Merge branch 'develop' into eliminate-more-contiguous

CMakeLists.txt

@@ -125,6 +125,8 @@ rocm_enable_cppcheck(
         functionConst:*program.*
         shadowFunction
         shadowVar
+        shadowVariable
+        unsafeClassDivZero
         definePrefix:*test/include/test.hpp
     FORCE
     INCONCLUSIVE

Jenkinsfile

@@ -99,13 +99,14 @@ rocmtest tidy: rocmnode('rocmtest') { cmake_build ->
             | xargs -n 1 -P 1 -I{} -t sh -c \'clang-format-5.0 -style=file {} | diff - {}\'
         '''
     }
-}, clang: rocmnode('vega') { cmake_build ->
+}, clang_debug: rocmnode('vega') { cmake_build ->
     stage('Clang Debug') {
-        // TODO: Enanle integer
+        // TODO: Enable integer
         def sanitizers = "undefined"
         def debug_flags = "-g -fno-omit-frame-pointer -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
         cmake_build("hcc", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}'")
     }
+}, clang_release: rocmnode('vega') { cmake_build ->
     stage('Clang Release') {
         cmake_build("hcc", "-DCMAKE_BUILD_TYPE=release")
     }

dev-requirements.txt

 pfultz2/rocm-recipes
-danmar/cppcheck@8aa68ee297c2d9ebadf5bcfd00c66ea8d9291e35 -DHAVE_RULES=1
+danmar/cppcheck@ef714225bb31e9a76ac2484796763572386955ae -DHAVE_RULES=1
 ROCm-Developer-Tools/HIP@2490e42baa7d90458f0632fd9fbead2d395f41b9
 python/cpython@v3.6.6 -X autotools -H sha256:92aa914572c695c0aeb01b0a214813f414da4b51a371234df514a74761f2bb36
 -f requirements.txt

src/include/migraphx/argument.hpp

@@ -36,7 +36,7 @@ struct argument : raw_data<argument>
     }
 
     /// Provides a raw pointer to the data
-    std::function<char*()> data;
+    std::function<char*()> data = nullptr;
 
     /// Whether data is available
     bool empty() const { return not data; }

src/include/migraphx/matcher.hpp

@@ -374,14 +374,14 @@ MIGRAPHX_PRED_MATCHER(same_input_shapes, instruction_ref ins)
         ins->inputs().begin(), ins->inputs().end(), [&](auto x) { return x->get_shape() == s; });
 }
 
-MIGRAPHX_BASIC_MATCHER(output, matcher_context& ctx, instruction_ref ins)
+MIGRAPHX_BASIC_MATCHER(output, const matcher_context& ctx, instruction_ref ins)
 {
     if(ins->outputs().size() == 1)
         return ins->outputs().front();
     return ctx.not_found();
 }
 
-MIGRAPHX_BASIC_MATCHER(used_once, matcher_context& ctx, instruction_ref ins)
+MIGRAPHX_BASIC_MATCHER(used_once, const matcher_context& ctx, instruction_ref ins)
 {
     if(ins->outputs().size() == 1)
         return ins;
@@ -482,7 +482,7 @@ inline auto nargs(std::size_t n)
 
 inline auto arg(std::size_t i)
 {
-    return make_basic_fun_matcher([=](matcher_context& ctx, instruction_ref ins) {
+    return make_basic_fun_matcher([=](const matcher_context& ctx, instruction_ref ins) {
         if(i < ins->inputs().size())
             return ins->inputs()[i];
         return ctx.not_found();

