feat(megdnn/rocm): add average inclusive mode for pooling

GitOrigin-RevId: 08162bd1fb42d5dae04c0733d843a9f3595ef7f2

dnn/src/rocm/miopen_wrapper.cpp

@@ -9,6 +9,7 @@
  * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  */
 #include "hcc_detail/hcc_defs_prologue.h"
+#include "megdnn/opr_param_defs.h"
 #include "src/rocm/miopen_wrapper.h"
 
 #include "src/common/utils.h"
@@ -114,6 +115,9 @@ void PoolingDesc::set(const param::Pooling& param) {
         case param::Pooling::Mode::AVERAGE_COUNT_EXCLUDE_PADDING:
             mode = miopenPoolingAverage;
             break;
+        case param::Pooling::Mode::AVERAGE:
+            mode = miopenPoolingAverageInclusive;
+            break;
         default:
             megdnn_throw(megdnn_mangle("Unsupported pooling mode for miopen"));
     }

dnn/test/rocm/pooling.cpp

@@ -31,10 +31,6 @@ TEST_F(ROCM, POOLING_FORWARD) {
         for (auto format : {Format::NCHW})
             for (auto&& arg : args) {
                 auto param = arg.param;
-                if (param.mode == param::Pooling::Mode::AVERAGE) {
-                    param.mode =
-                            param::Pooling::Mode::AVERAGE_COUNT_EXCLUDE_PADDING;
-                }
                 auto src = arg.ishape;
                 param.format = format;
                 Checker<Pooling> checker(handle_rocm());
@@ -53,11 +49,6 @@ TEST_F(ROCM, POOLING_BACKWARD) {
         TensorLayout ilayout = TensorLayout(arg.ishape, dtype::Float32());
         TensorLayout olayout;
 
-        auto& param = arg.param;
-        if (param.mode == param::Pooling::Mode::AVERAGE) {
-            param.mode = param::Pooling::Mode::AVERAGE_COUNT_EXCLUDE_PADDING;
-        }
-
         auto constraint = [this,
                            arg](CheckerHelper::TensorValueArray& tensors_orig) {
             megdnn_assert(tensors_orig.size() == 4);
@@ -141,7 +132,7 @@ TEST_F(ROCM, POOLING_FWD_BENCHMARK) {
         benchmarker.set_param(param);
         size_t OH = infer_conv_shape(IH, FH, SH, PH);
         size_t OW = infer_conv_shape(IW, FW, SW, PW);
-        // warm up  
+        // warm up
         benchmarker.execs({{N, IC, IH, IW}, {N, IC, OH, OW}});
         // do actual benchmark
         auto time_ms = benchmarker.execs({{N, IC, IH, IW}, {N, IC, OH, OW}});