Cleaning part II

example/12_reduce/reduce_blockwise.cpp

@@ -140,7 +140,7 @@ class SimpleAppArgs
 
     int processArgs(int argc, char* argv[])
     {
-        unsigned int ch;
+        int ch;
 
         while(1)
         {

example/13_pool2d_fwd/pool2d_fwd.cpp

@@ -80,8 +80,8 @@ static void pool_host_verify(const Tensor<InDataType>& in,
                 for(int x = 0; x < window_spatial_lengths[1]; ++x)
                 {
                     int wi = wo * window_strides[1] + x - in_left_pads[1];
-                    if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
-                       wi < in.mDesc.GetLengths()[3])
+                    if(hi >= 0 && hi < ck::type_convert<int>(in.mDesc.GetLengths()[2]) && wi >= 0 &&
+                       wi < ck::type_convert<int>(in.mDesc.GetLengths()[3]))
                     {
                         AccDataType currVal = static_cast<AccDataType>(in(n, c, hi, wi));
 

example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp

@@ -131,7 +131,7 @@ int main(int argc, char* argv[])
 
     std::size_t flop = 0, num_btype = 0;
 
-    for(int i = 0; i < gemm_shapes.size(); i++)
+    for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
     {
         a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
             gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
@@ -168,7 +168,7 @@ int main(int argc, char* argv[])
         }
     }
 
-    for(int i = 0; i < gemm_shapes.size(); i++)
+    for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
     {
         a_tensors_device.emplace_back(
             std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
@@ -213,7 +213,7 @@ int main(int argc, char* argv[])
 
     if(do_verification)
     {
-        for(int i = 0; i < gemm_shapes.size(); i++)
+        for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
         {
             c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
             auto ref_gemm    = ReferenceGemmInstance{};

include/ck/tensor_operation/gpu/device/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp

@@ -698,7 +698,7 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
         }
 
         // Gridwise GEMM size
-        for(int i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
+        for(int i = 0; i < ck::type_convert<int>(arg.a_grid_desc_k0_m_k1_container_.size()); i++)
         {
             if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
                                             arg.b_grid_desc_k0_n_k1_container_[i],

include/ck/tensor_operation/gpu/device/device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp

@@ -1413,7 +1413,7 @@ struct DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho
         }
 
         // Gridwise GEMM size
-        for(int i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
+        for(int i = 0; i < ck::type_convert<int>(arg.a_grid_desc_k0_m_k1_container_.size()); i++)
         {
             if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
                                             arg.b_grid_desc_k0_n_k1_container_[i],

include/ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp

@@ -862,17 +862,17 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
     static bool IsSupportedArgument(const Argument& arg)
     {
         // Input tensors can't be bigger than 2GB each.
-        constexpr std::size_t GB2 = 2 * 1e9;
+        constexpr std::size_t GB2 = 1e9;
 
-        if(arg.a_grid_desc_k0_m_k1_.GetElementSpaceSize() > GB2)
+        if(ck::type_convert<std::size_t>(arg.a_grid_desc_k0_m_k1_.GetElementSpaceSize()) > GB2)
         {
             return false;
         }
-        if(arg.b_grid_desc_k0_n_k1_.GetElementSpaceSize() > GB2)
+        if(ck::type_convert<std::size_t>(arg.b_grid_desc_k0_n_k1_.GetElementSpaceSize()) > GB2)
         {
             return false;
         }
-        if(arg.c_grid_desc_m_n_.GetElementSpaceSize() > GB2)
+        if(ck::type_convert<std::size_t>(arg.c_grid_desc_m_n_.GetElementSpaceSize()) > GB2)
         {
             return false;
         }

include/ck/tensor_operation/gpu/device/device_grouped_gemm_xdl.hpp

@@ -290,17 +290,18 @@ struct DeviceGroupedGemmXdl
         {
             grid_size_ = 0;
 
-            group_count_ = static_cast<int>(gemm_shapes.size());
+            group_count_ = ck::type_convert<ck::index_t>(gemm_shapes.size());
 
