Fixing most of the cppcheck errors. (#1142)

* fix cppcheck errors, first pass

* fix format

* fix returned value in examples

* add macro definitions for cppcheck

* fix the profile_gemm logic

* update the gemm profiler logic

* add more difinitions to cppcheck, fix couple more errors

* replace runtime error with message in device function

* fix a couple of int4 issues

* no return for fill function

* fix errors in data_types.hpp

* fix format

* fix few remaining errors

* fix errors in data_types.hpp

* fix last couple of errors in datat_types.hpp

Jenkinsfile

@@ -304,7 +304,7 @@ def buildHipClangJob(Map conf=[:]){
 
         gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCm', repo: 'composable_kernel') {
             withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
-                timeout(time: 20, unit: 'HOURS')
+                timeout(time: 48, unit: 'HOURS')
                 {
                     cmake_build(conf)
                 }
@@ -755,7 +755,11 @@ pipeline {
                                 -o -iname \'*.cl\' \
                                 | grep -v 'build/' \
                                 | xargs -n 1 -P 1 -I{} -t sh -c \'clang-format-12 -style=file {} | diff - {}\' && \
-                                /cppcheck/build/bin/cppcheck ../* -v -j \$(nproc) -I ../include -I ../profiler/include -I ../library/include --file-filter=*.cpp --enable=all --output-file=ck_cppcheck.log"
+                                /cppcheck/build/bin/cppcheck ../* -v -j \$(nproc) -I ../include -I ../profiler/include -I ../library/include \
+                                -D CK_ENABLE_FP64 -D CK_ENABLE_FP32 -D CK_ENABLE_FP16 -D CK_ENABLE_FP8 -D CK_ENABLE_BF16 -D CK_ENABLE_BF8 -D CK_ENABLE_INT8 -D DL_KERNELS \
+                                -D __gfx908__ -D __gfx90a__ -D __gfx940__ -D __gfx941__ -D __gfx942__ -D __gfx1030__ -D __gfx1100__ -D __gfx1101__ -D __gfx1102__ \
+                                -U __gfx803__ -U __gfx900__ -U __gfx906__ -U CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4 \
+                                --file-filter=*.cpp --force --enable=all --output-file=ck_cppcheck.log"
                     }
                     steps{
                         buildHipClangJobAndReboot(setup_cmd: "", build_cmd: "", execute_cmd: execute_cmd, no_reboot:true)

example/01_gemm/gemm_dl_int4.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #include "common.hpp"
 
@@ -43,3 +41,4 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
 #include "run_gemm_example.inc"
 
 int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
+#endif
\ No newline at end of file

example/01_gemm/gemm_xdl_int4.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #include "common.hpp"
 
@@ -44,3 +42,4 @@ using ReferenceGemmInstance = ck::tensor_operation::host::
 #include "run_gemm_example.inc"
 
 int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
+#endif
\ No newline at end of file

example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_int4.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #include "common.hpp"
 
@@ -58,3 +56,4 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataTyp
 #include "run_gemm_add_add_fastgelu_example.inc"
 
 int main(int argc, char* argv[]) { return !run_gemm_add_add_fastgelu_example(argc, argv); }
+#endif

example/10_convnd_fwd_multiple_d_multiple_reduce/convnd_fwd_max_xdl_int4.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #define BUILD_INT4_EXAMPLE
 
@@ -24,3 +22,4 @@ using RsDataType        = ck::Tuple<R0DataType>;
 #include "run_convnd_fwd_max_example.inc"
 
 int main(int argc, char* argv[]) { return !run_convnd_fwd_max_example(argc, argv); }
+#endif

example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp

@@ -274,13 +274,12 @@ int main(int argc, char* argv[])
             {
                 auto reduce0_acc         = reduce0_op.GetIdentityValue<ReduceAccDataType>();
                 auto reduce1_acc         = reduce1_op.GetIdentityValue<ReduceAccDataType>();
-
+                ReduceAccDataType d0_val = 0;
+                ReduceAccDataType d1_val = 0;
                 for(int n = 0; n < N; ++n)
                 {
                     auto c_val =
                         ck::type_convert<ReduceAccDataType>(c_g_m_n_host_result(batch, m, n));
-                    ReduceAccDataType d0_val;
-                    ReduceAccDataType d1_val;
 
