fix merge conflict

CMakeLists.txt

@@ -4,6 +4,12 @@ if("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
     message(FATAL_ERROR "The binary and source directroy cannot be the same")
 endif()
 
+# This has to be initialized before the project() command appears
+# Set the default of CMAKE_BUILD_TYPE to be release, unless user specifies with -D.  MSVC_IDE does not use CMAKE_BUILD_TYPE
+if( NOT MSVC_IDE AND NOT CMAKE_BUILD_TYPE )
+    set( CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build, options are: None Debug Release RelWithDebInfo MinSizeRel." )
+endif()
+
 project(migraphlib)
 find_package(ROCM REQUIRED)
 

src/CMakeLists.txt

 
 add_library(migraph 
     auto_contiguous.cpp
+    common_subexpression_elimination.cpp
     constant_propagate.cpp
     dead_code_elimination.cpp
     eliminate_allocation.cpp

src/common_subexpression_elimination.cpp

+#include <migraph/common_subexpression_elimination.hpp>
+#include <migraph/program.hpp>
+#include <migraph/instruction.hpp>
+#include <migraph/iterator_for.hpp>
+#include <migraph/ranges.hpp>
+#include <migraph/functional.hpp>
+
+#include <unordered_set>
+
+namespace migraph {
+
+template <class Range>
+void cse_range(program& p, Range&& r)
+{
+    std::unordered_multimap<std::string, instruction_ref> instructions;
+    for(auto ins : r)
+    {
+        // Skip dead instructions
+        if(ins->outputs().empty())
+            continue;
+        // Find instruction with the same name
+        auto found_instructions = range(instructions.equal_range(ins->name()));
+        for(const auto& pp : found_instructions)
+        {
+            auto eq = pp.second;
+            if(*eq != *ins)
+                continue;
+            p.replace_instruction(ins, eq);
+            cse_range(p, eq->outputs());
+        }
+        instructions.emplace(ins->name(), ins);
+    }
+}
+
+void common_subexpression_elimination::apply(program& p) const { cse_range(p, iterator_for(p)); }
+
+} // namespace migraph

src/include/migraph/builtin.hpp

@@ -4,6 +4,7 @@
 #include <migraph/context.hpp>
 #include <migraph/errors.hpp>
 #include <migraph/argument.hpp>
+#include <migraph/reflect.hpp>
 
 namespace migraph {
 
@@ -22,6 +23,13 @@ struct literal
 struct outline
 {
     shape s;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.s, "shape"));
+    }
+
     std::string name() const { return "@outline"; }
     shape compute_shape(const std::vector<shape>&) const { return s; }
     argument compute(context&, const shape&, const std::vector<argument>&) const
@@ -33,6 +41,13 @@ struct outline
 struct param
 {
     std::string parameter;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.parameter, "parameter"));
+    }
+
     std::string name() const { return "@param"; }
     shape compute_shape(const std::vector<shape>&) const { MIGRAPH_THROW("builtin"); }
     argument compute(context&, const shape&, const std::vector<argument>&) const

src/include/migraph/common_subexpression_elimination.hpp

+#ifndef MIGRAPH_GUARD_RTGLIB_COMMON_SUBEXPRESSION_ELIMINATION_HPP
+#define MIGRAPH_GUARD_RTGLIB_COMMON_SUBEXPRESSION_ELIMINATION_HPP
+
+#include <string>
+#include <migraph/instruction_ref.hpp>
+
+namespace migraph {
+
+struct program;
+
+struct common_subexpression_elimination
+{
+    std::string name() const { return "common_subexpression_elimination"; }
+    void apply(program& p) const;
+};
+
+} // namespace migraph
+
+#endif

src/include/migraph/instruction.hpp

@@ -5,6 +5,7 @@
 #include <migraph/shape.hpp>
 #include <migraph/instruction_ref.hpp>
 #include <migraph/operation.hpp>
+#include <migraph/erase.hpp>
 #include <string>
 #include <utility>
 
