Merge branch 'simplify_1_mul_div_ops' into divide_by_zero_check

src/include/migraphx/permutation.hpp

@@ -55,8 +55,14 @@ inline std::vector<int64_t> sort_permutation(const Vector& data, Op op)
     return result;
 }
 
+/*!
+ * Returns the permutation needed to apply to the shape to undo the current permutation
+ */
 std::vector<int64_t> invert_permutation(const std::vector<int64_t>& permutation);
 
+/*!
+ * Finds the permutation most likely from a transpose operator that has been applied to the shape.
+ */
 std::vector<int64_t> find_permutation(const shape& s);
 std::vector<int64_t> find_permutation(const std::vector<shape>& shapes);
 

src/include/migraphx/program.hpp

@@ -33,6 +33,8 @@
 #include <migraphx/instruction_ref.hpp>
 #include <migraphx/target.hpp>
 #include <migraphx/compile_options.hpp>
+#include <migraphx/target_assignments.hpp>
+#include <migraphx/assignment_options.hpp>
 #include <migraphx/env.hpp>
 #include <migraphx/config.hpp>
 #include <algorithm>
@@ -84,6 +86,9 @@ struct program
 
     instruction_ref validate() const;
 
+    target_assignments get_target_assignments(const std::vector<target>& targets,
+                                              assignment_options options = assignment_options{});
+
     void compile(const target& t, compile_options options = compile_options{});
 
     bool is_compiled() const;

src/include/migraphx/ranges.hpp

@@ -198,6 +198,12 @@ void transform(Range&& r, Iterator it, F f)
     std::transform(r.begin(), r.end(), it, f);
 }
 
+template <class Range1, class Range2, class Iterator, class F>
+void transform(Range1&& r1, Range2&& r2, Iterator it, F f)
+{
+    std::transform(r1.begin(), r1.end(), r2.begin(), it, f);
+}
+
 template <class Range>
 auto reverse(Range& r)
 {

src/include/migraphx/shape.hpp

@@ -82,6 +82,23 @@ struct shape
     {
     };
 
+    struct dynamic_dimension
+    {
+        std::size_t min = 0;
+        std::size_t max = 0;
+        std::size_t opt = 0;
+
+        template <class Self, class F>
+        static auto reflect(Self& self, F f);
+
+        bool is_fixed() const;
+        bool has_optimal() const;
+
+        friend bool operator==(const dynamic_dimension& x, const dynamic_dimension& y);
+        friend bool operator!=(const dynamic_dimension& x, const dynamic_dimension& y);
+        friend std::ostream& operator<<(std::ostream& os, const dynamic_dimension& x);
+    };
+
     static const std::vector<type_t>& types();
 
     static std::string name(type_t t);
@@ -92,6 +109,12 @@ struct shape
     shape(type_t t, std::vector<std::size_t> l);
     shape(type_t t, std::vector<std::size_t> l, std::vector<std::size_t> s);
 
+    // Force all calls of the format `shape( type_t, { size_t compatibles } )` to map to
+    // shape(type_t, std::vector<std::size_t> l)
+    shape(type_t t, std::initializer_list<std::size_t> d);
+
+    shape(type_t t, std::vector<dynamic_dimension> dims);
+
     template <class Range>
     shape(type_t t, const Range& l) : shape(t, std::vector<std::size_t>(l.begin(), l.end()))
     {
@@ -112,10 +135,44 @@ struct shape
     type_t type() const;
     const std::vector<std::size_t>& lens() const;
     const std::vector<std::size_t>& strides() const;
+
+    /*!
+     * Return the number of elements in the tensor.
+     */
     std::size_t elements() const;
+
+    /*!
+     * Return the number of total bytes used for storage of the tensor data; includes subshapes.
+     * For dynamic shape, returns the maximum number of bytes presuming a packed shape.
+     */
     std::size_t bytes() const;
+
+    /*!
+     * Return the size of the type of the main shape.
+     * Returns 0 if there are subshapes.
+     */
     std::size_t type_size() const;
 
+    const std::vector<dynamic_dimension>& dyn_dims() const;
+
+    /*!
+     * Minimum lengths for dynamic shape.
+     * lens() for fixed shape.
+     */
+    std::vector<std::size_t> min_lens() const;
+
+    /*!
+     * Maximum lengths for dynamic shape.
+     * lens() for fixed shape.
+     */
+    std::vector<std::size_t> max_lens() const;
+
+    /*!
+     * Optimum lengths for dynamic shape.
+     * lens() for fixed shape.
+     */
+    std::vector<std::size_t> opt_lens() const;
+
     /// Map multiple indices to space index
     std::size_t index(std::initializer_list<std::size_t> l) const;
     /// Map multiple indices to space index
@@ -136,19 +193,27 @@ struct shape
     std::vector<std::size_t> multi(std::size_t i) const;
     void multi_copy(std::size_t i, std::size_t* start, const std::size_t* end) const;
 
-    /// Returns true if the shape is packed with no padding
+    /// Returns true if the shape is packed (number of elements and buffer size the same) with no
+    /// padding
     bool packed() const;
+
     /// Returns true is the shape has been transposed. That is the strides are not in descending
     /// order
     bool transposed() const;
+
     /// Returns true if the shape is broadcasting a dimension. That is, one of the strides are zero
     bool broadcasted() const;
+
     /// Returns true if the shape is in its standard format. That is, the shape is both packed and
     /// not transposed.
     bool standard() const;
+
     /// Returns true if all strides are equal to 0 (scalar tensor)
     bool scalar() const;
 
