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

src/CMakeLists.txt

@@ -14,6 +14,7 @@ add_library(migraphx
     eliminate_pad.cpp
     fwd_conv_batchnorm_rewrite.cpp
     rewrite_rnn.cpp
+    rewrite_pooling.cpp
     env.cpp
     generate.cpp
     instruction.cpp

src/include/migraphx/op/binary.hpp

@@ -30,23 +30,29 @@ struct binary : op_name<Derived>
         argument result{output_shape};
         auto s1 = args[0].get_shape();
         auto s2 = args[1].get_shape();
-        visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
-            if(s1 == s2 and input1.get_shape().packed() and input2.get_shape().packed())
-            {
+        if(s1 == s2 and s1.packed())
+        {
+            shape std_shape{s1.type(), s1.lens()};
+            argument std_result{std_shape, result.data()};
+            argument std_arg0{std_shape, args[0].data()};
+            argument std_arg1{std_shape, args[1].data()};
+            visit_all(std_result, std_arg0, std_arg1)([&](auto output, auto input1, auto input2) {
                 std::transform(input1.begin(),
                                input1.end(),
                                input2.begin(),
                                output.begin(),
                                static_cast<const Derived&>(*this).apply());
-            }
-            else
-            {
+            });
+        }
+        else
+        {
+            visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
                 shape_for_each(output.get_shape(), [&](const auto& idx) {
                     output(idx.begin(), idx.end()) = static_cast<const Derived&>(*this).apply()(
                         input1(idx.begin(), idx.end()), input2(idx.begin(), idx.end()));
                 });
-            }
-        });
+            });
+        }
 
         return result;
     }

src/include/migraphx/op/capture.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_CAPTURE_HPP
+#define MIGRAPHX_GUARD_OPERATORS_CAPTURE_HPP
+
+#include <array>
+#include <migraphx/operation.hpp>
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/stringutils.hpp>
+#include <migraphx/streamutils.hpp>
+#include <migraphx/literal.hpp>
+#include <migraphx/shape_for_each.hpp>
+#include <migraphx/config.hpp>
+#include <cmath>
+#include <utility>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct capture
+{
+    std::size_t ins_index;
+    std::function<void(std::size_t ins_index, std::vector<argument>)> f{};
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.ins_index, "ins_index"));
+    }
+
+    std::string name() const { return "capture"; }
+
+    shape compute_shape(std::vector<shape> inputs) const { return inputs.front(); }
+
+    argument compute(const shape&, std::vector<argument> args) const
+    {
+        if(f)
+        {
+            f(ins_index, args);
+        }
+        else
+        {
+            MIGRAPHX_THROW("CAPTURE: callback function is not callable!");
+        }
+
+        return args.front();
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/reshape.hpp

@@ -59,7 +59,9 @@ struct reshape
 
         shape s{inputs.front().type(), rdims};
         if(s.elements() != inputs.front().elements())
-            MIGRAPHX_THROW("Wrong number of elements for reshape");
+            MIGRAPHX_THROW("Wrong number of elements for reshape: reshape has " +
+                           std::to_string(s.elements()) + " elements whereas the input has " +
+                           std::to_string(inputs.front().elements()));
         return s;
     }
     argument compute(shape output_shape, std::vector<argument> args) const

src/include/migraphx/op/unary.hpp

@@ -27,26 +27,34 @@ struct unary : op_name<Derived>
     argument compute(const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
-        result.visit([&](auto output) {
-            args[0].visit([&](auto input) {
-                if(input.get_shape().packed())
-                {
+        auto in_shape = args[0].get_shape();
+        if(in_shape.packed())
+        {
+            shape std_in_shape{in_shape.type(), in_shape.lens()};
+            shape std_out_shape{output_shape.type(), output_shape.lens()};
+            argument arg_in{std_in_shape, args[0].data()};
+            argument arg_out{std_out_shape, result.data()};
+            arg_out.visit([&](auto output) {
+                arg_in.visit([&](auto input) {
                     std::transform(input.begin(),
                                    input.end(),
                                    output.begin(),
                                    static_cast<const Derived&>(*this).apply());
 
-                    return result;
-                }
-
-                shape_for_each(output.get_shape(), [&](const auto& idx) {
-                    output(idx.begin(), idx.end()) =
-                        static_cast<const Derived&>(*this).apply()(input(idx.begin(), idx.end()));
                 });
-
-                return result;
             });
-        });
+        }
+        else
+        {
+            result.visit([&](auto output) {
+                args[0].visit([&](auto input) {
+                    shape_for_each(output.get_shape(), [&](const auto& idx) {
+                        output(idx.begin(), idx.end()) = static_cast<const Derived&>(*this).apply()(
+                            input(idx.begin(), idx.end()));
+                    });
+                });
+            });
+        }
 
         return result;
     }

src/include/migraphx/operators.hpp

@@ -13,6 +13,7 @@
 #include <migraphx/op/batch_norm.hpp>
 #include <migraphx/op/binary.hpp>
 #include <migraphx/op/broadcast.hpp>
+#include <migraphx/op/capture.hpp>
 #include <migraphx/op/clip.hpp>
 #include <migraphx/op/common.hpp>
 #include <migraphx/op/concat.hpp>

src/include/migraphx/program.hpp

@@ -126,6 +126,9 @@ struct program
     friend bool operator==(const program& x, const program& y);
     friend bool operator!=(const program& x, const program& y) { return !(x == y); }
 
+    std::shared_ptr<std::vector<std::pair<float, float>>> int8_quant_params =
+        std::make_shared<std::vector<std::pair<float, float>>>();
+
     private:
     void assign(const program& p);
 

src/include/migraphx/quantization.hpp

@@ -15,6 +15,14 @@ struct program;
 void quantize(program& prog, const std::vector<std::string>& ins_names);
 void quantize(program& prog);
 
+// insert the capture operator for the inputs of each operator to be quantized
+// to int8
+void capture_arguments(program& prog,
+                       const std::vector<std::string>& ins_names,
+                       const std::function<void(std::size_t, std::vector<argument>)>& func);
+void capture_arguments(program& prog, const std::vector<std::string>& ins_names);
+void capture_arguments(program& prog);
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/rewrite_pooling.hpp

+#ifndef MIGRAPHX_GUARD_RTGLIB_REWRITE_POOLING_HPP
+#define MIGRAPHX_GUARD_RTGLIB_REWRITE_POOLING_HPP
+
+#include <string>
+#include <migraphx/config.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct program;
+
+/**
+ * Rewrite pooling to reduce_mean
+ */
+struct rewrite_pooling
+{
+    std::string name() const { return "rewrite_pooling"; }
+    void apply(program& prog) const;
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/tensor_view.hpp

@@ -132,7 +132,11 @@ struct tensor_view
             return m_data + this->size();
     }
 
-    std::vector<T> to_vector() const { return std::vector<T>(this->begin(), this->end()); }
+    template <class U = T>
+    std::vector<U> to_vector() const
+    {
+        return std::vector<U>(this->begin(), this->end());
+    }
 
     friend std::ostream& operator<<(std::ostream& os, const tensor_view<T>& x)
     {

src/program.cpp

@@ -112,7 +112,8 @@ void program::assign(const program& p)
     {
         impl->instructions.clear();
     }
-    impl->ctx = p.impl->ctx;
+    impl->ctx         = p.impl->ctx;
+    int8_quant_params = p.int8_quant_params;
 
     std::unordered_map<instruction_ref, instruction_ref> ins_map;
     for(auto ins : iterator_for(p))

src/py/migraphx_py.cpp

@@ -156,6 +156,7 @@ PYBIND11_MODULE(migraphx, m)
     py::class_<migraphx::target>(m, "target");
 
