Merge pull request #325 from ROCmSoftwarePlatform/op_capture

add capture operator

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
         {
+            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/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;
+                });
+            });
         }
-
+        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()));
+                        output(idx.begin(), idx.end()) = static_cast<const Derived&>(*this).apply()(
+                            input(idx.begin(), idx.end()));
                     });
-
-                return result;
                 });
             });
+        }
 
         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/program.cpp

@@ -113,6 +113,7 @@ void program::assign(const program& p)
         impl->instructions.clear();
     }
     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 insert_quant_ins(program& prog,
                                  instruction_ref& ins,
                                  shape::type_t type,
-                            std::unordered_map<instruction_ref, instruction_ref>& map_fp16)
+                                 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);
             }
@@ -78,13 +99,6 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
         auto op        = ins->get_operator();
         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()))
-            {
-                prog.add_instruction(op::convert{orig_type}, ins);
-            }
-            else
         {
             // check the dead code case to avoid assert
             bool output_empty = ins->outputs().empty();
@@ -95,7 +109,6 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
                 prog.replace_instruction(ins, ins_orig_type);
             }
         }
-        }
 
         prog.replace_instruction(ins, op, converted_inputs);
     }
@@ -103,5 +116,80 @@ 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/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/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

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

@@ -3,8 +3,6 @@
 
 #include <migraphx/shape.hpp>
 #include <migraphx/op/convert.hpp>
-#include <migraphx/gpu/oper.hpp>
-#include <migraphx/gpu/device/convert.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -12,7 +10,7 @@ namespace gpu {
 
 struct context;
 
-struct hip_convert : unary_device<hip_convert, device::convert>
+struct hip_convert
 {
     op::convert op;
 
@@ -22,13 +20,15 @@ struct hip_convert : unary_device<hip_convert, device::convert>
         return migraphx::reflect(self.op, f);
     }
 
-    hip_convert(op::convert oper) : op(oper) {}
+    std::string name() const { return "gpu::convert"; }
 
-    shape compute_shape(std::vector<shape> inputs) const
+    shape compute_shape(std::vector<shape> inputs) const;
+
+    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
+
+    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
     {
-        inputs.pop_back();
-        check_shapes{inputs}.packed();
-        return op.compute_shape(inputs);
+        return shapes.size() - 1;
     }
 };
 

src/targets/gpu/quant_gemm.cpp

 #include <migraphx/gpu/quant_gemm.hpp>
 #include <migraphx/gpu/context.hpp>
 #include <migraphx/generate.hpp>
+#include <fstream>
+#include <iomanip>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

test/cpu_ops_test.cpp

@@ -5,6 +5,7 @@
 #include <migraphx/instruction.hpp>
 #include <migraphx/quantization.hpp>
 #include <migraphx/cpu/target.hpp>
+#include <migraphx/quantization.hpp>
 #include <migraphx/verify.hpp>
 #include <migraphx/onnx.hpp>
 #include "test.hpp"
@@ -2028,4 +2029,39 @@ TEST_CASE(sqdiff_test)
     EXPECT(migraphx::verify_range(results_vector, gold));
 }
 
+TEST_CASE(op_capture)
+{
+    migraphx::program p;
+    migraphx::shape s1{migraphx::shape::float_type, {3, 3}};
+    migraphx::shape s2{migraphx::shape::float_type, {3, 6}};
+    std::vector<float> d1(s1.elements());
+    std::vector<float> d2(s2.elements());
+    std::iota(d1.begin(), d1.end(), 0.0f);
+    std::iota(d2.begin(), d2.end(), 0.0f);
+
+    auto p1 = p.add_literal(s1, d1);
+    auto p2 = p.add_literal(s1, d1);
+    auto pb = p.add_literal(s2, d2);
+    auto pc = p.add_literal(s2, d2);
+    auto pa = p.add_instruction(migraphx::op::add{}, p1, p2);
+    auto ps = p.add_instruction(migraphx::op::dot{}, pa, pb, pc);
+    p.add_instruction(migraphx::op::dot{}, pa, ps);
+
+    migraphx::program capture_p = p;
+    migraphx::capture_arguments(capture_p);
+
+    p.compile(migraphx::cpu::target{});
+    capture_p.compile(migraphx::cpu::target{});
+
+    auto cap_res = capture_p.eval({});
+    auto res     = p.eval({});
+
+    std::vector<float> vec;
+    std::vector<float> cap_vec;
+    cap_res.visit([&](auto output) { cap_vec.assign(output.begin(), output.end()); });
+    res.visit([&](auto output) { vec.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(vec, cap_vec));
+};
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/gpu/miopen.cpp