+    /// Return true if the shape is dynamic
+    bool dynamic() const;
+
     shape normalize_standard() const;
 
     shape with_lens(type_t t, const std::vector<std::size_t>& l) const;
@@ -191,6 +256,10 @@ struct shape
 
         std::size_t size(std::size_t n = 1) const { return sizeof(type) * n; }
 
+        auto is_integral() const { return std::is_integral<type>{}; }
+        auto is_signed() const { return std::is_signed<type>{}; }
+        auto is_unsigned() const { return std::is_unsigned<type>{}; }
+
         template <class U>
         type* from(U* buffer, std::size_t n = 0) const
         {
@@ -248,6 +317,11 @@ struct shape
 
     const std::vector<shape>& sub_shapes() const;
 
+    /*!
+     * Returns the number of elements in the data buffer.
+     * For a dynamic shape, returns the maximum number of elements of the data buffer and assumes it
+     * is packed.
+     */
     std::size_t element_space() const;
 
     private:

src/include/migraphx/support_metric.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_MIGRAPHX_SUPPORT_METRIC_HPP
+#define MIGRAPHX_GUARD_MIGRAPHX_SUPPORT_METRIC_HPP
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+enum class support_metric
+{
+    latency,
+    throughput
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_MIGRAPHX_SUPPORT_METRIC_HPP

src/include/migraphx/target.hpp

@@ -37,6 +37,8 @@
 #include <migraphx/compile_options.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/support_metric.hpp>
+#include <migraphx/instruction_ref.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -61,6 +63,13 @@ struct target
      * @return The context to be used during compilation and execution.
      */
     context get_context() const;
+    /**
+     * @brief Check how well an instruction is supported on a target with the given metric
+     * @param ins Instruction to check if it's supported
+     * @param metric Used to define how the return value should be interpreted
+     * @return The value based on the chosen metric. Negative numbers mean unsupported
+     */
+    float is_supported(T&, instruction_ref ins, support_metric m) const;
     /**
      * @brief copy an argument to the current target.
      *
@@ -105,6 +114,12 @@ argument copy_from_target(T&, const argument& arg)
     return arg;
 }
 
+template <class T>
+float target_is_supported(T&, instruction_ref, support_metric)
+{
+    return 0;
+}
+
 #ifdef TYPE_ERASED_DECLARATION
 
 // Type-erased interface for:
@@ -117,6 +132,8 @@ struct target
     //
     context get_context() const;
     // (optional)
+    float is_supported(instruction_ref ins, support_metric m) const;
+    // (optional)
     argument copy_to(const argument& input) const;
     // (optional)
     argument copy_from(const argument& input) const;
@@ -207,6 +224,12 @@ struct target
         return (*this).private_detail_te_get_handle().get_context();
     }
 
+    float is_supported(instruction_ref ins, support_metric m) const
+    {
+        assert((*this).private_detail_te_handle_mem_var);
+        return (*this).private_detail_te_get_handle().is_supported(ins, m);
+    }
+
     argument copy_to(const argument& input) const
     {
         assert((*this).private_detail_te_handle_mem_var);
@@ -242,11 +265,31 @@ struct target
         virtual std::vector<pass> get_passes(context& ctx,
                                              const compile_options& options) const = 0;
         virtual context get_context() const                                        = 0;
+        virtual float is_supported(instruction_ref ins, support_metric m) const    = 0;
         virtual argument copy_to(const argument& input) const                      = 0;
         virtual argument copy_from(const argument& input) const                    = 0;
         virtual argument allocate(const shape& s) const                            = 0;
     };
 
+    template <class T>
+    static auto private_detail_te_default_is_supported(char,
+                                                       T&& private_detail_te_self,
+                                                       instruction_ref ins,
+                                                       support_metric m)
+        -> decltype(private_detail_te_self.is_supported(ins, m))
+    {
+        return private_detail_te_self.is_supported(ins, m);
+    }
+
+    template <class T>
+    static float private_detail_te_default_is_supported(float,
+                                                        T&& private_detail_te_self,
+                                                        instruction_ref ins,
+                                                        support_metric m)
+    {
+        return target_is_supported(private_detail_te_self, ins, m);
+    }
+
     template <class T>
     static auto
     private_detail_te_default_copy_to(char, T&& private_detail_te_self, const argument& input)
@@ -329,6 +372,12 @@ struct target
 
         context get_context() const override { return private_detail_te_value.get_context(); }
 