     py::class_<migraphx::program>(m, "program")
+        .def("clone", [](migraphx::program& p) { return *(new migraphx::program(p)); })
         .def("get_parameter_shapes", &migraphx::program::get_parameter_shapes)
         .def("get_shape", &migraphx::program::get_shape)
         .def("compile", [](migraphx::program& p, const migraphx::target& t) { p.compile(t); })
@@ -186,6 +187,11 @@ PYBIND11_MODULE(migraphx, m)
         migraphx::quantize(p, ins_names);
     });
     m.def("quantize", [](migraphx::program& p) { migraphx::quantize(p, {"all"}); });
+    m.def("capture_arguments", [](migraphx::program& p, const std::vector<std::string>& ins_names) {
+        migraphx::capture_arguments(p, ins_names);
+    });
+
+    m.def("capture_arguments", [](migraphx::program& p) { migraphx::capture_arguments(p); });
 
 #ifdef HAVE_GPU
     m.def("allocate_gpu", &migraphx::gpu::allocate_gpu, py::arg("s"), py::arg("host") = false);

src/quantization.cpp

@@ -3,32 +3,53 @@
 #include <migraphx/instruction.hpp>
 #include <migraphx/iterator_for.hpp>
 #include <migraphx/op/convert.hpp>
+#include <migraphx/op/dot.hpp>
+#include <migraphx/op/mul.hpp>
+#include <migraphx/op/add.hpp>
+#include <migraphx/op/quant_dot.hpp>
+#include <migraphx/op/capture.hpp>
+#include <migraphx/op/convolution.hpp>
+#include <migraphx/op/quant_convolution.hpp>
+#include <migraphx/op/multibroadcast.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/ranges.hpp>
 #include <utility>
+#include <iomanip>
+#include <fstream>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-instruction_ref insert_fp16(program& prog,
-                            instruction_ref& ins,
-                            shape::type_t type,
-                            std::unordered_map<instruction_ref, instruction_ref>& map_fp16)
+instruction_ref insert_quant_ins(program& prog,
+                                 instruction_ref& ins,
+                                 shape::type_t type,
+                                 std::unordered_map<instruction_ref, instruction_ref>& map_ins)
 {
-    if(map_fp16.count(ins) > 0)
+    if(map_ins.count(ins) > 0)
     {
-        return map_fp16[ins];
+        return map_ins[ins];
+    }
+
+    if(ins->name() == "undefined")
+    {
+        return ins;
     }
 
     assert(ins->get_shape().type() == shape::float_type ||
-           ins->get_shape().type() == shape::double_type);
-    instruction_ref ins_fp16{};
-    ins_fp16      = prog.insert_instruction(std::next(ins), op::convert{type}, ins);
-    map_fp16[ins] = ins_fp16;
+           ins->get_shape().type() == shape::double_type ||
+           ins->get_shape().type() == shape::int32_type);
+    instruction_ref quant_ins{};
+    quant_ins    = prog.insert_instruction(std::next(ins), op::convert{type}, ins);
+    map_ins[ins] = quant_ins;
 
-    return ins_fp16;
+    return quant_ins;
 }
 
+// This function is to convert any instructions specified in the input
+// from double or float to float16 by inserting a convert operator.
+// For the conversion, there could be cases of overflowing, but it
+// is very rare in the area of deeping learning, so we just do a
+// truncate of the input to get the fp16.
 void quantize(program& prog, const std::vector<std::string>& ins_names)
 {
     std::unordered_map<instruction_ref, instruction_ref> map_fp16;
@@ -60,7 +81,7 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
                 }
                 else
                 {
-                    input_fp16 = insert_fp16(prog, input, shape::half_type, map_fp16);
+                    input_fp16 = insert_quant_ins(prog, input, shape::half_type, map_fp16);
                 }
                 converted_inputs.push_back(input_fp16);
             }
@@ -80,21 +101,13 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
         auto ins_shape = compute_shape(op, converted_inputs);
         if(ins_shape.type() != orig_type)
         {
-            // insert another convert instruction to convert it back
-            if(ins == std::prev(prog.end()))
+            // check the dead code case to avoid assert
+            bool output_empty = ins->outputs().empty();
+            auto ins_orig_type =
+                prog.insert_instruction(std::next(ins), op::convert{orig_type}, ins);
+            if(!output_empty)
             {
-                prog.add_instruction(op::convert{orig_type}, ins);
-            }
-            else
-            {
-                // check the dead code case to avoid assert
-                bool output_empty = ins->outputs().empty();
-                auto ins_orig_type =
-                    prog.insert_instruction(std::next(ins), op::convert{orig_type}, ins);
-                if(!output_empty)
-                {
-                    prog.replace_instruction(ins, ins_orig_type);
-                }
+                prog.replace_instruction(ins, ins_orig_type);
             }
         }
 
