code backup

src/include/migraphx/op/convert.hpp

@@ -42,7 +42,8 @@ struct convert : unary<convert>
             float res = scale * x + shift;
             if(target_type == shape::int8_type)
             {
-                res = res + 0.5f;
+                int factor = (res > 0) ? 1 : -1;
+                res        = res + factor * 0.5f;
                 res        = res > 127.0 ? 127.0 : res;
                 res        = res < -128.0 ? -128.0 : res;
             }

src/include/migraphx/quantization.hpp

@@ -15,7 +15,9 @@ struct program;
 void quantize(program& prog, const std::vector<std::string>& ins_names);
 void quantize(program& prog);
 
-void quantize_int8(program& prog, const std::vector<std::string>& ins_names);
+void quantize_int8(program& prog,
+                   const std::vector<std::string>& ins_names,
+                   std::vector<std::pair<float, float>>& int8_quant_params);
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/py/migraphx_py.cpp

@@ -186,6 +186,12 @@ PYBIND11_MODULE(migraphx, m)
         migraphx::quantize(p, ins_names);
     });
     m.def("quantize", [](migraphx::program& p) { migraphx::quantize(p, {"all"}); });
+    m.def("quantize_int8",
+          [](migraphx::program& p,
+             std::vector<std::string>& ins_names,
+             std::vector<std::pair<float, float>>& quant_params) {
+              migraphx::quantize_int8(p, ins_names, quant_params);
+          });
 
 #ifdef HAVE_GPU
     m.def("allocate_gpu", &migraphx::gpu::allocate_gpu, py::arg("s"), py::arg("host") = false);

src/quantization.cpp

@@ -119,8 +119,38 @@ void quantize(program& prog) { quantize(prog, {"all"}); }
 // -128 ~ 127. To convert the float or double to int8, we need a scale and
 // a shift, then the convert can be done as v_int8 = fp * scale + shift.
 // To simplify the changes, we consider shift as 0.0f for now.
-void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
+void quantize_int8(program& prog,
+                   const std::vector<std::string>& ins_names,
+                   std::vector<std::pair<float, float>>& int8_quant_params)
 {
+    // // For debugging
+    // auto print_gemm_res = [&](std::size_t ins_index, std::vector<migraphx::argument> args) {
+    //     // 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()); });
+    //     std::cout << "quant_gemm = " << std::endl;
+    //     for (size_t i = 0; i < 20; i++)
+    //     {
+    //         std::cout << vec_val[i] << "\t";
+    //     }
+    //     std::cout << std::endl;
+    // };
+
+    // // For debugging
+    // auto print_conv_res = [&](std::size_t ins_index, std::vector<migraphx::argument> args) {
+    //     // 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()); });
+    //     std::cout << "quant_conv = " << std::endl;
+    //     for (size_t i = 0; i < 20; i++)
+    //     {
+    //         std::cout << vec_val[i] << "\t";
+    //     }
+    //     std::cout << std::endl;
+    // };
+
     // For now, we only support the int8 quantization of gemm and convolution
     std::vector<std::string> op_names = {"dot", "convolution"};
     if(!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) {
@@ -130,9 +160,7 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
         MIGRAPHX_THROW("QUANTIZE_INT8: only support DOT and CONVOLUTION operation");
     }
 
-    // tmp value used just testing
-    std::vector<std::pair<float, float>> int8_param{{127.0f, 0.0f}, {127.0f, 0.0f}, {128.0f, 0.0f}};
-
+    std::size_t quant_param_index = 0;
     std::unordered_map<instruction_ref, instruction_ref> map_quant_ins;
     for(auto ins : iterator_for(prog))
     {
@@ -151,14 +179,15 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
         // to a int8 type by adding a convert operator and replace
         // the operator with the corresponding int8 version
         auto inputs = ins->inputs();
-        std::size_t param_index = 0;
+        std::vector<std::pair<float, float>> ins_quant_params;
         for(auto input : inputs)
         {
             // In general, the target_type is int8, but for the dot
             // operation, if it has 3 inputs, then the last one should
             // be converted to int32_type
             shape::type_t quant_type = shape::int8_type;
-            auto param               = int8_param[param_index++];
+            auto param               = int8_quant_params[quant_param_index++];
+            ins_quant_params.push_back(param);
             if(ins->name() == "dot" and inputs.size() == 3 and input == inputs.back())
             {
                 quant_type = shape::int32_type;
@@ -211,7 +240,8 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
         if(ins->name() == "dot")
         {
             auto dot_op = any_cast<op::dot>(ins->get_operator());
-            float new_alpha = dot_op.alpha / (int8_param[0].first * int8_param[1].first);
+            float new_alpha =
+                dot_op.alpha / (ins_quant_params[0].first * ins_quant_params[1].first);
             float new_beta = dot_op.beta;
             // We need additional checking about the quant_alpha value. If
             // abs(quant_alpha) > 50 (some tmp value set here), we can convert
@@ -335,7 +365,7 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
             auto dilation      = conv_op.dilation;
             auto padding_mode  = conv_op.padding_mode;
             auto group         = conv_op.group;
-            auto adjust_factor = 1.0 / (int8_param[0].first * int8_param[1].first);
+            auto adjust_factor = 1.0 / (ins_quant_params[0].first * ins_quant_params[1].first);
 
             shape quant_shape =
                 compute_shape(op::quant_convolution{padding, stride, dilation, padding_mode, group},

src/targets/gpu/device/convert.cpp

@@ -20,8 +20,10 @@ void convert(hipStream_t stream,
             if(target_type == shape::int8_type)
             {
                 gs_launch(stream, result.get_shape().elements())([=](auto i) {
-                    output_ptr[i] = std::min<int8_t>(
-                        std::max<float>(-128, input_ptr[i] * scale + shift + 0.5), 127);
+                    float res  = input_ptr[i] * scale + shift;
+                    int factor = (res > 0) ? 1 : -1;
+                    output_ptr[i] =
+                        std::min<int8_t>(std::max<float>(-128, res + factor * 0.5), 127);
                 });
             }
             else