+        float is_supported(instruction_ref ins, support_metric m) const override
+        {
+
+            return private_detail_te_default_is_supported(char(0), private_detail_te_value, ins, m);
+        }
+
         argument copy_to(const argument& input) const override
         {
 

src/include/migraphx/target_assignments.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_MIGRAPHX_ASSIGNMENT_HPP
+#define MIGRAPHX_GUARD_MIGRAPHX_ASSIGNMENT_HPP
+
+#include <unordered_map>
+
+#include <migraphx/instruction_ref.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct target_assignments
+{
+    void add_assignment(instruction_ref ins, const std::string& target);
+
+    auto begin() const { return assignments.cbegin(); }
+    auto end() const { return assignments.cend(); }
+
+    private:
+    std::unordered_map<instruction_ref, std::string> assignments;
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_MIGRAPHX_ASSIGNMENT_HPP

src/module.cpp

@@ -410,7 +410,8 @@ module::add_instructions(const std::vector<instruction_ref>& instructions,
 }
 
 std::vector<instruction_ref>
-module::add_instructions(module_ref m, std::unordered_map<instruction_ref, instruction_ref> map_ins)
+module::add_instructions(const_module_ref m,
+                         std::unordered_map<instruction_ref, instruction_ref> map_ins)
 {
     return this->insert_instructions(this->end(), m, std::move(map_ins));
 }
@@ -431,8 +432,10 @@ module::insert_instructions(instruction_ref ins,
     return insert_generic_instructions(*this, ins, instructions, std::move(map_ins));
 }
 
-std::vector<instruction_ref> module::insert_instructions(
-    instruction_ref ins, module_ref m, std::unordered_map<instruction_ref, instruction_ref> map_ins)
+std::vector<instruction_ref>
+module::insert_instructions(instruction_ref ins,
+                            const_module_ref m,
+                            std::unordered_map<instruction_ref, instruction_ref> map_ins)
 {
     return insert_generic_instructions(*this, ins, iterator_for(*m), std::move(map_ins));
 }
@@ -447,11 +450,7 @@ module::insert_instructions(instruction_ref ins,
     return insert_generic_instructions(*this, ins, iterator_for(r), std::move(map_ins));
 }
 
-instruction_ref module::add_literal(literal l)
-{
-    impl->emplace_front(std::move(l));
-    return impl->instructions.begin();
-}
+instruction_ref module::add_literal(literal l) { return insert_literal(begin(), std::move(l)); }
 
 instruction_ref module::add_outline(const shape& s)
 {
@@ -461,10 +460,7 @@ instruction_ref module::add_outline(const shape& s)
 
 instruction_ref module::add_parameter(std::string name, shape s)
 {
-    assert(get_parameter_shape(name) == shape{});
-    impl->push_front({builtin::param{std::move(name), impl->nparams}, std::move(s), {}});
-    impl->nparams++;
-    return impl->instructions.begin();
+    return insert_parameter(begin(), std::move(name), std::move(s));
 }
 
 instruction_ref module::add_return(std::vector<instruction_ref> args)
@@ -477,6 +473,20 @@ instruction_ref module::add_return(std::vector<instruction_ref> args)
     return result;
 }
 
+instruction_ref module::insert_literal(instruction_ref ins, literal l)
+{
+    impl->emplace(ins, std::move(l));
+    return std::prev(ins);
+}
+
+instruction_ref module::insert_parameter(instruction_ref ins, std::string name, shape s)
+{
+    assert(get_parameter_shape(name) == shape{});
+    impl->insert(ins, {builtin::param{std::move(name), impl->nparams}, std::move(s), {}});
+    impl->nparams++;
+    return std::prev(ins);
+}
+
 instruction_ref module::replace_return(std::vector<instruction_ref> args)
 {
     auto last = std::prev(this->end());

src/onnx/include/migraphx/onnx/onnx_parser.hpp

@@ -93,9 +93,10 @@ struct onnx_parser
         onnx_parser&, const node_info&, std::vector<instruction_ref>)>;
     node_map nodes;
     std::unordered_map<std::string, instruction_ref> instructions;
-    program prog                  = program();
-    std::size_t default_dim_value = 1;
+    program prog                                   = program();
+    shape::dynamic_dimension default_dyn_dim_value = {1, 1, 0};
     std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims;
+    std::unordered_map<std::string, std::vector<shape::dynamic_dimension>> map_dyn_input_dims;
     bool skip_unknown_operators = false;
     int64_t max_loop_iterations = 10;
     int64_t opset_version       = 13;

src/onnx/onnx.cpp

@@ -41,8 +41,25 @@ template <class... Ts>
 program parse_onnx_from(const onnx_options& options, Ts&&... xs)
 {
     onnx::onnx_parser parser;
-    parser.map_input_dims         = options.map_input_dims;
-    parser.default_dim_value      = options.default_dim_value;
+    parser.map_input_dims     = options.map_input_dims;
+    parser.map_dyn_input_dims = options.map_dyn_input_dims;
+    auto dim_val              = options.default_dim_value;
+    if(dim_val != 0)
+    {
+        if(options.default_dyn_dim_value != shape::dynamic_dimension{1, 1, 0})
+        {
+            MIGRAPHX_THROW("PARSE_ONNX_FROM: both default_dim_value and default_dyn_dim_value"
+                           "set to non-default value");
+        }
+        else
+        {
+            parser.default_dyn_dim_value = {dim_val, dim_val, 0};
+        }
+    }
+    else
+    {
+        parser.default_dyn_dim_value = options.default_dyn_dim_value;
+    }
     parser.skip_unknown_operators = options.skip_unknown_operators;
     parser.max_loop_iterations    = options.max_loop_iterations;
 

src/onnx/onnx_parser.cpp

@@ -35,9 +35,11 @@
 #include <migraphx/file_buffer.hpp>
 #include <migraphx/filesystem.hpp>
 #include <migraphx/op/unknown.hpp>
+#include <migraphx/env.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
+
 namespace onnx {
 
 static onnx_parser::attribute_map get_attributes(const onnx::NodeProto& node)
@@ -255,6 +257,11 @@ int64_t onnx_parser::get_opset_version(const onnx::ModelProto& model)
 
 void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
 {
+    if(not map_input_dims.empty() and not map_dyn_input_dims.empty())
+    {
+        MIGRAPHX_THROW("PARSE_GRAPH: both map_input_dims and map_dyn_input_dims non-empty, only"
+                       "one should be used");
+    }
     std::unordered_map<std::string, instruction_ref> mod_insts;
     for(auto&& f : graph.initializer())
     {
@@ -268,7 +275,7 @@ void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
         // input not in initializer_data, so it is a real input
         if(!contains(mod_insts, name))
         {
-            // ONNX specification does not specify hwo to deal with the
+            // ONNX specification does not specify how to deal with the
             // scenario that a nested subgraph contains a parameter with the
             // name existed in its parent graph.
             // In the current implementation, MIGraphX throws an exception for that.
@@ -278,13 +285,22 @@ void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
                                "\" existing in parent graph!");
             }
 
+            shape s;
             std::vector<std::size_t> dims;
             if(map_input_dims.count(name) > 0)
             {
                 dims = map_input_dims.at(name);
+                s    = parse_type(input.type(), dims);
+            }
+            else if(map_dyn_input_dims.count(name) > 0)
+            {
+                shape::type_t shape_type = get_type(input.type().tensor_type().elem_type());
+                s                        = {shape_type, map_dyn_input_dims.at(name)};
+            }
+            else
+            {
+                s = parse_type(input.type(), dims);
             }
-
-            shape s         = parse_type(input.type(), dims);
             mod_insts[name] = mod->add_parameter(name, s);
         }
     }
@@ -439,30 +455,41 @@ shape onnx_parser::parse_type(const onnx::TypeProto& t,
         return {shape_type, input_dims};
     }
 
-    std::vector<std::size_t> dims;
+    std::vector<shape::dynamic_dimension> dynamic_dims;
     auto&& tensor_dims = t.tensor_type().shape().dim();
     std::transform(tensor_dims.begin(),
                    tensor_dims.end(),
-                   std::back_inserter(dims),
-                   [&](auto&& d) -> std::size_t {
+                   std::back_inserter(dynamic_dims),
+                   [&](auto&& d) -> shape::dynamic_dimension {
                        if(d.has_dim_value())
                        {
                            if(static_cast<int>(d.dim_value()) <= 0)
                            {
-                               return default_dim_value;
+                               return default_dyn_dim_value;
                            }
-                           return d.dim_value();
+                           std::size_t tmp = d.dim_value();
+                           return {tmp, tmp, 0};
                        }
                        else
                        {
-                           return default_dim_value;
+                           return default_dyn_dim_value;
                        }
                    });
 
-    if(dims.empty())
+    if(dynamic_dims.empty())
+    {
         return {shape_type};
-
-    return {shape_type, dims};
+    }
+    if(std::all_of(dynamic_dims.begin(), dynamic_dims.end(), [](auto dd) { return dd.is_fixed(); }))
+    {
+        std::vector<std::size_t> dims;
+        std::transform(dynamic_dims.begin(),
+                       dynamic_dims.end(),
+                       std::back_inserter(dims),
+                       [](auto d) { return d.max; });
+        return {shape_type, dims};
+    }
+    return {shape_type, dynamic_dims};
 }
 
 shape::type_t get_type(int dtype)

src/program.cpp

@@ -159,6 +159,25 @@ instruction_ref program::validate() const
     return mm->validate();
 }
 
+target_assignments program::get_target_assignments(const std::vector<target>& targets,
+                                                   assignment_options options)
+{
+    const auto m = options.metric;
+
+    target_assignments p;
+
+    const auto* mod = get_main_module();
+    for(auto it : iterator_for(*mod))
+    {
+        auto t = std::max_element(
+            targets.begin(), targets.end(), [it, m](const target& lhs, const target& rhs) {
+                return lhs.is_supported(it, m) < rhs.is_supported(it, m);
+            });
+        p.add_assignment(it, t->name());
+    }
+    return p;
+}
+
 bool program::is_compiled() const { return not this->impl->target_name.empty(); }
 
 void program::compile(const target& t, compile_options options)
@@ -514,12 +533,14 @@ static void mod_from_val(module_ref mod,
 
         if(name == "@param")
         {
-            output = mod->add_parameter(fields["parameter"].to<std::string>(),
-                                        migraphx::from_value<shape>(node.at("shape")));
+            output = mod->insert_parameter(mod->end(),
+                                           fields["parameter"].to<std::string>(),
+                                           migraphx::from_value<shape>(node.at("shape")));
         }
         else if(name == "@literal")
         {
-            output = mod->add_literal(migraphx::from_value<literal>(node.at("literal")));
+            output =
+                mod->insert_literal(mod->end(), migraphx::from_value<literal>(node.at("literal")));
         }
         else
         {
@@ -554,11 +575,11 @@ static void mod_from_val(module_ref mod,
             }
             else if(module_inputs.empty())
             {
-                output = mod->add_instruction(op, inputs);
+                output = mod->insert_instruction(mod->end(), op, inputs);
             }
             else
             {
-                output = mod->add_instruction(op, inputs, module_inputs);
+                output = mod->insert_instruction(mod->end(), op, inputs, module_inputs);
             }
         }
         output->set_normalized(normalized);
@@ -691,11 +712,13 @@ void program::perf_report(std::ostream& os,
     double overhead_percent       = overhead_time * 100.0 / total_time;
     double total_instruction_time = 0.0;
     std::unordered_map<std::string, double> op_times;
+    std::unordered_map<std::string, std::size_t> op_n;
     for(auto&& p : ins_vec)
     {
         double avg = common_average(p.second);
         op_times[perf_group(p.first->get_operator())] += avg;
         total_instruction_time += avg;
+        op_n[perf_group(p.first->get_operator())]++;
     }
     double calculate_overhead_time    = total_time - total_instruction_time;
     double calculate_overhead_percent = calculate_overhead_time * 100.0 / total_time;
@@ -716,18 +739,19 @@ void program::perf_report(std::ostream& os,
 
     os << std::endl;
     os << "Summary:" << std::endl;
-    std::vector<std::pair<double, std::string>> op_times_sorted;
-    std::transform(op_times.begin(),
-                   op_times.end(),
-                   std::back_inserter(op_times_sorted),
-                   [](auto p) { return std::make_pair(p.second, p.first); });
+    std::vector<std::tuple<double, std::size_t, std::string>> op_times_sorted;
+    std::transform(
+        op_times.begin(), op_times.end(), std::back_inserter(op_times_sorted), [&](auto p) {
+            auto&& name = p.first;
+            return std::make_tuple(p.second, op_n.at(name), name);
+        });
     std::sort(op_times_sorted.begin(), op_times_sorted.end(), std::greater<>{});
-    for(auto&& p : op_times_sorted)
+    for(auto&& [avg, nn, name] : op_times_sorted)
     {
-        auto&& name    = p.second;
-        double avg     = p.first;
         double percent = std::ceil(100.0 * avg / total_instruction_time);
-        os << name << ": " << avg << "ms, " << percent << "%" << std::endl;
+        double per_ins = avg / nn;
+        os << name << ": " << avg << "ms / " << nn << " = " << per_ins << "ms, " << percent << "%"
+           << std::endl;
     }
 
     os << std::endl;

src/serialize.cpp

@@ -36,7 +36,7 @@ void raw_data_to_value(value& v, const RawData& rd)
     result["shape"] = migraphx::to_value(rd.get_shape());
     if(rd.get_shape().type() == shape::tuple_type)
         result["sub"] = migraphx::to_value(rd.get_sub_objects());
-    else
+    else if(not rd.empty())
         result["data"] = migraphx::value::binary(rd.data(), rd.get_shape().bytes());
     v = result;
 }
@@ -56,7 +56,7 @@ void migraphx_from_value(const value& v, argument& a)
         literal l = migraphx::from_value<literal>(v);
         a         = l.get_argument();
     }
-    else
+    else if(v.contains("sub"))
     {
         a = migraphx::from_value<std::vector<argument>>(v.at("sub"));
     }

src/shape.cpp

@@ -26,6 +26,7 @@
 #include <migraphx/stringutils.hpp>
 #include <migraphx/serialize.hpp>
 #include <migraphx/permutation.hpp>
+#include <migraphx/ranges.hpp>
 #include <numeric>
 #include <algorithm>
 #include <functional>
@@ -65,13 +66,21 @@ struct shape_impl
                      std::is_sorted(m_strides.rbegin(), m_strides.rend());
     }
 
+    shape_impl(shape::type_t t, std::vector<shape::dynamic_dimension> dims)
+        : m_type(t), m_dyn_dims(std::move(dims))
+    {
+    }
+
     shape_impl(const std::vector<shape>& subs) : m_type(shape::tuple_type), m_shapes(subs) {}
+
     shape::type_t m_type;
     std::vector<std::size_t> m_lens    = {};
     std::vector<std::size_t> m_strides = {};
     std::vector<shape> m_shapes        = {};
     bool m_standard                    = false;
 
+    std::vector<shape::dynamic_dimension> m_dyn_dims = {};
+
     void calculate_strides()
     {
         m_strides.clear();
@@ -87,6 +96,12 @@ struct shape_impl
 
     std::size_t element_space() const
     {
+        if(not m_dyn_dims.empty())
+        {
+            auto maxes = max_lens();
+            return std::accumulate(maxes.begin(), maxes.end(), std::size_t{1}, std::multiplies<>());
+        }
+
         assert(m_lens.size() == m_strides.size());
         if(m_lens.empty())
             return 0;
@@ -101,6 +116,11 @@ struct shape_impl
 
     std::size_t elements() const
     {
+        if(not m_dyn_dims.empty())
+        {
+            MIGRAPHX_THROW("SHAPE: elements() called on dynamic shape");
+        }
+
         assert(m_lens.size() == m_strides.size());
         if(m_lens.empty())
             return 0;
@@ -108,6 +128,35 @@ struct shape_impl
             m_lens.begin(), m_lens.end(), std::size_t{1}, std::multiplies<std::size_t>());
     }
 
+    std::vector<std::size_t> min_lens() const
+    {
+        std::vector<std::size_t> ret(m_dyn_dims.size());
+        std::transform(m_dyn_dims.cbegin(),
+                       m_dyn_dims.cend(),
+                       ret.begin(),
+                       [](shape::dynamic_dimension x) { return x.min; });
+        return ret;
+    }
+
+    std::vector<std::size_t> max_lens() const
+    {
+        std::vector<std::size_t> ret(m_dyn_dims.size());
+        std::transform(m_dyn_dims.cbegin(),
+                       m_dyn_dims.cend(),
+                       ret.begin(),
+                       [](shape::dynamic_dimension x) { return x.max; });
+        return ret;
+    }
+
+    std::vector<std::size_t> opt_lens() const
+    {
+        std::vector<std::size_t> ret(m_dyn_dims.size());
+        std::transform(m_dyn_dims.cbegin(),
+                       m_dyn_dims.cend(),
+                       ret.begin(),
+                       [](shape::dynamic_dimension x) { return x.opt; });
+        return ret;
+    }
     // Does the shape skip over elements?
     bool skips() const
     {
@@ -165,6 +214,16 @@ shape::shape(type_t t, std::vector<std::size_t> l, std::vector<std::size_t> s)
 {
 }
 
+shape::shape(type_t t, std::initializer_list<std::size_t> d)
+    : shape::shape(t, std::vector<std::size_t>{d.begin(), d.end()})
+{
+}
+
+shape::shape(type_t t, std::vector<shape::dynamic_dimension> dims)
+    : impl(std::make_shared<shape_impl>(t, std::move(dims)))
+{
+}
+
 shape::shape(const std::vector<shape>& subs) : impl(std::make_shared<shape_impl>(subs)) {}
 
 shape::shape(std::shared_ptr<shape_impl> pimpl) : impl(std::move(pimpl)) {}
@@ -180,9 +239,13 @@ shape shape::from_permutation(type_t t,
 }
 
 shape::type_t shape::type() const { return impl->m_type; }
+
 const std::vector<std::size_t>& shape::lens() const { return impl->m_lens; }
+
 const std::vector<std::size_t>& shape::strides() const { return impl->m_strides; }
+
 std::size_t shape::elements() const { return impl->elements(); }
+
 std::size_t shape::bytes() const
 {
     if(this->sub_shapes().empty())
@@ -199,6 +262,7 @@ std::size_t shape::bytes() const
                                [&](auto x, auto y) { return x + y.bytes(); });
     }
 }
+
 std::size_t shape::type_size() const
 {
     std::size_t n = 0;
@@ -206,20 +270,35 @@ std::size_t shape::type_size() const
         this->visit_type([&](auto as) { n = as.size(); });
     return n;
 }
+
 std::size_t shape::index(std::initializer_list<std::size_t> l) const
 {
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: index() called on dynamic shape");
+    }
     assert(l.size() <= this->lens().size());
     assert(this->lens().size() == this->strides().size());
     return std::inner_product(l.begin(), l.end(), this->strides().begin(), std::size_t{0});
 }
+
 std::size_t shape::index(const std::vector<std::size_t>& l) const
 {
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: index() called on dynamic shape");
+    }
     assert(l.size() <= this->lens().size());
     assert(this->lens().size() == this->strides().size());
     return std::inner_product(l.begin(), l.end(), this->strides().begin(), std::size_t{0});
 }
+
 std::size_t shape::index(std::size_t i) const
 {
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: index() called on dynamic shape");
+    }
     assert(this->lens().size() == this->strides().size());
     if(this->standard())
         return i;
@@ -267,12 +346,20 @@ void shape::multi_copy(std::size_t i, std::size_t* start, const std::size_t* end
 
 bool shape::packed() const
 {
+    if(this->dynamic())
+    {
+        return false;
+    }
     return this->sub_shapes().empty() and not impl->skips() and
            this->elements() == this->element_space();
 }
 
 bool shape::transposed() const
 {
+    if(this->dynamic())
+    {
+        return false;
+    }
     if(this->broadcasted())
     {
         // TODO: Use a filter_iterator instead
@@ -292,6 +379,10 @@ bool shape::transposed() const
 
 bool shape::broadcasted() const
 {
+    if(this->dynamic())
+    {
+        return false;
+    }
     assert(this->lens().size() == this->strides().size());
     return std::any_of(
         this->strides().begin(), this->strides().end(), [](auto x) { return x == 0; });
@@ -299,6 +390,10 @@ bool shape::broadcasted() const
 
 bool shape::scalar() const
 {
+    if(this->dynamic())
+    {
+        return false;
+    }
     assert(this->lens().size() == this->strides().size());
     // if any stride > 0, then accumulate will return false
     return this->sub_shapes().empty() and
@@ -317,6 +412,10 @@ shape shape::normalize_standard() const
 
 shape shape::with_lens(type_t t, const std::vector<std::size_t>& l) const
 {
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: with_lens() called on dynamic shape");
+    }
     assert(l.size() == this->lens().size());
     auto perm = find_permutation(*this);
     return shape::from_permutation(t, l, perm);
@@ -324,6 +423,10 @@ shape shape::with_lens(type_t t, const std::vector<std::size_t>& l) const
 
 shape shape::with_lens(const std::vector<std::size_t>& l) const
 {
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: with_lens() called on dynamic shape");
+    }
     return this->with_lens(this->type(), l);
 }
 