-            if(!(group_count_ == p_a.size() && group_count_ == p_b.size() &&
-                 group_count_ == p_c.size()))
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_a.size()) &&
+                 group_count_ == ck::type_convert<ck::index_t>(p_b.size()) &&
+                 group_count_ == ck::type_convert<ck::index_t>(p_c.size())))
             {
                 throw std::runtime_error("wrong! group_count_ != P_a/b/c.size");
             }
 
             gemm_desc_kernel_arg_.reserve(group_count_);
 
-            for(index_t i = 0; i < gemm_shapes.size(); i++)
+            for(index_t i = 0; i < ck::type_convert<index_t>(gemm_shapes.size()); i++)
             {
                 const index_t M = gemm_shapes[i].M;
                 const index_t N = gemm_shapes[i].N;
@@ -487,7 +488,7 @@ struct DeviceGroupedGemmXdl
 
     static bool IsSupportedArgument(const Argument& arg)
     {
-        if(arg.gemm_desc_kernel_arg_.size() != arg.group_count_)
+        if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
             return false;
         else
             return true;

library/include/ck/library/host_tensor/host_reduction.hpp

@@ -211,7 +211,7 @@ struct ReductionHost
             AccDataType accuVal     = ReduceOpZeroVal<AccDataType, ReduceOpId>();
             IndexDataType accuIndex = 0;
 
-            for(IndexDataType i = 0; i < reduce_dim_indexes.size(); i++)
+            for(IndexDataType i = 0; i < ck::type_convert<IndexDataType>(reduce_dim_indexes.size()); i++)
             {
                 auto offset_reduce =
                     get_offset_from_index<NumReduceDim>(reduceStrides, reduce_dim_indexes[i]);
@@ -246,7 +246,7 @@ struct ReductionHost
                 auto offset_invariant =
                     get_offset_from_index<NumInvariantDim>(invariantStrides, invariant_index);
 
-                for(IndexDataType i = 0; i < reduce_dim_indexes.size(); i++)
+                for(IndexDataType i = 0; i < ck::type_convert<IndexDataType>(reduce_dim_indexes.size()); i++)
                 {
                     auto offset_reduce =
                         get_offset_from_index<NumReduceDim>(reduceStrides, reduce_dim_indexes[i]);

library/include/ck/library/host_tensor/host_tensor.hpp

@@ -316,7 +316,7 @@ float check_error(const Tensor<T>& ref, const Tensor<T>& result)
 
     constexpr float eps = 1e-10;
 
-    for(int i = 0; i < ref.mData.size(); ++i)
+    for(int i = 0; i < ck::type_convert<int>(ref.mData.size()); ++i)
     {
         float ref_v    = ck::type_convert<float>(ref.mData[i]);
         float result_v = ck::type_convert<float>(result.mData[i]);

library/include/ck/library/reference_tensor_operation/cpu/reference_conv_backward_weight.hpp

@@ -70,18 +70,18 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
             constexpr auto I1 = Number<1>{};
             auto f_kcyx       = [&](auto k, auto c, auto y, auto x) {
                 float v_acc = 0;
-                for(int n = 0; n < arg.out_n_k_ho_wo_.mDesc.GetLengths()[0]; ++n)
+                for(int n = 0; n < ck::type_convert<int>(arg.out_n_k_ho_wo_.mDesc.GetLengths()[0]); ++n)
                 {
-                    for(int ho = 0; ho < arg.out_n_k_ho_wo_.mDesc.GetLengths()[2]; ++ho)
+                    for(int ho = 0; ho < ck::type_convert<int>(arg.out_n_k_ho_wo_.mDesc.GetLengths()[2]); ++ho)
                     {
                         int hi = ho * arg.conv_strides_[I0] + y * arg.conv_dilations_[I0] -
                                  arg.in_left_pads_[I0];
-                        for(int wo = 0; wo < arg.out_n_k_ho_wo_.mDesc.GetLengths()[3]; ++wo)
+                        for(int wo = 0; wo < ck::type_convert<int>(arg.out_n_k_ho_wo_.mDesc.GetLengths()[3]); ++wo)
                         {
                             int wi = wo * arg.conv_strides_[I1] + x * arg.conv_dilations_[I1] -
                                      arg.in_left_pads_[I1];
-                            if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
-                               wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
+                            if(hi >= 0 && hi < ck::type_convert<int>(arg.in_n_c_hi_wi_.mDesc.GetLengths()[2]) &&
+                               wi >= 0 && wi < ck::type_convert<int>(arg.in_n_c_hi_wi_.mDesc.GetLengths()[3]))
                             {
                                 float v_out;
                                 float v_in;

library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation.hpp

@@ -73,18 +73,18 @@ struct ReferenceConvFwd_Bias_Activation : public device::BaseOperator
             auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
                 float v_acc = 0;
 