@@ -43,6 +44,10 @@ struct instruction
 
     const std::vector<instruction_ref>& outputs() const;
 
+    friend bool operator==(const instruction& x, const instruction& y);
+
+    friend bool operator!=(const instruction& x, const instruction& y);
+
     friend bool operator==(instruction_ref ref, const instruction& i);
 
     friend bool operator!=(const instruction& i, instruction_ref ref);
@@ -52,7 +57,10 @@ struct instruction
     void add_output(instruction_ref ins);
 
     template <class T>
-    void remove_output(const T& ins);
+    void remove_output(const T& ins)
+    {
+        migraph::erase(output, ins);
+    }
 
     static void backreference(instruction_ref ref);
 

src/include/migraph/operators.hpp

@@ -314,6 +314,57 @@ struct contiguous
     }
 };
 
+struct concat
+{
+    std::size_t axis = 0;
+    std::string name() const { return "concat"; }
+    std::vector<std::size_t> compute_offsets(const shape& output_shape,
+                                             const std::vector<argument> args) const
+    {
+        std::vector<std::size_t> offsets;
+        std::vector<std::size_t> offset(args[0].get_shape().lens().size(), 0);
+        offset[axis] = 0;
+        for(const auto& arg : args)
+        {
+            offsets.push_back(output_shape.index(offset));
+            offset[axis] += arg.get_shape().lens()[axis];
+        }
+        return offsets;
+    }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        if(inputs.empty())
+        {
+            MIGRAPH_THROW("Number of input tensors should exceed 0");
+        }
+
+        const auto& first_shape_lens = inputs.front().lens();
+        const auto& type             = inputs.front().type();
+        for(std::size_t l = 0; l < first_shape_lens.size(); l++)
+        {
+            if(l != axis)
+            {
+                if(!std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
+                       return s.lens()[l] == first_shape_lens[l];
+                   }))
+                {
+                    MIGRAPH_THROW("Non-axis dimensions should match");
+                }
+            }
+        }
+        std::size_t new_dim_axis = 0;
+        for(const auto& input : inputs)
+        {
+            const auto& lens = input.lens();
+            new_dim_axis += lens[axis];
+        }
+        std::vector<std::size_t> new_lens;
+        std::copy(first_shape_lens.begin(), first_shape_lens.end(), std::back_inserter(new_lens));
+        new_lens[axis] = new_dim_axis;
+        return {type, new_lens};
+    }
+};
+
 struct slice
 {
     std::vector<int64_t> axes;
@@ -531,7 +582,7 @@ struct reshape
     }
 };
 
-struct gemm
+struct dot
 {
     float alpha = 1.0;
     float beta  = 0.0;
@@ -542,7 +593,7 @@ struct gemm
         return pack(f(self.alpha, "alpha"), f(self.beta, "beta"));
     }
 
-    std::string name() const { return "gemm"; }
+    std::string name() const { return "dot"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
         check_shapes{inputs, *this}.has(2).same_type();

src/include/migraph/program.hpp

@@ -95,6 +95,10 @@ struct program
 
     void perf_report(std::ostream& os, std::size_t n, parameter_map params) const;
 
+    void debug_print();
+    void debug_print(instruction_ref ins);
+    void debug_print(const std::vector<instruction_ref>& inss);
+
     friend std::ostream& operator<<(std::ostream& os, const program& p);
     friend bool operator==(const program& x, const program& y);
     friend bool operator!=(const program& x, const program& y) { return !(x == y); }

src/include/migraph/ranges.hpp

@@ -92,6 +92,12 @@ iterator_range<Iterator> range(Iterator start, Iterator last)
     return {start, last};
 }
 
+template <class Iterator>
+iterator_range<Iterator> range(std::pair<Iterator, Iterator> p)
+{
+    return {p.first, p.second};
+}
+
 } // namespace migraph
 