@@ -338,20 +441,80 @@ std::size_t shape::element_space() const { return impl->element_space(); }
 
 std::string shape::type_string() const { return name(this->type()); }
 
+bool shape::dynamic() const { return not impl->m_dyn_dims.empty(); }
+
+const std::vector<shape::dynamic_dimension>& shape::dyn_dims() const { return impl->m_dyn_dims; }
+
+std::vector<std::size_t> shape::min_lens() const
+{
+    return this->dynamic() ? impl->min_lens() : this->lens();
+}
+
+std::vector<std::size_t> shape::max_lens() const
+{
+    return this->dynamic() ? impl->max_lens() : this->lens();
+}
+
+std::vector<std::size_t> shape::opt_lens() const
+{
+    return this->dynamic() ? impl->opt_lens() : this->lens();
+}
+
+bool shape::dynamic_dimension::is_fixed() const { return this->min == this->max; }
+
+bool shape::dynamic_dimension::has_optimal() const { return opt != 0; }
+
+template <class Self, class F>
+auto shape::dynamic_dimension::reflect(Self& self, F f)
+{
+    return pack(f(self.min, "min"), f(self.max, "max"), f(self.opt, "opt"));
+}
+
+bool operator==(const shape::dynamic_dimension& x, const shape::dynamic_dimension& y)
+{
+    return (x.min == y.min and x.max == y.max and x.opt == y.opt);
+}
+
+bool operator!=(const shape::dynamic_dimension& x, const shape::dynamic_dimension& y)
+{
+    return !(x == y);
+}
+std::ostream& operator<<(std::ostream& os, const shape::dynamic_dimension& x)
+{
+    os << "[" << x.min << ", " << x.max << ", " << x.opt << "]";
+    return os;
+}
+
 bool operator==(const shape& x, const shape& y)
 {
-    return x.impl == y.impl or (x.type() == y.type() and x.lens() == y.lens() and
-                                x.strides() == y.strides() and x.sub_shapes() == y.sub_shapes());
+    if(x.dynamic() and y.dynamic())
+    {
+        return x.impl == y.impl or (x.type() == y.type() and x.dyn_dims() == y.dyn_dims() and
+                                    x.sub_shapes() == y.sub_shapes());
+    }
+    return x.impl == y.impl or
+           (x.dynamic() == y.dynamic() and x.type() == y.type() and x.lens() == y.lens() and
+            x.strides() == y.strides() and x.sub_shapes() == y.sub_shapes());
 }
+
 bool operator!=(const shape& x, const shape& y) { return !(x == y); }
 
 std::ostream& operator<<(std::ostream& os, const shape& x)
 {
     if(x.sub_shapes().empty())
     {
-        os << x.type_string() << ", ";
-        os << "{" << to_string_range(x.lens()) << "}, ";
-        os << "{" << to_string_range(x.strides()) << "}";
+        if(x.dynamic())
+        {
+            os << "dynamic, ";
+            os << x.type_string() << ", ";
+            os << "{" << to_string_range(x.dyn_dims()) << "}";
+        }
+        else
+        {
+            os << x.type_string() << ", ";
+            os << "{" << to_string_range(x.lens()) << "}, ";
+            os << "{" << to_string_range(x.strides()) << "}";
+        }
     }
     else
     {
@@ -375,12 +538,14 @@ const std::vector<shape>& shape::sub_shapes() const { return impl->m_shapes; }
 void migraphx_to_value(value& v, const shape& s)
 {
     value result;
-    result["type"]       = migraphx::to_value(s.type_string());
-    result["lens"]       = migraphx::to_value(s.lens());
-    result["strides"]    = migraphx::to_value(s.strides());
-    result["sub_shapes"] = migraphx::to_value(s.sub_shapes());
-    v                    = result;
+    result["type"]               = migraphx::to_value(s.type_string());
+    result["lens"]               = migraphx::to_value(s.lens());
+    result["strides"]            = migraphx::to_value(s.strides());
+    result["sub_shapes"]         = migraphx::to_value(s.sub_shapes());
+    result["dynamic_dimensions"] = migraphx::to_value(s.dyn_dims());
+    v                            = result;
 }
+
 void migraphx_from_value(const value& v, shape& s)
 {
     auto t = v.at("type").get_string();
@@ -390,9 +555,25 @@ void migraphx_from_value(const value& v, shape& s)
     }
     else
     {
-        s = shape{shape::parse_type(t),
-                  v.at("lens").to_vector<std::size_t>(),
-                  v.at("strides").to_vector<std::size_t>()};
+        if(v.at("dynamic_dimensions").empty())
+        {
+            s = shape{shape::parse_type(t),
+                      v.at("lens").to_vector<std::size_t>(),
+                      v.at("strides").to_vector<std::size_t>()};
+        }
+        else
+        {
+            auto v_dd = v.at("dynamic_dimensions");
+            std::vector<shape::dynamic_dimension> dyn_dims(v.at("dynamic_dimensions").size());
+            std::transform(v_dd.begin(), v_dd.end(), dyn_dims.begin(), [](migraphx::value x) {
+                auto x_min = x.at("min").template to<size_t>();
+                auto x_max = x.at("max").template to<size_t>();
+                auto x_opt = x.at("opt").template to<size_t>();
+                return shape::dynamic_dimension{x_min, x_max, x_opt};
+            });
+
+            s = shape{shape::parse_type(t), dyn_dims};
+        }
     }
 }
 

src/simplify_reshapes.cpp

@@ -272,7 +272,7 @@ struct find_concat_transpose
 {
     auto matcher() const
     {
-        return match::name("concat")(match::all_of[match::inputs()](match::transpose_shape()));
+        return match::name("concat")(match::all_of[match::inputs()](match::name("transpose")));
     }
 
     void apply(module& m, const match::matcher_result& mr) const
@@ -601,6 +601,69 @@ struct find_transpose_contiguous_reshaper_unary
     }
 };
 
