Merge commit '8c73c72e' into navi-reduce

requirements.txt

@@ -29,4 +29,4 @@ pybind/pybind11@d159a563383d10c821ba7b2a71905d1207db6de4 --build
 msgpack/msgpack-c@cpp-3.3.0 -DMSGPACK_BUILD_TESTS=Off
 sqlite3@3.43.2 -DCMAKE_POSITION_INDEPENDENT_CODE=On
 ROCmSoftwarePlatform/composable_kernel@70eefcf4f263aa5c25f3c9ff0db8f6f199ef0fb9 -DCK_BUILD_JIT_LIB=On -DCMAKE_POSITION_INDEPENDENT_CODE=On
-ROCmSoftwarePlatform/rocMLIR@9e66e8050209f03349a41b6b497f0da2b285a53b -DBUILD_FAT_LIBROCKCOMPILER=On
+ROCmSoftwarePlatform/rocMLIR@a6880f1e6daec99876cd6a4820fbc69c57216401 -DBUILD_FAT_LIBROCKCOMPILER=On

src/eliminate_data_type.cpp

@@ -31,6 +31,72 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
+void insert_convert_to_supported_type(module& m,
+                                      instruction_ref ins,
+                                      migraphx::shape::type_t target_type,
+                                      std::set<migraphx::shape::type_t> unsupported_types)
+{
+    migraphx::shape::type_t orig_type   = ins->get_shape().type();
+    std::vector<instruction_ref> inputs = ins->inputs();
+    std::transform(inputs.begin(), inputs.end(), inputs.begin(), [&](const auto& i) {
+        if(contains(unsupported_types, i->get_shape().type()))
+        {
+            return m.insert_instruction(
+                ins,
+                migraphx::make_op("convert", {{"target_type", migraphx::to_value(target_type)}}),
+                i);
+        }
+        else
+        {
+            return i;
+        }
+    });
+    // if no change
+    if(inputs == ins->inputs())
+        return;
+    auto op         = ins->get_operator();
+    auto attributes = op.attributes();
+    if(attributes.contains("general_data_type"))
+    {
+        op = make_op(attributes["general_data_type"].to<std::string>(), op.to_value());
+    }
+    auto new_ins = m.insert_instruction(ins, op, inputs);
+    if(orig_type == shape::tuple_type)
+    {
+        auto orig_outs = ins->outputs();
+        if(not std::all_of(orig_outs.begin(), orig_outs.end(), [&](const auto out_ins) {
+               return out_ins->name() == "get_tuple_elem";
+           }))
+            MIGRAPHX_THROW(
+                "eliminate_data_type: Instruction with tuple output doesn't have all its "
+                "usages as get_tuple_elem instruction");
+
+        std::transform(
+            orig_outs.begin(), orig_outs.end(), orig_outs.begin(), [&](const auto out_ins) {
+                auto gte_ins       = m.insert_instruction(ins, out_ins->get_operator(), new_ins);
+                auto orig_out_type = out_ins->get_shape().type();
+                if(contains(unsupported_types, orig_out_type))
+                {
+                    auto gte_convert = m.insert_instruction(
+                        ins, make_op("convert", {{"target_type", orig_out_type}}), gte_ins);
+                    return m.replace_instruction(out_ins, gte_convert);
+                }
+                else
+                {
+                    return m.replace_instruction(out_ins, gte_ins);
+                }
+            });
+    }
+    else
+    {
+        auto convert_back_ins = m.insert_instruction(
+            ins,
+            migraphx::make_op("convert", {{"target_type", migraphx::to_value(orig_type)}}),
+            new_ins);
+        m.replace_instruction(ins, convert_back_ins);
+    }
+}
+
 void eliminate_data_type::apply(module& m) const
 {
     static const std::vector<std::string> skip_op_names = {"convert",
@@ -42,31 +108,17 @@ void eliminate_data_type::apply(module& m) const
                                                            "scatternd_add",
                                                            "scatternd_mul",
                                                            "scatternd_none"};
+    if(unsupported_types.empty())
+        return;
+
     for(auto ins : iterator_for(m))
     {
         if(ins->name()[0] == '@')
             continue;
-        if(contains(skip_op_names, ins->name()))
-            continue;
-        auto inputs = ins->inputs();
-        std::transform(inputs.begin(), inputs.end(), inputs.begin(), [&](auto i) {
-            if(types.count(i->get_shape().type()) == 0)
-                return i;
-            return m.insert_instruction(ins, make_op("convert", {{"target_type", target_type}}), i);
-        });
-        if(inputs == ins->inputs())
+        if(contains(skip_op_names, ins->name()) and not contains(unsupported_ops, ins->name()))
             continue;
-        auto op         = ins->get_operator();
-        auto attributes = op.attributes();
-        if(attributes.contains("general_data_type"))
-        {
-            op = make_op(attributes["general_data_type"].to<std::string>(), op.to_value());
-        }
-        auto old_type = ins->get_shape().type();
-        auto out      = m.insert_instruction(ins, op, inputs);
-        auto convert =
-            m.insert_instruction(ins, make_op("convert", {{"target_type", old_type}}), out);
-        m.replace_instruction(ins, convert);
+        if(contains(unsupported_ops, "all") or contains(unsupported_ops, ins->name()))
+            insert_convert_to_supported_type(m, ins, target_type, unsupported_types);
     }
 }
 

src/include/migraphx/eliminate_data_type.hpp

@@ -40,8 +40,9 @@ struct module;
  */
 struct MIGRAPHX_EXPORT eliminate_data_type
 {
-    std::set<shape::type_t> types;
+    std::set<shape::type_t> unsupported_types;
     shape::type_t target_type;
+    std::set<std::string> unsupported_ops = {"all"};
     std::string name() const { return "eliminate_data_type"; }
     void apply(module& m) const;
 };

src/targets/gpu/CMakeLists.txt

@@ -126,7 +126,6 @@ add_library(migraphx_gpu
     fuse_ck.cpp
     fuse_mlir.cpp
     fuse_ops.cpp
-    gather.cpp
     gemm_impl.cpp
     hip.cpp
     kernel.cpp
@@ -140,7 +139,6 @@ add_library(migraphx_gpu
     nonzero.cpp
     pack_args.cpp
     prefuse_ops.cpp
-    pad.cpp
     perfdb.cpp
     pooling.cpp
     reverse.cpp
@@ -168,12 +166,10 @@ endfunction()
 register_migraphx_gpu_ops(hip_
     argmax
     argmin
-    gather
     logsoftmax
     loop
     multinomial
     nonzero
-    pad
     prefix_scan_sum
     reverse
     scatter
@@ -263,6 +259,8 @@ check_library_exists(MIOpen "miopenHiddenSetConvolutionFindMode" "${MIOPEN_LOCAT
 check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_2_API)
 # Beta API for automated GEMM tuning
 check_library_exists(roc::rocblas "rocblas_gemm_ex_get_solutions" "${ROCBLAS_LOCATION}" HAS_ROCBLAS_TUNING_BETA_FEATURE_API)
+# rocblas FP8 API
+check_library_exists(roc::rocblas "rocblas_gemm_strided_batched_ex3" "${ROCBLAS_LOCATION}" HAS_ROCBLAS_FP8_BETA_API)
 
 set(MIGRAPHX_USE_FIND_2_API "${HAS_FIND_2_API}" CACHE BOOL "")
 
@@ -292,10 +290,18 @@ else()
     message(STATUS "rocBLAS does not have User Tuning Beta API")
 endif()
 
+if(HAS_ROCBLAS_FP8_BETA_API)
+    target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_USE_ROCBLAS_FP8_API -DROCBLAS_BETA_FEATURES_API -DROCBLAS_NO_DEPRECATED_WARNINGS)
+    message(STATUS "MIGraphX is using Beta API of rocBLAS for FP8 computations")
+else()
+    message(STATUS "rocBLAS does not have Fp8 Beta API")
+endif()
+
 target_link_libraries(migraphx_gpu PUBLIC migraphx MIOpen roc::rocblas)
 target_link_libraries(migraphx_gpu PRIVATE migraphx_device migraphx_kernels)
 if(MIGRAPHX_USE_COMPOSABLEKERNEL)
     target_link_libraries(migraphx_gpu PRIVATE composable_kernel::jit_library)
+    target_compile_definitions(migraphx_gpu PRIVATE MIGRAPHX_USE_COMPOSABLEKERNEL=1)
 endif()
 
 add_subdirectory(driver)