-                for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
+                for(int c = 0; c < ck::type_convert<int>(arg.wei_k_c_y_x_.mDesc.GetLengths()[1]); ++c)
                 {
-                    for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
+                    for(int y = 0; y < ck::type_convert<int>(arg.wei_k_c_y_x_.mDesc.GetLengths()[2]); ++y)
                     {
                         int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
                                  arg.in_left_pads_[0];
-                        for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
+                        for(int x = 0; x < ck::type_convert<int>(arg.wei_k_c_y_x_.mDesc.GetLengths()[3]); ++x)
                         {
                             int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
                                      arg.in_left_pads_[1];
-                            if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
-                               wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
+                            if(hi >= 0 && hi < ck::type_convert<int>(arg.in_n_c_hi_wi_.mDesc.GetLengths()[2]) && wi >= 0 &&
+                               wi < ck::type_convert<int>(arg.in_n_c_hi_wi_.mDesc.GetLengths()[3]))
                             {
                                 float v_in;
                                 float v_wei;

library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation_add.hpp

@@ -76,18 +76,18 @@ struct ReferenceConvFwd_Bias_Activation_Add : public device::BaseOperator
             auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
                 float v_acc = 0;
 
-                for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
+                for(int c = 0; c < ck::type_convert<int>(arg.wei_k_c_y_x_.mDesc.GetLengths()[1]); ++c)
                 {
-                    for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
+                    for(int y = 0; y < ck::type_convert<int>(arg.wei_k_c_y_x_.mDesc.GetLengths()[2]); ++y)
                     {
                         int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
                                  arg.in_left_pads_[0];
-                        for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
+                        for(int x = 0; x < ck::type_convert<int>(arg.wei_k_c_y_x_.mDesc.GetLengths()[3]); ++x)
                         {
                             int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
                                      arg.in_left_pads_[1];
-                            if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
-                               wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
+                            if(hi >= 0 && hi < ck::type_convert<int>(arg.in_n_c_hi_wi_.mDesc.GetLengths()[2]) && wi >= 0 &&
+                               wi < ck::type_convert<int>(arg.in_n_c_hi_wi_.mDesc.GetLengths()[3]))
                             {
                                 float v_in;
                                 float v_wei;

library/src/host_tensor/host_tensor.cpp

@@ -25,7 +25,7 @@ std::size_t HostTensorDescriptor::GetElementSize() const
 std::size_t HostTensorDescriptor::GetElementSpace() const
 {
     std::size_t space = 1;
-    for(int i = 0; i < mLens.size(); ++i)
+    for(int i = 0; i < ck::type_convert<int>(mLens.size()); ++i)
     {
         space += (mLens[i] - 1) * mStrides[i];
     }
@@ -68,7 +68,7 @@ void ostream_HostTensorDescriptor(const HostTensorDescriptor& desc, std::ostream
 // FIXME: remove
 void bf16_to_f32_(const Tensor<ck::bhalf_t>& src, Tensor<float>& dst)
 {
-    for(int i = 0; i < src.mData.size(); ++i)
+    for(int i = 0; i < ck::type_convert<int>(src.mData.size()); ++i)
         dst.mData[i] = ck::type_convert<float>(src.mData[i]);
 }
 #endif

profiler/include/profile_convnd_bwd_data_impl.hpp

@@ -222,7 +222,7 @@ static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
 {
     float max_diff = 1e-6;
 
-    for(int i = 0; i < ref.mData.size(); ++i)
+    for(int i = 0; i < ck::type_convert<int>(ref.mData.size()); ++i)
     {
         float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
         if(max_diff < diff)
@@ -236,16 +236,16 @@ template <typename DataType>
 void show_data_nhwc_layout(Tensor<DataType>& nhwc)
 {
     std::cout << "[";
-    for(int n = 0; n < nhwc.mDesc.GetLengths()[0]; n++)
+    for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
     {
         std::cout << "[";
-        for(int hi = 0; hi < nhwc.mDesc.GetLengths()[2]; hi++)
+        for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
         {
             std::cout << "[";
-            for(int wi = 0; wi < nhwc.mDesc.GetLengths()[3]; wi++)
+            for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
             {
                 std::cout << "[";
-                for(int c = 0; c < nhwc.mDesc.GetLengths()[1]; c++)
+                for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
                 {
                     std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << "  ";
                 }

profiler/include/profile_grouped_gemm_impl.hpp

@@ -71,7 +71,7 @@ void profile_grouped_gemm_impl(int do_verification,
             }
         };
 
-    int group_count = Ms.size();
+    std::size_t group_count = Ms.size();
 
     if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
          group_count == StrideBs.size() && group_count == StrideCs.size()))
@@ -83,7 +83,7 @@ void profile_grouped_gemm_impl(int do_verification,
     std::vector<Tensor<BDataType>> b_k_n;
     std::vector<Tensor<CDataType>> c_m_n_device_results;
 
-    for(int i = 0; i < Ms.size(); i++)
+    for(int i = 0; i < ck::type_convert<int>(Ms.size()); i++)
     {
         a_m_k.push_back(
             Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
@@ -144,7 +144,7 @@ void profile_grouped_gemm_impl(int do_verification,
 
     gemm_shapes.reserve(group_count);
 
-    for(int i = 0; i < group_count; i++)
+    for(int i = 0; i < ck::type_convert<int>(group_count); i++)
     {
         a_device_buf.emplace_back(
             std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpace()));
@@ -234,7 +234,7 @@ void profile_grouped_gemm_impl(int do_verification,
             float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
 
             std::size_t flop = 0, num_btype = 0;
-            for(int i = 0; i < gemm_shapes.size(); i++)
+            for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
             {
                 flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
 
@@ -258,7 +258,7 @@ void profile_grouped_gemm_impl(int do_verification,
 
             if(do_verification)
             {
-                for(int i = 0; i < gemm_shapes.size(); i++)
+                for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
                 {
 
                     c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());

profiler/src/profile_reduce.cpp

@@ -186,7 +186,7 @@ class AppArgs
 
     int processArgs(int argc, char* argv[])
     {
-        unsigned int ch;
+        int ch;
 
         optind++; // to skip the "reduce" module name
 

test/gemm_split_k/gemm_split_k.cpp

@@ -45,7 +45,7 @@ static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
 {
     float max_diff = 1e-6;
 
-    for(int i = 0; i < ref.mData.size(); ++i)
+    for(int i = 0; i < ck::type_convert<int>(ref.mData.size()); ++i)
     {
         float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
         if(max_diff < diff)

test/grouped_gemm/grouped_gemm_fp16.cpp

@@ -104,7 +104,7 @@ bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
     b_tensors_device.reserve(group_count);
     c_tensors_device.reserve(group_count);
 
-    for(int i = 0; i < gemm_shapes.size(); i++)
+    for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
     {
         a_tensors.emplace_back(Tensor<ADataType>(f_host_tensor_descriptor(
             gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
@@ -119,7 +119,7 @@ bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
         b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
     }
 
-    for(int i = 0; i < gemm_shapes.size(); i++)
+    for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
     {
         a_tensors_device.emplace_back(
             std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize()));
@@ -147,7 +147,7 @@ bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
 
     invoker_ptr->Run(argument_ptr.get());
 
-    for(int i = 0; i < gemm_shapes.size(); i++)
+    for(int i = 0; i < ck::type_convert<int>(gemm_shapes.size()); i++)
     {
         c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
 

test/reduce/reduce_no_index.cpp

@@ -460,7 +460,7 @@ class SimpleAppArgs
 
     int processArgs(int argc, char* argv[])
     {
-        unsigned int ch;
+        int ch;
 
         while(1)
         {

test/reduce/reduce_util.hpp

@@ -9,7 +9,7 @@ namespace reduce_util {
 template <typename T>
 void to_f32_vector(const Tensor<T>& src, Tensor<float>& dst)
 {
-    for(int i = 0; i < src.mData.size(); ++i)
+    for(int i = 0; i < ck::type_convert<int>(src.mData.size()); ++i)
         dst.mData[i] = type_convert<float>(src.mData[i]);
 }