+    return false;
+#else
+    const auto device_name = trim(split_string(get_device_name(), ':').front());
+    return (starts_with(device_name, "gfx9") and device_name >= "gfx940");
+#endif
+}
+
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/target.cpp

@@ -98,12 +98,28 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
     ctx.set_exhaustive_tune_flag(options.exhaustive_tune);
     std::set<shape::type_t> unsupported_types(shape::types().begin(), shape::types().end());
     unsupported_types.erase(shape::type_t::float_type);
+    unsupported_types.erase(shape::type_t::fp8e4m3fnuz_type);
     unsupported_types.erase(shape::type_t::half_type);
     unsupported_types.erase(shape::type_t::bool_type);
     unsupported_types.erase(shape::type_t::int8_type);
     unsupported_types.erase(shape::type_t::uint8_type);
     unsupported_types.erase(shape::type_t::int32_type);
     unsupported_types.erase(shape::type_t::tuple_type);
+    std::set<std::string> unsupported_fp8_ops = {};
+    if(not gpu::rocblas_fp8_available())
+    {
+        unsupported_fp8_ops.insert("dot");
+    }
+    // add all device kernels
+    unsupported_fp8_ops.insert("logsoftmax");
+    unsupported_fp8_ops.insert("nonzero");
+    unsupported_fp8_ops.insert("prefix_scan_sum");
+    unsupported_fp8_ops.insert("scatter_none");
+    unsupported_fp8_ops.insert("topk");
+    unsupported_fp8_ops.insert("rnn_var_sl_shift_output");
+    unsupported_fp8_ops.insert("multinomial");
+    unsupported_fp8_ops.insert("argmax");
+    unsupported_fp8_ops.insert("argmin");
     // clang-format off
     return
     {
@@ -135,6 +151,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
         prefuse_ops{},
         dead_code_elimination{},
         auto_contiguous{},
+        eliminate_data_type{{migraphx::shape::fp8e4m3fnuz_type}, shape::float_type, unsupported_fp8_ops},
+        dead_code_elimination{},
         optimize_module{},
         fuse_pointwise{},
         dead_code_elimination{},

test/gpu/fuse_mlir.cpp

@@ -144,10 +144,12 @@ TEST_CASE(int_quant_dot_tanh_fails)
         auto tanh = add_pointwise(p1, "main:pointwise0", {dot}, single_pointwise("tanh"));
         mm->add_return({tanh});
     }
-    migraphx::program p2(p1);
-    // This pass should do nothing as int32_t tanh isn't supported.
+    // This pass should not fuse as int32_t tanh isn't supported.
     run_pass(p1);
-    EXPECT(p1 == p2);
+    auto* mm = p1.get_main_module();
+    bool has_pointwise =
+        std::any_of(mm->begin(), mm->end(), [&](const auto& i) { return i.name() == "pointwise"; });
+    EXPECT(has_pointwise);
 }
 
 int main(int argc, const char* argv[])

test/gpu/jit.cpp

@@ -139,7 +139,8 @@ const std::string math_template = R"__migraphx__(
 #include <migraphx/kernels/pointwise.hpp>
 #include <migraphx/kernels/math.hpp>
 #include <migraphx/kernels/types.hpp>
-using namespace migraphx;
+
+namespace migraphx {
 extern "C" {
 __global__ void kernel(${type}* p) 
 {
@@ -148,6 +149,7 @@ __global__ void kernel(${type}* p)
 
 }
 }
+}
 
 int main() {}
 
@@ -348,18 +350,19 @@ TEST_CASE(compile_math)
     auto vec_sizes = {2, 4, 6};
     for(auto&& t : migraphx::shape::types())
     {
-        if(contains({migraphx::shape::bool_type,
-                     migraphx::shape::fp8e4m3fnuz_type,
-                     migraphx::shape::tuple_type},
-                    t))
+        if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
             continue;
         auto name = migraphx::shape::cpp_type(t);
         if(t == migraphx::shape::half_type)
             name.insert(0, "migraphx::");
         data_types.push_back(name);
-        migraphx::transform(vec_sizes, std::back_inserter(data_types), [&](auto i) {
-            return "migraphx::vec<" + name + ", " + std::to_string(i) + ">";
-        });
+        // fp8 doesn't have vectorization support yet, therefore skip it for now.
+        if(t != migraphx::shape::fp8e4m3fnuz_type)
+        {
+            migraphx::transform(vec_sizes, std::back_inserter(data_types), [&](auto i) {
+                return "migraphx::vec<" + name + ", " + std::to_string(i) + ">";
+            });
+        }
     }
     migraphx::shape input{migraphx::shape::float_type, {5, 2}};
     migraphx::gpu::hip_compile_options options;
@@ -429,7 +432,6 @@ TEST_CASE(assert_type_min_max)
                 min = std::to_string(as.min());
                 max = std::to_string(as.max());
             }
-
             auto src = migraphx::interpolate_string(assert_template,
                                                     {{"type", name}, {"max", max}, {"min", min}});
             migraphx::shape input{migraphx::shape::float_type, {5, 2}};