src/targets/gpu/device/gather.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/shape.hpp>
-#include <migraphx/argument.hpp>
-#include <migraphx/gpu/device/gather.hpp>
-#include <migraphx/gpu/device/tensor.hpp>
-#include <migraphx/gpu/device/launch.hpp>
-#include <migraphx/gpu/device/types.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-namespace device {
-
-argument gather(hipStream_t stream, argument result, argument arg1, argument arg2, int64_t axis)
-{
-    const auto& input_shape = arg1.get_shape();
-    auto lens               = input_shape.lens();
-    auto axis_dim_size      = lens[axis];
-    lens[axis]              = arg2.get_shape().elements();
-    shape out_comp_shape{result.get_shape().type(), lens};
-    std::size_t nelements = result.get_shape().elements();
-
-    visit_all(result, arg1)([&](auto output, auto input_v) {
-        hip_visit_views(input_v, out_comp_shape)([&](auto input, auto out_comp) {
-            arg2.visit([&](auto indices) {
-                const auto* indices_ptr = device_cast(indices.data());
-                auto* output_ptr        = device_cast(output.data());
-                gs_launch(stream, nelements, 256)([=](auto i) __device__ {
-                    auto idx      = out_comp.multi(i);
-                    auto in_index = indices_ptr[idx[axis]];
-                    in_index      = (in_index < 0) ? in_index + axis_dim_size : in_index;
-                    idx[axis]     = in_index;
-                    output_ptr[i] = input[idx];
-                });
-            });
-        });
-    });
-
-    return result;
-}
-
-} // namespace device
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/device/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/shape.hpp>
-#include <migraphx/argument.hpp>
-#include <migraphx/clamp.hpp>
-#include <migraphx/gpu/device/nary.hpp>
-#include <migraphx/gpu/device/pad.hpp>
-#include <migraphx/gpu/device/tensor.hpp>
-#include <migraphx/gpu/device/launch.hpp>
-#include <migraphx/float_equal.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-namespace device {
-
-argument
-pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads)
-{
-    std::size_t nelements = arg1.get_shape().elements();
-    hip_visit_all(result, arg1)([&](auto output, auto input) {
-        using type      = typename decltype(output)::value_type;
-        using hip_index = typename decltype(output)::hip_index;
-        type device_val = pad_clamp<host_type<type>>(value);
-        gs_launch(stream, result.get_shape().elements())(
-            [=](auto i) __device__ { output.data()[i] = device_val; });
-
-        hip_index offsets;
-        std::copy(pads.begin(), pads.begin() + offsets.size(), offsets.begin());
-        gs_launch(stream, nelements)([=](auto i) __device__ {
-            auto idx = input.get_shape().multi(i);
-            for(std::size_t j = 0; j < offsets.size(); j++)
-            {
-                idx[j] += offsets[j];
-            }
-            output[idx] = input.data()[i];
-        });
-    });
-    return result;
-}
-
-} // namespace device
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/fuse_mlir.cpp

@@ -114,10 +114,7 @@ struct mlir_op
             }
             if(ins->name() == "@return")
             {
-                auto s = ins_shapes[ins->inputs().at(0)].with_type(type);
-                if(not s.standard())
-                    MIGRAPHX_THROW("MLIR doesnt support non-standard output");
-                return s;
+                return ins_shapes[ins->inputs().at(0)].with_type(type);
             }
             std::vector<shape> input_shapes;
             input_shapes.resize(ins->inputs().size());
@@ -139,8 +136,15 @@ get_fusable_input_op_stream(instruction_ref lower_input)
 {
     instruction_ref upper_input = lower_input;
     std::vector<operation> op_stream;
-    while(
-        contains({"slice", "transpose", "contiguous", "reshape", "squeeze", "flatten", "unsqueeze"},
+    while(contains({"slice",
+                    "transpose",
+                    "multibroadcast",
+                    "broadcast",
+                    "contiguous",
+                    "reshape",
+                    "squeeze",
+                    "flatten",
+                    "unsqueeze"},
                    upper_input->name()))
     {
         operation op = upper_input->get_operator();

src/targets/gpu/gather.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/gather.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/gpu/device/gather.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape hip_gather::compute_shape(std::vector<shape> inputs) const
-{
-    inputs.pop_back();
-    return op.normalize_compute_shape(inputs);
-}
-
-argument hip_gather::compute(context& ctx, const shape&, const std::vector<argument>& args) const
-{
-    return device::gather(ctx.get_stream().get(), args.back(), args[0], args[1], op.axis);
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/gemm_impl.cpp