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/quant_convolution.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_QUANT_CONVOLUTION_HPP
+#define MIGRAPHX_GUARD_OPERATORS_QUANT_CONVOLUTION_HPP
+
+#include <array>
+#include <migraphx/op/common.hpp>
+#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 quant_convolution
+{
+    std::array<std::size_t, 2> padding  = {{0, 0}};
+    std::array<std::size_t, 2> stride   = {{1, 1}};
+    std::array<std::size_t, 2> dilation = {{1, 1}};
+
+    padding_mode_t padding_mode = default_;
+    int group                   = 1;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.padding, "padding"),
+                    f(self.stride, "stride"),
+                    f(self.dilation, "dilation"),
+                    f(self.padding_mode, "padding_mode"),
+                    f(self.group, "group"));
+    }
+
+    std::string name() const { return "quant_convolution"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(2).same_type().same_ndims().only_dims(4);
+
+        const shape& input   = inputs.at(0);
+        const shape& weights = inputs.at(1);
+        auto t               = input.type();
+
+        // all input type must be int8_type and output is float_type
+        if(t != shape::int8_type)
+        {
+            MIGRAPHX_THROW("QUANT_CONVOLUTION: only accept input and weights of type int8_t");
+        }
+        t = shape::int32_type;
+
+        return {t,
+                {
+                    input.lens()[0],
+                    weights.lens()[0],
+                    std::size_t(std::max<std::ptrdiff_t>(
+                        1,
+                        (input.lens()[2] - (1 + dilation[0] * (weights.lens()[2] - 1)) +
+                         2 * padding[0]) /
+                                stride[0] +
+                            1)),
+                    std::size_t(std::max<std::ptrdiff_t>(
+                        1,
+                        (input.lens()[3] - (1 + dilation[1] * (weights.lens()[3] - 1)) +
+                         2 * padding[1]) /
+                                stride[1] +
+                            1)),
+                }};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/quant_dot.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_QUANT_DOT_HPP
+#define MIGRAPHX_GUARD_OPERATORS_QUANT_DOT_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 quant_dot
+{
+    int32_t alpha = 1;
+    int32_t beta  = 1;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(as_number(self.alpha), "alpha"), f(as_number(self.beta), "beta"));
+    }
+
+    std::string name() const { return "quant_dot"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{{inputs.at(0), inputs.at(1)}, *this}.same_type();
+        const shape& a = inputs.at(0);
+        const shape& b = inputs.at(1);
+        auto t         = a.type();
+        if(t != shape::int8_type)
+        {
+            MIGRAPHX_THROW("QUANT_DOT: only support data type int8_t");
+        }
+
+        if(!std::all_of(inputs.begin(), inputs.end(), [](auto s) { return s.lens().size() >= 2; }))
+        {
+            MIGRAPHX_THROW("QUANT_DOT: dot only accept 2 or more dims operands");
+        }
+
+        // only handle the case that the batch size of a and b are the same
+        if(!std::equal(
+               a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
+        {
+            MIGRAPHX_THROW("QUANT_DOT: batch size of A and B mismatch: {" +
+                           to_string_range(a.lens()) + "} x {" + to_string_range(b.lens()) + "}");
+        }
+
+        std::size_t dim_0 = a.lens().size() - 2;
+        std::size_t dim_1 = a.lens().size() - 1;
+        if(a.lens()[dim_1] != b.lens()[dim_0])
+        {
+            MIGRAPHX_THROW("QUANT_DOT: inner dimensions do not match: {" +
+                           to_string_range(a.lens()) + "} x {" + to_string_range(b.lens()) + "}");
+        }
+
+        // k be multiple of 4
+        if((a.lens()[dim_1] % 4) != 0)
+        {
+            MIGRAPHX_THROW("QUANT_DOT: size of A {" + to_string_range(a.lens()) + "} and B {" +
+                           to_string_range(b.lens()) + "} must be multiple of 4 for int8 type");
+        }
+
+        auto out_lens   = a.lens();
+        out_lens[dim_1] = b.lens()[dim_1];
+        if(inputs.size() == 3 && out_lens != inputs.at(2).lens())
+        {
+            MIGRAPHX_THROW("QUANT_DOT: dimension mismatch, operand C: {" +
+                           to_string_range(inputs.at(2).lens()) +
+                           "}, cannot add to operand A * B: {" + to_string_range(out_lens) + "}");
+        }
+
+        if(inputs.size() == 3 && inputs.at(2).type() != shape::int32_type)
+        {
+            MIGRAPHX_THROW("QUANT_DOT: operand C type must be int32");
+        }
+
+        return {shape::int32_type, out_lens};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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>
@@ -45,6 +46,8 @@
 #include <migraphx/op/outline.hpp>
 #include <migraphx/op/pad.hpp>
 #include <migraphx/op/pooling.hpp>
+#include <migraphx/op/quant_convolution.hpp>
+#include <migraphx/op/quant_dot.hpp>
 #include <migraphx/op/pow.hpp>
 #include <migraphx/op/reduce_sum.hpp>
 #include <migraphx/op/reduce_mean.hpp>

src/include/migraphx/quantization.hpp

@@ -15,6 +15,15 @@ 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
+std::size_t capture_arguments(program& prog,
+                              const std::vector<std::string>& ins_names,
+                              const std::function<void(std::size_t, std::vector<argument>)>& func);
+std::shared_ptr<std::vector<std::pair<float, float>>>
+capture_arguments(program& prog, const std::vector<std::string>& ins_names);
+std::shared_ptr<std::vector<std::pair<float, float>>> capture_arguments(program& prog);
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/verify.hpp

@@ -168,6 +168,7 @@ bool verify_range(R1&& r1, R2&& r2, double tolerance = 80, double* out_error = n
 {
     double threshold = std::numeric_limits<range_value<R1>>::epsilon() * tolerance;
     auto error       = rms_range(r1, r2);
+    // cppcheck-suppress uninitvar
     if(out_error != nullptr)
         *out_error = error;
     return error <= threshold;

src/onnx/onnx.cpp

@@ -1011,9 +1011,10 @@ struct onnx_parser
         }
 
         std::vector<operation> vec_actv_funcs(vec_names.size());
-        std::transform(vec_names.begin(), vec_names.end(), vec_actv_funcs.begin(), [&](auto& fn) {
-            return map_actv_funcs[fn];
-        });
+        std::transform(vec_names.begin(),
+                       vec_names.end(),
+                       vec_actv_funcs.begin(),
+                       [&](const auto& fn) { return map_actv_funcs[fn]; });
 