test/gpu/mlir.cpp

@@ -141,9 +141,9 @@ TEST_CASE(conv)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_convolution(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-    %0 = migraphx.convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
-    return %0 : tensor<1x2x2x2xf32>
+  func.func @mlir_convolution(%arg0: !migraphx.shaped<2x8x3x3xf32, 72x9x3x1>, %arg1: !migraphx.shaped<1x8x4x4xf32, 128x16x4x1>) -> !migraphx.shaped<1x2x2x2xf32, 8x4x2x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.convolution %arg1, %arg0 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xf32, 128x16x4x1>, <2x8x3x3xf32, 72x9x3x1> -> <1x2x2x2xf32, 8x4x2x1>
+    return %0 : !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>
   }
 }
 )__migraphx__";
@@ -160,15 +160,38 @@ module {
     EXPECT(verify_mlir(m));
 }
 
+TEST_CASE(conv_nhwc)
+{
+    const std::string mlir_output = R"__migraphx__(
+module {
+  func.func @mlir_convolution(%arg0: !migraphx.shaped<2x8x3x3xf32, 72x1x24x8>, %arg1: !migraphx.shaped<1x8x4x4xf32, 128x1x32x8>) -> !migraphx.shaped<1x2x2x2xf32, 8x1x4x2> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.convolution %arg1, %arg0 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xf32, 128x1x32x8>, <2x8x3x3xf32, 72x1x24x8> -> <1x2x2x2xf32, 8x1x4x2>
+    return %0 : !migraphx.shaped<1x2x2x2xf32, 8x1x4x2>
+  }
+}
+)__migraphx__";
+    migraphx::module m;
+    auto x    = m.add_parameter("x", {migraphx::shape::float_type, {1, 8, 4, 4}, {128, 1, 32, 8}});
+    auto w    = m.add_parameter("w", {migraphx::shape::float_type, {2, 8, 3, 3}, {72, 1, 24, 8}});
+    auto conv = m.add_instruction(migraphx::make_op("convolution"), x, w);
+    m.add_return({conv});
+    auto s = migraphx::gpu::dump_mlir(m);
+    // Skip test if MLIR is not enabled
+    if(s.empty())
+        return;
+    CHECK(encode(s) == encode(mlir_output));
+    EXPECT(verify_mlir(m));
+}
+
 TEST_CASE(conv_add_relu)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_convolution_add_relu(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-    %0 = migraphx.convolution(%arg2, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
-    %1 = migraphx.add(%0, %arg0) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
-    %2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
-    return %2 : tensor<1x2x2x2xf32>
+  func.func @mlir_convolution_add_relu(%arg0: !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>, %arg1: !migraphx.shaped<2x8x3x3xf32, 72x9x3x1>, %arg2: !migraphx.shaped<1x8x4x4xf32, 128x16x4x1>) -> !migraphx.shaped<1x2x2x2xf32, 8x4x2x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.convolution %arg2, %arg1 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xf32, 128x16x4x1>, <2x8x3x3xf32, 72x9x3x1> -> <1x2x2x2xf32, 8x4x2x1>
+    %1 = migraphx.add %0, %arg0 : <1x2x2x2xf32, 8x4x2x1>, <1x2x2x2xf32, 8x4x2x1> -> <1x2x2x2xf32, 8x4x2x1>
+    %2 = migraphx.relu %1 : <1x2x2x2xf32, 8x4x2x1> -> <1x2x2x2xf32, 8x4x2x1>
+    return %2 : !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>
   }
 }
 )__migraphx__";
@@ -192,10 +215,10 @@ TEST_CASE(quant_dot_add)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_quant_dot_add(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-    %0 = migraphx.quant_dot(%arg0, %arg1) : (tensor<1x5x4xi8>, tensor<1x4x3xi8>) -> tensor<1x5x3xi32>
-    %1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xi32>, tensor<1x5x3xi32>) -> tensor<1x5x3xi32>
-    return %1 : tensor<1x5x3xi32>
+  func.func @mlir_quant_dot_add(%arg0: !migraphx.shaped<1x5x4xi8, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xi8, 12x3x1>, %arg2: !migraphx.shaped<1x5x3xi32, 15x3x1>) -> !migraphx.shaped<1x5x3xi32, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.quant_dot %arg0, %arg1 : <1x5x4xi8, 20x4x1>, <1x4x3xi8, 12x3x1> -> <1x5x3xi32, 15x3x1>
+    %1 = migraphx.add %0, %arg2 : <1x5x3xi32, 15x3x1>, <1x5x3xi32, 15x3x1> -> <1x5x3xi32, 15x3x1>
+    return %1 : !migraphx.shaped<1x5x3xi32, 15x3x1>
   }
 }
 )__migraphx__";
@@ -219,10 +242,10 @@ TEST_CASE(dot_add)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_dot_add(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-    %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
-    %1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