@@ -22,11 +22,14 @@
  * THE SOFTWARE.
  */
 
+#include <rocblas/internal/rocblas-types.h>
 #include <rocblas/rocblas.h>
+#include <migraphx/gpu/rocblas.hpp>
 #include <migraphx/gpu/gemm_impl.hpp>
 #include <migraphx/reduce_dims.hpp>
 #include <migraphx/generate.hpp>
 #include <migraphx/time.hpp>
+#include <type_traits>
 
 using microseconds = std::chrono::duration<double, std::micro>;
 
@@ -34,6 +37,20 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
+/*
+Regular rocBLAS API takes compute_type as `rocblas_datatype` enum value v/s "ex3" BETA API takes it
+as `rocblas_computetype` enum value. `rb_compute_type` is faciliator to implictly cast integer enum
+value to required type that can be used inside `common_args` generator.
+*/
+struct rb_compute_type
+{
+    int type = 0;
+    rb_compute_type(rocblas_datatype t) : type(static_cast<int>(t)) {}
+    rb_compute_type(rocblas_computetype t) : type(static_cast<int>(t)) {}
+    operator rocblas_datatype() const { return static_cast<rocblas_datatype>(type); }
+    operator rocblas_computetype() const { return static_cast<rocblas_computetype>(type); }
+};
+
 // Convert rocBLAS datatypes to equivalent Migraphx data types
 rocblas_datatype get_type(shape::type_t type)
 {
@@ -46,7 +63,7 @@ rocblas_datatype get_type(shape::type_t type)
     case shape::uint8_type: return rocblas_datatype_u8_r;
     case shape::int32_type: return rocblas_datatype_i32_r;
     case shape::uint32_type: return rocblas_datatype_u32_r;
-    case shape::fp8e4m3fnuz_type:
+    case shape::fp8e4m3fnuz_type: return rocblas_datatype_f8_r;
     case shape::tuple_type:
     case shape::bool_type:
     case shape::uint16_type:
@@ -183,12 +200,17 @@ struct gemm_impl
         {
             output_type = rocblas_datatype_i32_r;
         }
-        compute_type = output_type;
+        compute_type = rb_compute_type{output_type};
         if(compute_fp32)
         {
             if(arg_type == rocblas_datatype_f16_r)
                 compute_type = rocblas_datatype_f32_r;
         }
+        if(arg_type == rocblas_datatype_f8_r)
+        {
+            assert(get_type(input_shapes[1].type()) == rocblas_datatype_f8_r);
+            compute_type = rocblas_compute_type_f32;
+        }
 
         auto a_lens = input_shapes[0].lens();
         auto b_lens = input_shapes[1].lens();
@@ -216,6 +238,34 @@ struct gemm_impl
     }
 
     void run(context& ctx, const std::vector<argument>& input_args, int32_t solution_idx = 0) const
+    {
+#ifdef MIGRAPHX_USE_ROCBLAS_FP8_API
+        if(rocblas_fp8_available() and
+           std::any_of(input_args.begin(), input_args.end(), [](const auto i) {
+               return i.get_shape().type() == migraphx::shape::fp8e4m3fnuz_type;
+           }))
+        {
+            if(strided_batched)
+            {
+                auto common_args = create_strided_batched_args_common(ctx, input_args);
+                rocblas_invoke(&rocblas_gemm_strided_batched_ex3,
+                               common_args,
+                               rocblas_gemm_algo_standard,
+                               solution_idx,
+                               gemm_flags);
+            }
+            else
+            {
+                auto common_args = create_gemm_ex_args_common(ctx, input_args);
+                rocblas_invoke(&rocblas_gemm_ex3,
+                               common_args,
+                               rocblas_gemm_algo_standard,
+                               solution_idx,
+                               gemm_flags);
+            }
+        }
+        else
+#endif
         {
             if(strided_batched)
             {
@@ -236,6 +286,7 @@ struct gemm_impl
                                gemm_flags);
             }
         }
+    }
 