         // To be added later
         float clip = 0.0;
@@ -1127,9 +1128,10 @@ struct onnx_parser
         }
 
         std::vector<operation> vec_actv_funcs(vec_names.size());
-        std::transform(vec_names.begin(), vec_names.end(), vec_actv_funcs.begin(), [&](auto& name) {
-            return map_actv_funcs[name];
-        });
+        std::transform(vec_names.begin(),
+                       vec_names.end(),
+                       vec_actv_funcs.begin(),
+                       [&](const auto& name) { return map_actv_funcs[name]; });
 
         float clip = 0.0;
         if(contains(attributes, "clip"))
@@ -1299,9 +1301,10 @@ struct onnx_parser
         }
 
         std::vector<operation> vec_actv_funcs(vec_names.size());
-        std::transform(vec_names.begin(), vec_names.end(), vec_actv_funcs.begin(), [&](auto& name) {
-            return map_actv_funcs[name];
-        });
+        std::transform(vec_names.begin(),
+                       vec_names.end(),
+                       vec_actv_funcs.begin(),
+                       [&](const auto& name) { return map_actv_funcs[name]; });
 
         float clip = 0.0;
         if(contains(attributes, "clip"))

src/opt/memory_coloring_impl.cpp

@@ -85,6 +85,9 @@ bool memory_coloring_impl::allocate(interval_ptr interval)
             offset += (element_size - (offset % element_size));
         conflict_queue.pop();
     }
+    // when int8 type is used, the offset could be any number
+    // if not 4-byte aligned, miopen int8 convolution can crash
+    offset         = (offset + 3) / 4 * 4;
     segment.offset = offset;
     MIGRAPHX_DEBUG(segment.dump());
     required_bytes = std::max(required_bytes, offset + segment.size);

src/opt/memory_coloring_impl.hpp

@@ -107,7 +107,7 @@ struct memory_coloring_impl
         return ins->name() == "check_context";
     }
 
-    static bool is_disjoin(live_range& range1, live_range& range2)
+    static bool is_disjoin(const live_range& range1, const live_range& range2)
     {
         if((range1.size == 0) || (range2.size == 0))
             return false;

src/program.cpp

@@ -241,7 +241,7 @@ instruction_ref program::remove_instructions(instruction_ref first, instruction_
     // TODO: Check every element
     assert(has_instruction(first));
     std::for_each(first, last, [&](instruction& ins) { ins.clear_arguments(); });
-    assert(std::all_of(first, last, [&](instruction& ins) { return ins.outputs().empty(); }));
+    assert(std::all_of(first, last, [&](const instruction& ins) { return ins.outputs().empty(); }));
     return impl->instructions.erase(first, last);
 }
 

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); })

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;
@@ -59,7 +80,7 @@ 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);
             }
@@ -79,21 +100,13 @@ 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);
             }
         }
 
@@ -103,5 +116,91 @@ 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
+std::size_t 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);
+    }
+
+    return num_quant_params;
+}
+
+std::shared_ptr<std::vector<std::pair<float, float>>>
+capture_arguments(program& prog, const std::vector<std::string>& ins_names)
+{
+    std::shared_ptr<std::vector<std::pair<float, float>>> int8_quant_params =
+        std::make_shared<std::vector<std::pair<float, float>>>();
+    std::shared_ptr<std::vector<float>> max_abs_vals = std::make_shared<std::vector<float>>();
+
+    auto calc_quant_params = [int8_quant_params, max_abs_vals](
+                                 std::size_t ins_index, std::vector<migraphx::argument> args) {
+        std::pair<float, float> param_pair{64.0f, 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));
+        max_abs_vals->at(ins_index) = std::max(max_abs_vals->at(ins_index), max_abs);
+
+        param_pair.first                 = 127.0f / max_abs_vals->at(ins_index);
+        int8_quant_params->at(ins_index) = param_pair;
+    };
+
+    auto num_params = capture_arguments(prog, ins_names, calc_quant_params);
+
+    int8_quant_params->resize(num_params, std::pair<float, float>(64.0f, 0.0f));
+    max_abs_vals->resize(num_params, 0.0f);
+
+    return int8_quant_params;
+}
+
+std::shared_ptr<std::vector<std::pair<float, float>>> capture_arguments(program& prog)
+{
+    std::vector<std::string> ins_names = {"dot", "convolution"};
+    return capture_arguments(prog, ins_names);
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/rewrite_rnn.cpp

@@ -674,7 +674,6 @@ void rewrite_rnn::apply_lstm(program& prog, instruction_ref ins) const
     std::vector<float> ihc_data(ihc_shape.elements(), 0.0);
 
     migraphx::shape pph_shape{type, {1, 3 * hidden_size}};
-    std::vector<float> pph_data(pph_shape.elements(), 0.0);
 
     auto actv_funcs         = lstm_actv_funcs(ins);
     auto lstm_op            = any_cast<op::lstm>(ins->get_operator());