-    return %1 : tensor<1x5x3xf32>
+  func.func @mlir_dot_add(%arg0: !migraphx.shaped<1x5x4xf32, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xf32, 12x3x1>, %arg2: !migraphx.shaped<1x5x3xf32, 15x3x1>) -> !migraphx.shaped<1x5x3xf32, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.dot %arg0, %arg1 : <1x5x4xf32, 20x4x1>, <1x4x3xf32, 12x3x1> -> <1x5x3xf32, 15x3x1>
+    %1 = migraphx.add %0, %arg2 : <1x5x3xf32, 15x3x1>, <1x5x3xf32, 15x3x1> -> <1x5x3xf32, 15x3x1>
+    return %1 : !migraphx.shaped<1x5x3xf32, 15x3x1>
   }
 }
 )__migraphx__";
@@ -245,11 +268,11 @@ TEST_CASE(conv_int8_dequantize_quantize)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: tensor<2x8x3x3xi8>, %arg1: tensor<1x8x4x4xi8>, %arg2: tensor<1x2x2x2xf32>, %arg3: tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-      %0 = migraphx.quant_convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xi8>, tensor<2x8x3x3xi8>) -> tensor<1x2x2x2xi32>
-      %1 = migraphx.dequantizelinear(%0, %arg2, %arg3) : (tensor<1x2x2x2xi32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xf32>
-      %2 = migraphx.quantizelinear(%1, %arg2, %arg3) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32>
-      return %2 : tensor<1x2x2x2xi32>
+  func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: !migraphx.shaped<2x8x3x3xi8, 72x9x3x1>, %arg1: !migraphx.shaped<1x8x4x4xi8, 128x16x4x1>, %arg2: !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>, %arg3: !migraphx.shaped<1x2x2x2xi32, 8x4x2x1>) -> !migraphx.shaped<1x2x2x2xi32, 8x4x2x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+      %0 = migraphx.quant_convolution %arg1, %arg0 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xi8, 128x16x4x1>, <2x8x3x3xi8, 72x9x3x1> -> <1x2x2x2xi32, 8x4x2x1>
+      %1 = migraphx.dequantizelinear %0, %arg2, %arg3 : <1x2x2x2xi32, 8x4x2x1>, <1x2x2x2xf32, 8x4x2x1>, !migraphx.shaped<1x2x2x2xi32, 8x4x2x1> -> <1x2x2x2xf32, 8x4x2x1>
+      %2 = migraphx.quantizelinear %1, %arg2, %arg3 : <1x2x2x2xf32, 8x4x2x1>, <1x2x2x2xf32, 8x4x2x1>, !migraphx.shaped<1x2x2x2xi32, 8x4x2x1> -> <1x2x2x2xi32, 8x4x2x1>
+      return %2 : !migraphx.shaped<1x2x2x2xi32, 8x4x2x1>
     }
 }
 )__migraphx__";
@@ -278,10 +301,10 @@ TEST_CASE(dot_convert)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_dot_convert(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>) -> tensor<1x5x3xf16> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-    %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
-    %1 = migraphx.convert(%0) {target_type  =  1  :  i64} : (tensor<1x5x3xf32>) -> tensor<1x5x3xf16>
-    return %1 : tensor<1x5x3xf16>
+  func.func @mlir_dot_convert(%arg0: !migraphx.shaped<1x5x4xf32, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xf32, 12x3x1>) -> !migraphx.shaped<1x5x3xf16, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.dot %arg0, %arg1 : <1x5x4xf32, 20x4x1>, <1x4x3xf32, 12x3x1> -> <1x5x3xf32, 15x3x1>
+    %1 = migraphx.convert %0 {target_type  =  1  :  i64} : <1x5x3xf32, 15x3x1> to <1x5x3xf16, 15x3x1>
+    return %1 : !migraphx.shaped<1x5x3xf16, 15x3x1>
   }
 }
 )__migraphx__";
@@ -304,10 +327,10 @@ TEST_CASE(dot_where)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_dot_where(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xi8>, %arg3: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
-    %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
-    %1 = migraphx.where(%arg2, %0, %arg3) : (tensor<1x5x3xi8>, tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
-    return %1 : tensor<1x5x3xf32>
+  func.func @mlir_dot_where(%arg0: !migraphx.shaped<1x5x4xf32, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xf32, 12x3x1>, %arg2: !migraphx.shaped<1x5x3xi8, 15x3x1>, %arg3: !migraphx.shaped<1x5x3xf32, 15x3x1>) -> !migraphx.shaped<1x5x3xf32, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
+    %0 = migraphx.dot %arg0, %arg1 : <1x5x4xf32, 20x4x1>, <1x4x3xf32, 12x3x1> -> <1x5x3xf32, 15x3x1>
+    %1 = migraphx.where %arg2, %0, %arg3 : <1x5x3xi8, 15x3x1>, <1x5x3xf32, 15x3x1>, <1x5x3xf32, 15x3x1> -> <1x5x3xf32, 15x3x1>
+    return %1 : !migraphx.shaped<1x5x3xf32, 15x3x1>
   }
 }
 )__migraphx__";

test/onnx/.onnxrt-commit

-a5537f2f563d4975c7e6121a7eb260bbbfd9455a
+d69842226b47e5336568103541b071447caeb9bf