@@ -3816,4 +3816,21 @@ struct test_reduce_mean_half : verify_program<test_reduce_mean_half>
     };
 };
 
+struct test_convert : verify_program<test_convert>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        migraphx::shape sa{migraphx::shape::float_type, {8, 24}};
+        migraphx::shape sb{migraphx::shape::float_type, {24, 6}};
+        auto pa = p.add_parameter("a", sa);
+        auto pb = p.add_parameter("b", sb);
+        auto ia = p.add_instruction(migraphx::op::convert{migraphx::shape::int8_type}, pa);
+        auto ib = p.add_instruction(migraphx::op::convert{migraphx::shape::int8_type}, pb);
+        p.add_instruction(migraphx::op::quant_dot{}, ia, ib);
+
+        return p;
+    };
+};
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/type_conversion.cpp

@@ -202,4 +202,55 @@ TEST_CASE(literal_add)
     }
 }
 
+TEST_CASE(op_capture)
+{
+    auto test_func = [&](std::size_t ins_index, const std::vector<migraphx::argument>& args) {
+        (void)ins_index;
+        (void)args;
+    };
+
+    auto create_program_float = [] {
+        migraphx::program p;
+        migraphx::shape s1{migraphx::shape::float_type, {3, 3}};
+        migraphx::shape s2{migraphx::shape::float_type, {3, 6}};
+
+        auto p1 = p.add_parameter("x", s1);
+        auto p2 = p.add_parameter("y", s1);
+        auto pb = p.add_parameter("b", s2);
+        auto pc = p.add_parameter("c", s2);
+        auto pa = p.add_instruction(migraphx::op::add{}, p1, p2);
+        auto ps = p.add_instruction(migraphx::op::dot{}, pa, pb, pc);
+        p.add_instruction(migraphx::op::dot{}, pa, ps);
+
+        return p;
+    };
+
+    auto create_program_op = [&] {
+        migraphx::program p;
+        migraphx::shape s1{migraphx::shape::float_type, {3, 3}};
+        migraphx::shape s2{migraphx::shape::float_type, {3, 6}};
+
+        auto p1  = p.add_parameter("x", s1);
+        auto p2  = p.add_parameter("y", s1);
+        auto pb  = p.add_parameter("b", s2);
+        auto pc  = p.add_parameter("c", s2);
+        auto pa  = p.add_instruction(migraphx::op::add{}, p1, p2);
+        auto opb = p.insert_instruction(std::next(pb), migraphx::op::capture{1, test_func}, pb);
+        auto opc = p.insert_instruction(std::next(pc), migraphx::op::capture{2, test_func}, pc);
+        auto opa = p.add_instruction(migraphx::op::capture{0, test_func}, pa);
+        auto ps  = p.add_instruction(migraphx::op::dot{}, opa, opb, opc);
+        auto ops = p.add_instruction(migraphx::op::capture{3, test_func}, ps);
+        p.add_instruction(migraphx::op::dot{}, opa, ops);
+
+        return p;
+    };
+
+    {
+        auto p            = create_program_float();
+        auto op_capture_p = create_program_op();
+        migraphx::capture_arguments(p);
+        EXPECT(p == op_capture_p);
+    }
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }