Fix example_convnd_fwd_max_xdl_int8 failures on MI300 (#1666)

* Improve test verbosity.

* BUGFIX: Add missing initialization for reduction buffer

* Change default initialization method

Performance may be affected for fp32 and int8 examples.

* Improve test verbosity

* Cleanup

example/10_convnd_fwd_multiple_d_multiple_reduce/common.hpp

@@ -80,7 +80,7 @@ using RLayout = typename LayoutSettingSelector<NDimSpatial>::RLayout;
 struct ExecutionConfig final
 {
     bool do_verification = true;
-    int init_method      = 1;
+    int init_method      = 2;
     bool time_kernel     = false;
 };
 

example/10_convnd_fwd_multiple_d_multiple_reduce/run_convnd_fwd_max_example.inc

@@ -73,16 +73,25 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
     Tensor<EDataType> conv_output_device(conv_output_g_n_k_wos_desc);
     Tensor<R0DataType> r0_device(r0_desc);
 
+    std::cout << "input: " << conv_input.mDesc << std::endl;
+    std::cout << "weight: " << conv_weight.mDesc << std::endl;
+    std::cout << "output: " << conv_output_device.mDesc << std::endl;
+    std::cout << "reduction: " << r0_device.mDesc << std::endl << std::endl;
+
     switch(config.init_method)
     {
     case 0: break;
     case 1:
         ck::utils::FillUniformDistributionIntegerValue<ADataType>{-8, 7}(conv_input);
-        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-8, 7}(conv_weight);
+        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-1, 1}(conv_weight);
+        break;
+    case 2:
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-8, 7}(conv_input);
+        ck::utils::FillUniformDistribution<BDataType>{-1, 1}(conv_weight);
         break;
     default:
-        ck::utils::FillUniformDistribution<ADataType>{-5, 5}(conv_input);
-        ck::utils::FillUniformDistribution<BDataType>{-5, 5}(conv_weight);
+        ck::utils::FillUniformDistribution<ADataType>{-8, 7}(conv_input);
+        ck::utils::FillUniformDistribution<BDataType>{-1, 1}(conv_weight);
     }
 
     DeviceMem conv_input_device_buf(sizeof(ADataType) * conv_input.mDesc.GetElementSpaceSize());
@@ -161,15 +170,25 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
         return false;
     }
 
+    // XXX: DeviceGroupedConvFwdMultipleDMultipleR_Xdl_CShuffle will not initialize r0.
+    r0_device_buf.SetValue(ck::NumericLimits<R0DataType>::Lowest());
+
     const float avg_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
 
-    const std::size_t flop      = problem_size.GetFlops();
-    const std::size_t num_btype = problem_size.GetByte<ADataType, BDataType, EDataType>();
+    if(config.time_kernel)
+    {
+        const std::size_t flop      = problem_size.GetFlops();
+        const std::size_t num_btype = problem_size.GetByte<ADataType, BDataType, EDataType>();
 
-    const float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
-    const float gb_per_sec = num_btype / 1.E6 / avg_time;
-    std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
-              << conv.GetTypeString() << std::endl;
+        const float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+        const float gb_per_sec = num_btype / 1.E6 / avg_time;
+        std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << conv.GetTypeString() << std::endl;
+    }
+    else
+    {
+        std::cout << "FINISHED: " << conv.GetTypeString() << std::endl;
+    }
 
     if(config.do_verification)
     {
@@ -189,6 +208,7 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
                                                   BElementOp{},
                                                   PassThrough{});
 
+        std::cout << "\nRunning verification on CPU." << std::endl;
         ref_invoker.Run(ref_argument);
 
         Tensor<R0DataType> r0_host(r0_device.mDesc);
@@ -273,13 +293,18 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
         conv_output_device_buf.FromDevice(conv_output_device.mData.data());
         r0_device_buf.FromDevice(r0_device.mData.data());
 
-        return ck::utils::check_err(conv_output_device,
-                                    conv_output_host,
-                                    "Error: incorrect results! (Matrix E)",
-                                    1e-5f,
-                                    1e-4f) &&
-               ck::utils::check_err(
-                   r0_device, r0_host, "Error: incorrect results! (Matrix R0)", 1e-5f, 1e-4f);
+        auto pass = ck::utils::check_err(conv_output_device,
+                                         conv_output_host,
+                                         "Error: incorrect results! (Matrix E)",
+                                         1e-3f,
+                                         1e-3f);
+        pass =
+            pass && ck::utils::check_err(
+                        r0_device, r0_host, "Error: incorrect results! (Matrix R0)", 1e-3f, 1e-3f);
+        if(pass)
+            std::cout << "Verification on CPU: PASS" << std::endl;
+
+        return pass;
     }
 
     return true;

example/16_gemm_multi_d_multi_reduces/gemm_add_add_mean_meansquare_xdl_fp16.cpp

@@ -198,7 +198,7 @@ int main()
         throw std::runtime_error("wrong! this device_op instance does not support this problem");
     }
 
-    // init reducetion buffer to 0
+    // init reduction buffer to 0
     r0_device_buf.SetZero();
     r1_device_buf.SetZero();