                     UnaryIdenticElementOp{}(d0_val, c_val);
                     UnarySquareElementOp{}(d1_val, c_val);

example/30_grouped_conv_fwd_multiple_d/grouped_conv_fwd_bias_relu_add_xdl_int4.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #include "common.hpp"
 
@@ -29,3 +27,4 @@ using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd;
 #include "run_grouped_conv_fwd_bias_relu_add_example.inc"
 
 int main(int argc, char* argv[]) { return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv); }
+#endif

example/31_batched_gemm_gemm/batched_gemm_gemm_xdl_int4.cpp

@@ -9,9 +9,7 @@ Gemm + Gemm fused operation. Computes C_m_o = A_m_k * B0_k_n * B1_n_o
                                                        Gemm1
 */
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #include <iostream>
 #include <numeric>
@@ -144,3 +142,4 @@ static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
 #endif
 
 int main(int argc, char* argv[]) { return run_batched_gemm_gemm_example(argc, argv) ? 0 : 1; }
+#endif

example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_int4.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-#error Should compile this file with ck::int4_t support
-#endif
+#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 
 #include <cstdlib>
 #include <iostream>
@@ -120,3 +118,4 @@ static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
 #endif
 
 int main(int argc, char* argv[]) { return run_grouped_conv_conv_fwd_example(argc, argv) ? 0 : 1; }
+#endif

example/48_pool3d_fwd/pool3d_fwd_common.hpp

@@ -32,6 +32,8 @@ std::vector<ck::index_t> f_tensor_strides_ncdhw(ck::index_t N_,
         return {C_ * D * H * W, D * H * W, H * W, W, 1_uz};
     else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
         return {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_};
+    throw std::runtime_error("Pool3d_fwd: problem with layout. ");
+    return {0, 0, 0, 0, 0};
 };
 
 template <typename TensorLayout>
@@ -53,6 +55,8 @@ HostTensorDescriptor f_host_tensor_descriptor(std::size_t N_,
         return HostTensorDescriptor({N_, C_, D, H, W},
                                     {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_});
     }
+    throw std::runtime_error("Pool3d_fwd: problem with layout. ");
+    return HostTensorDescriptor({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0});
 };
 
 template <typename DevicePoolFwdInstance,

example/51_avgpool3d_bwd/avgpool3d_bwd_common.hpp

@@ -26,6 +26,8 @@ std::vector<ck::index_t> f_tensor_strides_ncdhw(ck::index_t N_,
         return {C_ * D * H * W, D * H * W, H * W, W, 1_uz};
     else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
         return {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_};
+    throw std::runtime_error("Avgpool3d_bwd: problem with layout. ");
+    return {0, 0, 0, 0, 0};
 };
 
 template <typename TensorLayout>
@@ -47,6 +49,8 @@ HostTensorDescriptor f_host_tensor_descriptor(std::size_t N_,
         return HostTensorDescriptor({N_, C_, D, H, W},
                                     {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_});
     }
+    throw std::runtime_error("Avgpool3d_bwd: problem with layout. ");
+    return HostTensorDescriptor({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0});
 };
 
 template <typename DevicePoolBwdInstance,

include/ck/utility/data_type.hpp

@@ -189,6 +189,7 @@ struct vector_type<T, 1>
     }
 };
 
