update example

example/42_groupnorm/groupnorm_sigmoid_fp16.cpp

@@ -12,6 +12,7 @@
 #include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp"
 #include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
 
+#include "ck/library/utility/fill.hpp"
 #include "ck/library/utility/check_err.hpp"
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_common_util.hpp"
@@ -67,22 +68,41 @@ using DeviceInstance =
                                                       8,   // BetaScalarPerVector
                                                       8>;  // OutScalarPerVector
 
-int main()
+int main(int argc, char* argv[])
 {
-    ck::index_t N = 1;
+    ck::index_t N = 128;
     ck::index_t H = 16;
     ck::index_t W = 16;
     ck::index_t G = 32;
     ck::index_t C = 40;
 
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 6)
+    {
+        N = std::stoi(argv[1]);
+        H = std::stoi(argv[2]);
+        W = std::stoi(argv[3]);
+        G = std::stoi(argv[4]);
+        C = std::stoi(argv[5]);
+    }
+    else
+    {
+        std::cerr << "arg1 to 5: N, H, W, G, C" << std::endl;
+
+        return 1;
+    }
+
     Tensor<XDataType> x({N, H, W, G, C});
     Tensor<YDataType> y({N, H, W, G, C});
     Tensor<GammaDataType> gamma({G, C});
     Tensor<BetaDataType> beta({G, C});
 
-    x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0.0, 1.0});
-    gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
-    beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});
+    ck::utils::FillUniformDistribution<XDataType>{0.f, 1.f}(x.begin(), x.end());
+    ck::utils::FillUniformDistribution<GammaDataType>{0.f, 1.f}(gamma.begin(), gamma.end());
+    ck::utils::FillUniformDistribution<BetaDataType>{0.f, 1.f}(beta.begin(), beta.end());
 
     DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
     DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
@@ -116,9 +136,8 @@ int main()
         return 1;
     };
 
-    bool time_kernel = true;
     auto invoker_ptr = device_instance.MakeInvokerPointer();
-    float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel, true});
+    float ave_time   = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true, true});
 
     std::size_t num_btype = sizeof(XDataType) * N * H * W * G * C +
                             sizeof(YDataType) * N * H * W * G * C + sizeof(GammaDataType) * G * C +