 #ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
     auto validate(context& ctx, const std::vector<shape>& input_shapes, int32_t solution_idx) const
@@ -331,7 +382,6 @@ struct gemm_impl
                     num_matrices,
                     compute_type);
     }
-
     /**
      * Helper method to create that subset of a long rocBLAS argument list that is common
      * to multiple "gemm_ex..." calls.
@@ -366,6 +416,7 @@ struct gemm_impl
                     ldd,
                     compute_type);
     }
+
 #ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
     /**
      * Find best rocBLAS solution:  Get list of solutions and try them all, returning the index
@@ -481,8 +532,8 @@ struct gemm_impl
     rocblas_int b_stride          = 0;
     rocblas_int c_stride          = 0;
     rocblas_int d_stride          = 0;
-    rocblas_datatype compute_type = rocblas_datatype_f32_r;
     rocblas_datatype arg_type     = rocblas_datatype_f32_r;
+    rb_compute_type compute_type  = rocblas_datatype_f32_r;
     rocblas_datatype output_type  = rocblas_datatype_f32_r;
     bool strided_batched          = true;
     bool is_3inputs               = true;

src/targets/gpu/include/migraphx/gpu/device/gather.hpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_GATHER_HPP
-#define MIGRAPHX_GUARD_RTGLIB_DEVICE_GATHER_HPP
-
-#include <migraphx/argument.hpp>
-#include <migraphx/gpu/device/config.hpp>
-#include <hip/hip_runtime_api.h>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-namespace device {
-
-argument MIGRAPHX_DEVICE_EXPORT
-gather(hipStream_t stream, argument result, argument arg1, argument arg2, int64_t axis);
-
-} // namespace device
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

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

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_PAD_HPP
-#define MIGRAPHX_GUARD_RTGLIB_DEVICE_PAD_HPP
-
-#include <migraphx/argument.hpp>
-#include <migraphx/gpu/device/config.hpp>
-#include <hip/hip_runtime_api.h>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-namespace device {
-
-argument MIGRAPHX_DEVICE_EXPORT pad(hipStream_t stream,
-                                    argument result,
-                                    argument arg1,
-                                    float value,
-                                    std::vector<std::int64_t> pads);
-
-} // namespace device
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

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

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#ifndef MIGRAPHX_GUARD_RTGLIB_GATHER_HPP
-#define MIGRAPHX_GUARD_RTGLIB_GATHER_HPP
-
-#include <migraphx/argument.hpp>
-#include <migraphx/reflect.hpp>
-#include <migraphx/op/gather.hpp>
-#include <migraphx/gpu/context.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct context;
-
-struct hip_gather
-{
-    op::gather op;
-
-    template <class Self, class F>
-    static auto reflect(Self& self, F f)
-    {
-        return migraphx::reflect(self.op, f);
-    }
-
-    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
-    {
-        return shapes.size() - 1;
-    }
-};
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx
-
-#endif

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

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

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

@@ -40,6 +40,8 @@ struct context;
 
 MIGRAPHX_GPU_EXPORT bool get_compute_fp32_flag();
 
+MIGRAPHX_GPU_EXPORT bool rocblas_fp8_available();
+
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/kernels/include/migraphx/kernels/float8.hpp

@@ -501,9 +501,7 @@ class numeric_limits<fp8e5m2fnuz>
     {
         return fp8e5m2fnuz(0x7F, fp8e5m2fnuz::from_bits());
     }
-    // this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01. I am not sure if we
-    // want to make this distinction. For the floating points we would end up using lowest most of
-    // the times.
+    // this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01.
     static constexpr __device__ fp8e5m2fnuz min()
     {
         return fp8e5m2fnuz(0x4, fp8e5m2fnuz::from_bits());
@@ -528,9 +526,7 @@ class numeric_limits<fp8e5m2>
     }
 
     static constexpr __device__ fp8e5m2 max() { return fp8e5m2(0x7B, fp8e5m2::from_bits()); }
-    // this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01. I am not sure if we
-    // want to make this distinction. For the floating points we would end up using lowest most of
-    // the times.
+    // this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01.
     static constexpr __device__ fp8e5m2 min() { return fp8e5m2(0x4, fp8e5m2::from_bits()); }
 
     static constexpr __device__ fp8e5m2 lowest() { return fp8e5m2(0xFB, fp8e5m2::from_bits()); }

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
@@ -319,31 +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());
     }
 
@@ -505,11 +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());
@@ -582,7 +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();
@@ -608,7 +603,7 @@ struct mlir_program
             return "func.return";
         if(ins->name() == "@literal")
         {
-            return "tosa.const";
+            return "migraphx.literal";
         }
         return "migraphx." + ins->name();
     }
@@ -667,7 +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}});
@@ -945,35 +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);
     }

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/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

@@ -105,6 +105,21 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
     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
     {
@@ -136,6 +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/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__";