+int static err = 0;
 template <typename T>
 struct vector_type<T, 2>
 {
@@ -221,6 +222,10 @@ struct vector_type<T, 2>
         {
             return data_.d2x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -236,6 +241,10 @@ struct vector_type<T, 2>
         {
             return data_.d2x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -278,6 +287,10 @@ struct vector_type<T, 4>
         {
             return data_.d4x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -298,6 +311,10 @@ struct vector_type<T, 4>
         {
             return data_.d4x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -347,6 +364,10 @@ struct vector_type<T, 8>
         {
             return data_.d8x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -372,6 +393,10 @@ struct vector_type<T, 8>
         {
             return data_.d8x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -428,6 +453,10 @@ struct vector_type<T, 16>
         {
             return data_.d16x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -458,6 +487,10 @@ struct vector_type<T, 16>
         {
             return data_.d16x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -520,6 +553,10 @@ struct vector_type<T, 32>
         {
             return data_.d32x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -554,6 +591,10 @@ struct vector_type<T, 32>
         {
             return data_.d32x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -623,6 +664,10 @@ struct vector_type<T, 64>
         {
             return data_.d64x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -662,6 +707,10 @@ struct vector_type<T, 64>
         {
             return data_.d64x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -737,6 +786,10 @@ struct vector_type<T, 128>
         {
             return data_.d128x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -780,6 +833,10 @@ struct vector_type<T, 128>
         {
             return data_.d128x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 
@@ -861,6 +918,10 @@ struct vector_type<T, 256>
         {
             return data_.d256x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 
     template <typename X>
@@ -908,6 +969,10 @@ struct vector_type<T, 256>
         {
             return data_.d256x1_;
         }
+        else
+        {
+            return err;
+        }
     }
 };
 

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

@@ -265,6 +265,8 @@ struct ReferenceColumnToImage : public device::BaseOperator
 
                 return 0;
             }
+            throw std::runtime_error("Col2Img: number of dimensions should be between 1 and 3.");
+            return 1;
         }
 
         float Run(const device::BaseArgument* p_arg,

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

@@ -313,6 +313,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
 
                 return 0;
             }
+            throw std::runtime_error(
+                "Conv_bwd_data: number of dimensions must be between 1 and 3.");
+            return 1;
         }
 
         float Run(const device::BaseArgument* p_arg,

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

@@ -265,6 +265,8 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
 
                 return 0;
             }
+            throw std::runtime_error("Conv_bwd: number of dimensions must be between 1 and 3.");
+            return 1;
         }
 
         float Run(const device::BaseArgument* p_arg,

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

@@ -360,6 +360,8 @@ struct ReferenceConvFwd : public device::BaseOperator
 
                 return 0;
             }
+            throw std::runtime_error("Conv_fwd: number of dimensions must be between 1 and 3.");
+            return 1;
         }
 
         float Run(const device::BaseArgument* p_arg,

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

@@ -63,12 +63,11 @@ struct ReferenceGemm : public device::BaseOperator
                 const int K = arg.a_m_k_.mDesc.GetLengths()[1];
 
                 AccDataType v_acc = 0;
+                ComputeTypeA v_a  = 0;
+                ComputeTypeB v_b  = 0;
 
                 for(int k = 0; k < K; ++k)
                 {
-                    ComputeTypeA v_a;
-                    ComputeTypeB v_b;
-
                     // use PassThrough instead of ConvertBF16RTN for reference calculation
                     if constexpr(is_same_v<AElementwiseOperation,
                                            ck::tensor_operation::element_wise::ConvertBF16RTN>)
@@ -94,7 +93,7 @@ struct ReferenceGemm : public device::BaseOperator
                         ck::type_convert<AccDataType>(v_a) * ck::type_convert<AccDataType>(v_b);
                 }
 
-                CDataType v_c;
+                CDataType v_c = 0;
 
                 arg.c_element_op_(v_c, v_acc);
 

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

@@ -230,6 +230,8 @@ struct ReferenceImageToColumn : public device::BaseOperator
 
                 return 0;
             }
+            throw std::runtime_error("Img2Col: number of dimensions should be between 1 and 3.");
+            return 1;
         }
 
         float Run(const device::BaseArgument* p_arg,

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_gemm.hpp

@@ -106,9 +106,8 @@ struct DeviceOperationInstanceFactory<
         return op_ptrs;
     }
 };
-
+#endif
 } // namespace instance
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
-#endif

library/include/ck/library/tensor_operation_instance/gpu/gemm_streamk.hpp

@@ -114,9 +114,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmSt
         return op_ptrs;
     }
 };
-
+#endif
 } // namespace instance
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
-#endif