 #endif

src/instruction.cpp

@@ -94,6 +94,17 @@ const std::vector<instruction_ref>& instruction::inputs() const { return argumen
 
 const std::vector<instruction_ref>& instruction::outputs() const { return output; }
 
+bool operator==(const instruction& x, const instruction& y)
+{
+    if(not(x.result == y.result and x.op == y.op and x.arguments == y.arguments))
+        return false;
+    if(x.name() == "@literal")
+        return x.lit == y.lit;
+    return true;
+}
+
+bool operator!=(const instruction& x, const instruction& y) { return !(x == y); }
+
 bool operator==(instruction_ref ref, const instruction& i) { return i == ref; }
 
 bool operator!=(const instruction& i, instruction_ref ref) { return !(i == ref); }
@@ -106,12 +117,6 @@ void instruction::add_output(instruction_ref ins)
         output.push_back(ins);
 }
 
-template <class T>
-void instruction::remove_output(const T& ins)
-{
-    migraph::erase(output, ins);
-}
-
 void instruction::backreference(instruction_ref ref)
 {
     for(auto&& arg : ref->inputs())
@@ -151,6 +156,7 @@ void instruction::replace(std::vector<instruction_ref> args)
 
 void instruction::replace_argument(instruction_ref old, instruction_ref new_ins)
 {
+    assert(std::any_of(arguments.begin(), arguments.end(), [&](auto i) { return i == old; }));
     std::replace(arguments.begin(), arguments.end(), old, new_ins);
     old->remove_output(*this);
 }

src/onnx/onnx.cpp

@@ -50,7 +50,7 @@ struct onnx_parser
     {
         add_generic_op("Add", op::add{});
         add_generic_op("Div", op::div{});
-        add_generic_op("MatMul", op::gemm{});
+        add_generic_op("MatMul", op::dot{});
         add_generic_op("Mul", op::mul{});
         add_generic_op("Relu", op::activation{"relu"});
         add_generic_op("Sub", op::sub{});
@@ -67,6 +67,10 @@ struct onnx_parser
         add_mem_op("Gemm", &onnx_parser::parse_gemm);
         add_mem_op("BatchNormalization", &onnx_parser::parse_batchnorm);
         add_mem_op("Softmax", &onnx_parser::parse_softmax);
+        add_mem_op("Squeeze", &onnx_parser::parse_squeeze);
+        add_mem_op("Unsqueeze", &onnx_parser::parse_unsqueeze);
+        add_mem_op("Slice", &onnx_parser::parse_slice);
+        add_mem_op("Concat", &onnx_parser::parse_concat);
     }
 
     template <class F>
@@ -188,6 +192,52 @@ struct onnx_parser
         return prog.add_instruction(op::flatten{axis}, args[0]);
     }
 
+    instruction_ref
+    parse_squeeze(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        op::squeeze op;
+        literal s = parse_value(attributes.at("axes"));
+        s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
+        return prog.add_instruction(op, args[0]);
+    }
+
+    instruction_ref
+    parse_unsqueeze(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        op::unsqueeze op;
+        literal s = parse_value(attributes.at("axes"));
+        s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
+        return prog.add_instruction(op, args[0]);
+    }
+
+    instruction_ref
+    parse_concat(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        std::size_t axis = parse_value(attributes.at("axis")).at<int>();
+        op::concat op{axis};
+        return prog.add_instruction(op, std::move(args));
+    }
+
+    instruction_ref
+    parse_slice(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        op::slice op;
+        if(contains(attributes, "axes"))
+        {
+            literal s = parse_value(attributes.at("axes"));
+            s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
+        }
+        {
+            literal s = parse_value(attributes.at("ends"));
+            s.visit([&](auto v) { copy(v, std::back_inserter(op.ends)); });
+        }
+        {
+            literal s = parse_value(attributes.at("starts"));
+            s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); });
+        }
+        return prog.add_instruction(op, args[0]);
+    }
+
     instruction_ref parse_constant(const std::string&,
                                    attribute_map attributes,
                                    const std::vector<instruction_ref>&)
@@ -225,11 +275,11 @@ struct onnx_parser
         if(args.size() == 3)
         {
             uint64_t axis = 1;
-            auto l3       = prog.add_instruction(op::gemm{alpha, beta}, l1, l2);
+            auto l3       = prog.add_instruction(op::dot{alpha, beta}, l1, l2);
             auto l4       = prog.add_instruction(op::broadcast{axis, l3->get_shape()}, args[2]);
             return prog.add_instruction(op::add{}, l3, l4);
         }
-        return prog.add_instruction(op::gemm{alpha, beta}, l1, l2);
+        return prog.add_instruction(op::dot{alpha, beta}, l1, l2);
     }
 
     instruction_ref

src/program.cpp

@@ -23,6 +23,36 @@ struct program_impl
 
 const operation& get_operation(instruction_ref ins) { return ins->get_operator(); }
 
+static void print_instruction(std::ostream& os,
+                              instruction_ref ins,
+                              const std::unordered_map<instruction_ref, std::string>& names)
+{
+    os << names.at(ins) << " = ";
+
+    os << ins->get_operator();
+
+    if(ins->name() == "@literal")
+    {
+        if(ins->get_literal().get_shape().elements() > 10)
+            os << "{ ... }";
+        else
+            os << "{" << ins->get_literal() << "}";
+    }
+
+    if(!ins->inputs().empty())
+    {
+        char delim = '(';
+        for(auto&& arg : ins->inputs())
+        {
+            os << delim << names.at(arg);
+            delim = ',';
+        }
+        os << ")";
+    }
+
+    os << " -> " << ins->get_shape();
+}
+
 template <class F>
 static void print_program(std::ostream& os, const program& p, F annonate)
 {
@@ -36,38 +66,21 @@ static void print_program(std::ostream& os, const program& p, F annonate)
         {
             var_name = any_cast<builtin::param>(ins->get_operator()).parameter;
         }
+        names.emplace(ins, var_name);
 
-        os << var_name << " = ";
-
-        os << ins->get_operator();
-
-        if(ins->name() == "@literal")
-        {
-            if(ins->get_literal().get_shape().elements() > 10)
-                os << "{ ... }";
-            else
-                os << "{" << ins->get_literal() << "}";
-        }
-
-        if(!ins->inputs().empty())
+        // TODO: Use all_of
+        for(auto&& arg : ins->inputs())
         {
-            char delim = '(';
-            for(auto&& arg : ins->inputs())
-            {
-                assert(p.has_instruction(arg) && "Instruction not found");
-                os << delim << names.at(arg);
-                delim = ',';
-            }
-            os << ")";
+            assert(p.has_instruction(arg) && "Instruction not found");
+            (void)arg;
         }
 
-        os << " -> " << ins->get_shape();
+        print_instruction(os, ins, names);
 
         annonate(ins, names);
 
         os << std::endl;
 
-        names.emplace(ins, var_name);
         count++;
     }
 }
@@ -124,7 +137,9 @@ instruction_ref program::replace_instruction(instruction_ref ins, instruction_re
     {
         return rep;
     }
-    for(auto&& out : ins->outputs())
+    // Make a copy of outputs which can be changed when calling replace_argument
+    auto outputs = ins->outputs();
+    for(auto out : outputs)
     {
         // TODO: Check for possible cycles
         if(out != rep)
@@ -135,6 +150,10 @@ instruction_ref program::replace_instruction(instruction_ref ins, instruction_re
     }
     // Replacement should not be dead code unless its the last instruction
     assert(!rep->outputs().empty() or rep == std::prev(end()));
+    // Output of the original instruction should only be the replacement or empty
+    assert(ins->outputs().empty() or std::all_of(ins->outputs().begin(),
+                                                 ins->outputs().end(),
+                                                 [&](auto i) { return i == rep; }));
     assert(ins->valid(begin()));
     assert(rep->valid(begin()));
     return rep;
@@ -449,6 +468,25 @@ void program::perf_report(std::ostream& os, std::size_t n, parameter_map params)
        << ", " << std::round(calculate_overhead_percent) << "%" << std::endl;
 }
 