+struct find_slice_transpose
+{
+    auto matcher() const
+    {
+        return match::any(match::any_of[match::outputs()](
+            match::name("slice")(match::output(match::name("transpose")))));
+    }
+
+    static std::vector<int64_t> find_common_perm(const std::vector<instruction_ref>& transposes)
+    {
+        std::map<std::vector<int64_t>, int64_t> count;
+        for(auto t : transposes)
+        {
+            auto perm = t->get_operator().to_value()["permutation"].to_vector<int64_t>();
+            count[perm]++;
+        }
+        return std::max_element(
+                   count.begin(), count.end(), by(std::less<>{}, [](auto&& p) { return p.second; }))
+            ->first;
+    }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins = r.result;
+        std::vector<instruction_ref> splits;
+        std::copy_if(ins->outputs().begin(),
+                     ins->outputs().end(),
+                     std::back_inserter(splits),
+                     [&](instruction_ref out) {
+                         return out->name() == "slice" and out->outputs().size() == 1 and
+                                out->outputs().front()->name() == "transpose";
+                     });
+        if(splits.size() < 2)
+            return;
+        std::vector<instruction_ref> transposes;
+        std::transform(splits.begin(),
+                       splits.end(),
+                       std::back_inserter(transposes),
+                       [](auto split) { return split->outputs().front(); });
+        auto perm  = find_common_perm(transposes);
+        auto iperm = invert_permutation(perm);
+        auto pre   = m.insert_instruction(
+            std::next(ins), make_op("transpose", {{"permutation", perm}}), ins);
+        for(auto i : range(transposes.size()))
+        {
+            auto split = splits[i];
+            auto t     = transposes[i];
+            auto op    = any_cast<op::slice>(split->get_operator());
+            std::transform(op.axes.begin(), op.axes.end(), op.axes.begin(), [&](auto axis) {
+                return iperm[axis];
+            });
+            auto new_ins = m.insert_instruction(t, op, pre);
+            if(t->get_operator() != pre->get_operator())
+            {
+                auto curr = t->get_operator().to_value()["permutation"].to_vector<int64_t>();
+                new_ins   = m.insert_instruction(
+                    t, make_op("transpose", {{"permutation", reorder_dims(iperm, curr)}}), new_ins);
+            }
+            m.replace_instruction(t, new_ins);
+        }
+    }
+};
+
 void simplify_reshapes::apply(module& m) const
 {
     for(int i = 0; i < 2; i++)
@@ -616,6 +679,7 @@ void simplify_reshapes::apply(module& m) const
                             find_nested_convert{},
                             find_nested_slice{},
                             find_nested_concat{},
+                            find_slice_transpose{},
                             find_transpose_contiguous_reshaper_unary{});
         dead_code_elimination{}.apply(m);
     }

src/target_assignments.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/target_assignments.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+void target_assignments::add_assignment(instruction_ref ins, const std::string& target)
+{
+    assignments.emplace(ins, target);
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/cpu/write_literals.cpp

@@ -25,6 +25,7 @@
 #include <migraphx/module.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/iterator_for.hpp>
+#include <migraphx/register_op.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -52,6 +53,7 @@ struct cpu_literal
         return os;
     }
 };
+MIGRAPHX_REGISTER_OP(cpu_literal);
 
 void write_literals::apply(module& m) const
 {

src/targets/fpga/CMakeLists.txt

+#####################################################################################
+# 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.
+#####################################################################################
+
+add_library(migraphx_fpga
+    target.cpp
+    lowering.cpp
+    subgraph.cpp
+    vitis_ai_adapter.cpp
+)
+
+set_target_properties(migraphx_fpga PROPERTIES EXPORT_NAME fpga)
+rocm_set_soversion(migraphx_fpga ${MIGRAPHX_SO_VERSION})
+
+rocm_clang_tidy_check(migraphx_fpga)
+target_link_libraries(migraphx_fpga migraphx)
+
+rocm_install_targets(
+    TARGETS migraphx_fpga
+    INCLUDE
+    ${CMAKE_CURRENT_SOURCE_DIR}/include
+)

src/targets/fpga/include/migraphx/fpga/context.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_FPGA_CONTEXT_HPP
+#define MIGRAPHX_GUARD_FPGA_CONTEXT_HPP
+
+#include <migraphx/config.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace fpga {
+
+struct context
+{
+    int id = 0;
+
+    void finish() const {}
+};
+
+} // namespace fpga
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif // MIGRAPHX_GUARD_FPGA_CONTEXT_HPP

src/targets/fpga/include/migraphx/fpga/lowering.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_FPGA_LOWERING_HPP
+#define MIGRAPHX_GUARD_FPGA_LOWERING_HPP
+
+#include <migraphx/program.hpp>
+#include <migraphx/config.hpp>
+#include <migraphx/fpga/context.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace fpga {
+
+struct lowering
+{
+    context* ctx = nullptr;
+    std::string name() const { return "fpga::lowering"; }
+    void apply(module& m) const;
+};
+
+} // namespace fpga
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif // MIGRAPHX_GUARD_FPGA_LOWERING_HPP