Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into auto_contig_fix

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

@@ -106,7 +106,7 @@ __device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
 #endif
     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 +117,7 @@ __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 +244,8 @@ 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 +392,7 @@ 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 +481,7 @@ 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/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/pad.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#include <migraphx/gpu/pad.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/gpu/device/pad.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape hip_pad::compute_shape(std::vector<shape> inputs) const
-{
-    inputs.pop_back();
-    check_shapes{inputs, *this}.has(1).standard();
-    return op.compute_shape(inputs);
-}
-
-argument hip_pad::compute(context& ctx, const shape&, const std::vector<argument>& args) const
-{
-    return device::pad(ctx.get_stream().get(), args.back(), args.front(), op.value, op.pads);
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

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/target.cpp

@@ -98,6 +98,7 @@ 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);

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

@@ -350,18 +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;
@@ -431,7 +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

test/onnx/gen_onnx.py

@@ -9543,6 +9543,97 @@ def undefined_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def unique_dynamic_sorted_test():
+    x = helper.make_tensor_value_info('X', TensorProto.FLOAT, [6])
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=1)
+    return ([node], [x], [y, y_ind, x_ind, count])
+
+
+@onnx_test()
+def unique_dynamic_sorted_3D_test():
+    x = helper.make_tensor_value_info('X', TensorProto.INT64, [4, 4, 4])
+    y = helper.make_tensor_value_info('Y', TensorProto.INT64, [16])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [16])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [64])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [16])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        sorted=1)
+    return ([node], [x], [y, y_ind, x_ind, count])
+
+
+@onnx_test()
+def unique_dynamic_unsorted_test():
+    x = helper.make_tensor_value_info('X', TensorProto.FLOAT, [6])
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=0)
+    return ([node], [x], [y, y_ind, x_ind, count])
+
+
+@onnx_test()
+def unique_sorted_test():
+    x = helper.make_tensor('X', TensorProto.FLOAT, [6], [2, 1, 1, 3, 4, 3])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=1)
+    return ([node], [], [y, y_ind, x_ind, count], [x])
+
+
+@onnx_test()
+def unique_unsorted_test():
+    x = helper.make_tensor('X', TensorProto.FLOAT, [6], [2, 1, 1, 3, 4, 3])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
+    y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
+    x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
+                                          [6])
+    count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
+
+    node = onnx.helper.make_node(
+        'Unique',
+        inputs=['X'],
+        outputs=['Y', 'indices', 'inverse_indices', 'counts'],
+        axis=0,
+        sorted=0)
+    return ([node], [], [y, y_ind, x_ind, count], [x])
+
+
 @onnx_test()
 def unknown_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])

test/onnx/onnx_test.cpp

@@ -4826,8 +4826,9 @@ TEST_CASE(multinomial_test)
     migraphx::shape s{migraphx::shape::float_type, {1}};
     std::vector<float> seed_data = {seed};
     auto seed_input              = mm->add_literal(migraphx::literal(s, seed_data));
-    auto rand_dummy =
-        mm->add_literal(migraphx::literal{migraphx::shape::float_type, {batch_size * sample_size}});
+    auto rand_dummy              = mm->add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {batch_size, sample_size}},
+                          std::vector<float>(batch_size * sample_size)});
 
     auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, rand_dummy);
     mm->add_instruction(migraphx::make_op("multinomial"), cdf, randoms);
@@ -4978,8 +4979,9 @@ TEST_CASE(multinomial_int64_test)
     auto seed_input         = mm->add_literal(migraphx::literal(s, data));
 
     // static size
-    auto rand_dummy =
-        mm->add_literal(migraphx::literal{migraphx::shape::float_type, {batch_size * sample_size}});
+    auto rand_dummy = mm->add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {batch_size, sample_size}},
+                          std::vector<float>(batch_size * sample_size)});
     auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, rand_dummy);
     mm->add_instruction(migraphx::make_op("multinomial", {{"dtype", dtype}}), cdf, randoms);
     auto prog = optimize_onnx("multinomial_int64_test.onnx");
@@ -8604,6 +8606,86 @@ TEST_CASE(undefined_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(unique_dynamic_sorted_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s{migraphx::shape::float_type, {6}};
+    auto x = mm->add_parameter("X", s);
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}, {"axis", 0}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+
+    mm->add_return({y, y_ind, x_ind, count});
+    auto prog = migraphx::parse_onnx("unique_dynamic_sorted_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(unique_dynamic_sorted_3D_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s{migraphx::shape::int64_type, {4, 4, 4}};
+    auto x = mm->add_parameter("X", s);
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+
+    mm->add_return({y, y_ind, x_ind, count});
+    auto prog = migraphx::parse_onnx("unique_dynamic_sorted_3D_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(unique_sorted_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s_x{migraphx::shape::float_type, {6}};
+    std::vector<float> x_data = {2, 1, 1, 3, 4, 3};
+    auto x                    = mm->add_literal(migraphx::literal(s_x, x_data));
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}, {"axis", 0}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+    mm->add_return({y, y_idx, x_idx, count});
+    auto prog = migraphx::parse_onnx("unique_sorted_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(unique_unsorted_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape s_x{migraphx::shape::float_type, {6}};
+    std::vector<float> x_data = {2, 1, 1, 3, 4, 3};
+    auto x                    = mm->add_literal(migraphx::literal(s_x, x_data));
+
+    auto out   = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 0}, {"axis", 0}}), x);
+    auto y     = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
+    auto y_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
+    auto x_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
+    auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
+    mm->add_return({y, y_idx, x_idx, count});
+    auto prog = migraphx::parse_onnx("unique_unsorted_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(unknown_test)
 {
     migraphx::program p;

test/onnx/unique_dynamic_sorted_3D_test.onnx

+	unique_dynamic_sorted_3D_test:Ö

+?
+
X
Yindicesinverse_indicescounts"Unique*
+sorted
 
unique_dynamic_sorted_3D_testZ
+
X
+
+
+
+b
+
Y
+
+
+b
+indices
+
+
+b
+inverse_indices
+
+
+@b
+counts
+
+
+B
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