+void program::debug_print() { std::cout << *this << std::endl; }
+void program::debug_print(instruction_ref ins)
+{
+    std::stringstream ss;
+    print_program(ss, *this, [&](auto x, auto&& names) {
+        if(x == ins)
+        {
+            print_instruction(std::cout, x, names);
+            std::cout << std::endl;
+        }
+    });
+}
+void program::debug_print(const std::vector<instruction_ref>& inss)
+{
+    for(auto ins : inss)
+        debug_print(ins);
+    std::cout << std::endl;
+}
+
 bool operator==(const program& x, const program& y) { return to_string(x) == to_string(y); }
 
 std::ostream& operator<<(std::ostream& os, const program& p)

src/targets/cpu/cpu_lowering.cpp

@@ -282,10 +282,38 @@ struct cpu_contiguous
     }
 };
 
+struct cpu_concat
+{
+    op::concat op;
+    std::string name() const { return "cpu::concat"; }
+    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
+    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+    {
+        argument result{output_shape};
+        std::vector<std::size_t> coffsets = op.compute_offsets(output_shape, args);
+        for(std::size_t l = 0; l < args.size(); l++)
+        {
+            auto argl             = args[l];
+            std::size_t nelements = argl.get_shape().elements();
+            visit_all(result, argl)([&](auto output, auto input) {
+                auto slice_shape =
+                    shape{output_shape.type(), input.get_shape().lens(), output_shape.strides()};
+                auto slice = make_view(slice_shape, output.data() + coffsets[l]);
+                // cppcheck-suppress useStlAlgorithm
+                for(std::size_t i = 0; i < nelements; i++)
+                {
+                    slice[i] = input[i];
+                }
+            });
+        }
+        return result;
+    }
+};
+
 struct cpu_gemm
 {
-    op::gemm op;
-    std::string name() const { return "cpu::gemm"; }
+    op::dot op;
+    std::string name() const { return "cpu::dot"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
 
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
@@ -564,25 +592,24 @@ struct cpu_apply
     {
         apply_map["im2col"]      = extend_op<cpu_im2col, op::im2col>();
         apply_map["convolution"] = extend_op<cpu_convolution, op::convolution>();
-        apply_map["gemm"]        = extend_op<cpu_gemm, op::gemm>();
+        apply_map["dot"]         = extend_op<cpu_gemm, op::dot>();
         apply_map["batch_norm_inference"] =
             extend_op<cpu_batch_norm_inference, op::batch_norm_inference>();
         apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
+        apply_map["concat"]     = extend_op<cpu_concat, op::concat>();
         apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();
-
-        apply_map["identity"] = simple_op<cpu_unary<identity_op>>();
-        apply_map["tanh"]     = simple_op<cpu_unary<tanh_op>>();
-        apply_map["sigmoid"]  = simple_op<cpu_unary<sigmoid_op>>();
-        apply_map["exp"]      = simple_op<cpu_unary<exp_op>>();
-        apply_map["neg"]      = simple_op<cpu_unary<neg_op>>();
-        apply_map["sin"]      = simple_op<cpu_unary<sin_op>>();
-        apply_map["cos"]      = simple_op<cpu_unary<cos_op>>();
-        apply_map["tan"]      = simple_op<cpu_unary<tan_op>>();
-        apply_map["add"]      = simple_op<cpu_binary<add_op>>();
-        apply_map["sub"]      = simple_op<cpu_binary<sub_op>>();
-        apply_map["mul"]      = simple_op<cpu_binary<mul_op>>();
-        // apply_map["scalar"]   = simple_op<cpu_binary<mul_op>>();
-        apply_map["div"]      = simple_op<cpu_binary<div_op>>();
+        apply_map["identity"]   = simple_op<cpu_unary<identity_op>>();
+        apply_map["tanh"]       = simple_op<cpu_unary<tanh_op>>();
+        apply_map["sigmoid"]    = simple_op<cpu_unary<sigmoid_op>>();
+        apply_map["exp"]        = simple_op<cpu_unary<exp_op>>();
+        apply_map["neg"]        = simple_op<cpu_unary<neg_op>>();
+        apply_map["sin"]        = simple_op<cpu_unary<sin_op>>();
+        apply_map["cos"]        = simple_op<cpu_unary<cos_op>>();
+        apply_map["tan"]        = simple_op<cpu_unary<tan_op>>();
+        apply_map["add"]        = simple_op<cpu_binary<add_op>>();
+        apply_map["sub"]        = simple_op<cpu_binary<sub_op>>();
+        apply_map["mul"]        = simple_op<cpu_binary<mul_op>>();
+        apply_map["div"]        = simple_op<cpu_binary<div_op>>();
 
         apply_map["softmax"] = simple_op<softmax2d>();
     }

src/targets/gpu/CMakeLists.txt

@@ -15,6 +15,7 @@ add_library(migraph_device
     device/add_relu.cpp
     device/contiguous.cpp
     device/mul.cpp
+    device/concat.cpp
 )
 rocm_clang_tidy_check(migraph_device)
 target_link_libraries(migraph_device migraph hip::device)
@@ -32,6 +33,7 @@ add_library(migraph_gpu
     convolution.cpp
     softmax.cpp
     contiguous.cpp
+    concat.cpp
     relu.cpp
     leaky_relu.cpp
     add.cpp

src/targets/gpu/add.cpp

@@ -14,9 +14,9 @@ shape hip_add::compute_shape(const std::vector<shape>& inputs) const
     return inputs.at(0);
 }
 
-argument hip_add::compute(context&, const shape&, const std::vector<argument>& args) const
+argument hip_add::compute(context& ctx, const shape&, const std::vector<argument>& args) const
 {
-    device::add(args[2], args[0], args[1]);
+    device::add(ctx.get_stream().get(), args[2], args[0], args[1]);
     return args[2];
 }
 
@@ -34,7 +34,7 @@ argument miopen_add::compute(context& ctx,
     auto a_desc = make_tensor(args[0].get_shape());
     auto b_desc = make_tensor(args[1].get_shape());
     auto c_desc = make_tensor(output_shape);
-    miopenOpTensor(ctx.handle.get(),
+    miopenOpTensor(ctx.get_stream().get_miopen(),
                    miopenTensorOpAdd,
                    &alpha,
                    a_desc.get(),

src/targets/gpu/batchnorm.cpp

@@ -23,7 +23,7 @@ argument miopen_batch_norm_inference::compute(context& ctx,
 
     float alpha = 1.0, beta = 0.0f;
 
-    miopenBatchNormalizationForwardInference(ctx.handle.get(),
+    miopenBatchNormalizationForwardInference(ctx.get_stream().get_miopen(),
                                              miopenBatchNormMode_t(op.bn_mode),
                                              &alpha,
                                              &beta,

src/targets/gpu/concat.cpp

+#include <migraph/gpu/concat.hpp>
+#include <migraph/operators.hpp>
+#include <migraph/manage_ptr.hpp>
+#include <migraph/gpu/miopen.hpp>
+#include <migraph/gpu/device/concat.hpp>
+#include <utility>
+
+namespace migraph {
+namespace gpu {
+
+shape hip_concat::compute_shape(std::vector<shape> inputs) const
+{
+    inputs.pop_back();
+    return op.compute_shape(inputs);
+}
+
+argument hip_concat::compute(context& ctx,
+                             const shape& output_shape,
+                             const std::vector<argument>& args) const
+{
+    std::vector<std::size_t> offsets = op.compute_offsets(output_shape, args);
+    return device::concat(ctx.get_stream().get(), output_shape, args, offsets);
+}
+
+} // namespace gpu
+
+} // namespace migraph

src/targets/gpu/contiguous.cpp

@@ -12,13 +12,14 @@ shape miopen_contiguous::compute_shape(const std::vector<shape>& inputs) const
     check_shapes{inputs, *this}.has(2);
     return op.compute_shape({inputs.at(0)});
 }
-argument
-miopen_contiguous::compute(context&, shape output_shape, const std::vector<argument>& args) const
+argument miopen_contiguous::compute(context& ctx,
+                                    shape output_shape,
+                                    const std::vector<argument>& args) const
 {
     assert(output_shape == args[1].get_shape());
     assert(output_shape.standard());
     (void)output_shape;
-    device::contiguous(args.at(1), args.at(0));
+    device::contiguous(ctx.get_stream().get(), args.at(1), args.at(0));
     return args.at(1);
 }
 

src/targets/gpu/convolution.cpp

@@ -21,7 +21,7 @@ argument miopen_convolution::compute(context& ctx,
     auto y_desc = make_tensor(output_shape);
 
     float alpha = 1, beta = 0;
-    miopenConvolutionForward(ctx.handle.get(),
+    miopenConvolutionForward(ctx.get_stream().get_miopen(),
                              &alpha,
                              x_desc.get(),
                              args[0].implicit(),
@@ -47,18 +47,22 @@ shape miopen_convolution::compile(context& ctx,
     auto y_desc = make_tensor(output_shape);
 
     std::size_t workspace_size = 0;
-    miopenConvolutionForwardGetWorkSpaceSize(
-        ctx.handle.get(), w_desc.get(), x_desc.get(), cd.get(), y_desc.get(), &workspace_size);
+    miopenConvolutionForwardGetWorkSpaceSize(ctx.get_stream().get_miopen(),
+                                             w_desc.get(),
+                                             x_desc.get(),
+                                             cd.get(),
+                                             y_desc.get(),
+                                             &workspace_size);
     workspace_shape = shape{shape::int8_type, {workspace_size}};
 
     auto x         = to_gpu(generate_argument(inputs[0]->get_shape()));
     auto w         = to_gpu(generate_argument(inputs[1]->get_shape()));
-    auto y         = to_gpu(generate_argument(output_shape));
+    auto y         = allocate_gpu(output_shape);
     auto workspace = allocate_gpu(workspace_shape);
 
     int algo_count = 1;
     miopenConvAlgoPerf_t perf;
-    miopenFindConvolutionForwardAlgorithm(ctx.handle.get(),
+    miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
                                           x_desc.get(),
                                           x.implicit(),
                                           w_desc.get(),

src/targets/gpu/device/add.cpp

@@ -5,14 +5,18 @@ namespace migraph {
 namespace gpu {
 namespace device {
 
-void add(const argument& result, const argument& arg1, const argument& arg2)
+void add(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
 {
-    nary(result, arg1, arg2)([](auto x, auto y) { return x + y; });
+    nary(stream, result, arg1, arg2)([](auto x, auto y) { return x + y; });
 }
 
-void add(const argument& result, const argument& arg1, const argument& arg2, const argument& arg3)
+void add(hipStream_t stream,
+         const argument& result,
+         const argument& arg1,
+         const argument& arg2,
+         const argument& arg3)
 {
-    nary(result, arg1, arg2, arg3)([](auto x, auto y, auto z) { return x + y + z; });
+    nary(stream, result, arg1, arg2, arg3)([](auto x, auto y, auto z) { return x + y + z; });
 }
 
 } // namespace device