@@ -104,5 +117,80 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
 
 void quantize(program& prog) { quantize(prog, {"all"}); }
 
+// For the input of each input argument, we need to insert a
+// capture operator to compute the scale and shift
+void capture_arguments(program& prog,
+                       const std::vector<std::string>& ins_names,
+                       const std::function<void(std::size_t, std::vector<argument>)>& func)
+{
+
+    size_t num_quant_params = 0;
+    // the int8 quantization only support dot and convolution
+    std::vector<std::string> op_names = {"dot", "convolution"};
+    if(!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) {
+           return std::find(op_names.begin(), op_names.end(), name) != op_names.end();
+       }))
+    {
+        MIGRAPHX_THROW("CAPTURE_ARGUMENTS: input operator is not supported");
+    }
+
+    std::unordered_map<instruction_ref, instruction_ref> ins_map;
+    for(auto ins : iterator_for(prog))
+    {
+        if(not contains(ins_names, ins->name()))
+        {
+            continue;
+        }
+
+        auto inputs = ins->inputs();
+        std::vector<instruction_ref> new_args;
+        for(auto input : inputs)
+        {
+            instruction_ref new_ins{};
+            if(ins_map.count(input) > 0)
+            {
+                new_ins = ins_map[input];
+            }
+            else
+            {
+                new_ins = prog.insert_instruction(
+                    std::next(input), op::capture{num_quant_params++, func}, input);
+                ins_map[input] = new_ins;
+            }
+            new_args.push_back(new_ins);
+        }
+        instruction::replace(ins, ins->get_operator(), ins->get_shape(), new_args);
+    }
+
+    // set one pair of parameter for each argument
+    prog.int8_quant_params->resize(num_quant_params, std::make_pair(-1.0f, -1.0f));
+}
+
+void capture_arguments(program& prog, const std::vector<std::string>& ins_names)
+{
+    auto calc_quant_params = [&](std::size_t ins_index, std::vector<migraphx::argument> args) {
+        std::pair<float, float> param_pair{1.0f, 0.0f};
+
+        // scale and shift is need for only int8 type, and we do not
+        // consider shift, so set shift to 0
+        std::vector<float> vec_val;
+        args.front().visit([&](auto output) { vec_val.assign(output.begin(), output.end()); });
+        auto max_val = *std::max_element(vec_val.begin(), vec_val.end());
+        auto min_val = *std::min_element(vec_val.begin(), vec_val.end());
+        auto max_abs = std::max(std::fabs(max_val), std::fabs(min_val));
+
+        param_pair.first                     = 127.0f / max_abs;
+        (*prog.int8_quant_params)[ins_index] = param_pair;
+    };
+
+    capture_arguments(prog, ins_names, calc_quant_params);
+}
+
+void capture_arguments(program& prog)
+{
+    std::vector<std::string> ins_names = {"dot", "convolution"};
+    capture_arguments(prog, ins_names);
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/rewrite_pooling.cpp

+#include <migraphx/rewrite_pooling.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/iterator_for.hpp>
+#include <migraphx/op/pooling.hpp>
+#include <migraphx/op/reshape.hpp>
+#include <migraphx/op/reduce_mean.hpp>
+#include <migraphx/program.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+void rewrite_pooling::apply(program& prog) const
+{
+    for(auto ins : iterator_for(prog))
+    {
+        if(ins->name() != "pooling")
+            continue;
+        if(ins->get_shape().lens().size() != 4)
+            continue;
+        if(ins->inputs().empty())
+            continue;
+        auto&& s  = ins->inputs().front()->get_shape();
+        auto&& op = any_cast<op::pooling>(ins->get_operator());
+        if(op.mode != "average")
+            continue;
+        if(op.padding[0] != 0 and op.padding[1] != 0)
+            continue;
+        if(op.stride[0] != 1 and op.stride[1] != 1)
+            continue;
+        if(s.lens()[2] != op.lengths[0] and s.lens()[3] != op.lengths[1])
+            continue;
+        std::int64_t n = s.lens()[0];
+        std::int64_t c = s.lens()[1];
+        auto reshape =
+            prog.insert_instruction(ins, op::reshape{{n * c, -1}}, ins->inputs().front());
+        auto pooling = prog.insert_instruction(ins, op::reduce_mean{{1}}, reshape);
+        prog.replace_instruction(ins, op::reshape{{n, c, 1, 1}}, pooling);
+    }
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/cpu/lowering.cpp

@@ -261,7 +261,8 @@ struct cpu_quant_convolution
                         const auto in_ch = group_id * wei_c + k;
                         if(in_x >= 0 && in_x < in_h && in_y >= 0 && in_y < in_w)
                         {
-                            acc += input(o, in_ch, in_x, in_y) * weights(w, k, x, y);
+                            acc += static_cast<int32_t>(input(o, in_ch, in_x, in_y)) *
+                                   weights(w, k, x, y);
                         }
                     });
                     output(o, w, i, j) = acc;
@@ -576,8 +577,7 @@ struct cpu_quant_gemm
         }
 
         // 2 input arguments
-        int32_t beta = 0;
-        migemm(result, arg_0, arg_1, op.alpha, beta);
+        migemm(result, arg_0, arg_1, op.alpha, int32_t{0});
 
         return result;
     }

src/targets/gpu/CMakeLists.txt

@@ -82,6 +82,7 @@ add_library(migraphx_gpu
     elu.cpp
     pad.cpp
     gather.cpp
+    convert.cpp
     lrn.cpp
     schedule_model.cpp
     adjust_allocation.cpp

src/targets/gpu/convert.cpp

+#include <migraphx/gpu/convert.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/device/convert.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape hip_convert::compute_shape(std::vector<shape> inputs) const
+{
+    inputs.pop_back();
+    check_shapes{inputs}.packed();
+    return op.compute_shape(inputs);
+}
+
+argument hip_convert::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+{
+    device::convert(ctx.get_stream().get(), args[1], args[0]);
+    return args[1];
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

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

@@ -16,6 +16,12 @@ struct hip_array
     MIGRAPHX_DEVICE_CONSTEXPR T& operator[](std::size_t i) { return d[i]; }
     MIGRAPHX_DEVICE_CONSTEXPR const T& operator[](std::size_t i) const { return d[i]; }
 
+    MIGRAPHX_DEVICE_CONSTEXPR T& front() { return d[0]; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T& front() const { return d[0]; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T& back() { return d[N - 1]; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T& back() const { return d[N - 1]; }
+
     MIGRAPHX_DEVICE_CONSTEXPR T* data() { return d; }
     MIGRAPHX_DEVICE_CONSTEXPR const T* data() const { return d; }
 

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

@@ -209,28 +209,15 @@ constexpr std::size_t compute_block_size(std::size_t n, std::size_t max_block_si
 }
 
 template <class Op, class T, class Input, class Output>
-void reduce(hipStream_t stream,
-            const argument& result,
-            const argument& arg,
-            Op op,
-            T init,
-            Input read_input,
-            Output read_output)
+void reduce_multi_impl(hipStream_t stream,
+                       const argument& result,
+                       const argument& arg,
+                       Op op,
+                       T init,
+                       Input read_input,
+                       Output read_output,
+                       const shape& reduce_slice)
 {
-    auto&& output_shape = result.get_shape();
-    auto&& input_shape  = arg.get_shape();
-    std::vector<std::size_t> reduce_lens;
-    std::transform(output_shape.lens().begin(),
-                   output_shape.lens().end(),
-                   input_shape.lens().begin(),
-                   std::back_inserter(reduce_lens),
-                   [](auto x, auto y) -> std::size_t {
-                       if(x == y)
-                           return 1;
-                       else
-                           return y;
-                   });
-    shape reduce_slice{output_shape.type(), reduce_lens};
     hip_visit_all(result, arg, reduce_slice)([&](auto output, auto input, auto reduce_shape) {
         auto nelements = result.get_shape().elements();
         auto relements = reduce_slice.elements();
@@ -250,6 +237,83 @@ void reduce(hipStream_t stream,
     });
 }
 
+template <class Op, class T, class Input, class Output>
+void reduce_standard_impl(hipStream_t stream,
+                          const argument& result,
+                          const argument& arg,
+                          Op op,
+                          T init,
+                          Input read_input,
+                          Output read_output,
+                          std::size_t relements,
+                          std::size_t stride)
+{
+    hip_visit_all(result, arg)([&](auto output, auto input) {
+        auto nelements = result.get_shape().elements();
+
+        const std::size_t max_block_size = 256;
+        const std::size_t block_size     = compute_block_size(relements, max_block_size);
+        gs_launch(stream, nelements * block_size, block_size)([=](auto i, auto idx) __device__ {
+            const auto out_idx  = i / block_size;
+            const auto base_idx = out_idx * stride;
+            auto r = block_reduce<max_block_size>(idx, op, init, relements, [&](auto j) __device__ {
+                return read_input(input.data()[base_idx + j]);
+            });
+            if(idx.local == 0)
+                output.data()[out_idx] = read_output(r);
+        });
+    });
+}
+
+template <class Op, class T, class Input, class Output>
+void reduce(hipStream_t stream,
+            const argument& result,
+            const argument& arg,
+            Op op,
+            T init,
+            Input read_input,
+            Output read_output)
+{
+    auto&& output_shape = result.get_shape();
+    auto&& input_shape  = arg.get_shape();
+    if(input_shape.standard() and output_shape.standard() and
+       output_shape.lens().back() != input_shape.lens().back() and
+       std::equal(output_shape.lens().begin(),
+                  std::prev(output_shape.lens().end()),
+                  input_shape.lens().begin()))
+    {
+        std::size_t stride = std::accumulate(input_shape.strides().begin(),
+                                             input_shape.strides().end(),
+                                             1,
+                                             std::multiplies<size_t>());
+        reduce_standard_impl(stream,
+                             result,
+                             arg,
+                             op,
+                             init,
+                             read_input,
+                             read_output,
+                             input_shape.lens().back(),
+                             stride);
+    }
+    else
+    {
+        std::vector<std::size_t> reduce_lens;
+        std::transform(output_shape.lens().begin(),
+                       output_shape.lens().end(),
+                       input_shape.lens().begin(),
+                       std::back_inserter(reduce_lens),
+                       [](auto x, auto y) -> std::size_t {
+                           if(x == y)
+                               return 1;
+                           else
+                               return y;
+                       });
+        shape reduce_slice{output_shape.type(), reduce_lens};
+        reduce_multi_impl(stream, result, arg, op, init, read_input, read_output, reduce_slice);
+    }
+}
+
 } // namespace device
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS

src/targets/gpu/device/int8_gemm_pack.cpp

@@ -69,6 +69,8 @@ void int8_gemm_pack_b(hipStream_t stream, const argument& result, const argument
     });
 }
 
+void sync_stream(hipStream_t stream) { hipStreamSynchronize(stream); }
+
 } // namespace device
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS