mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-03-20 15:17:41 +00:00
revert delete of inc file
This commit is contained in:
lalala-sh
@@ -11,6 +11,7 @@
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#include "ck_tile/host.hpp"
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#include "flatmm_basic.hpp"
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#include "run_flatmm_example.inc"
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#include <type_traits>
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template <typename T>
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@@ -366,195 +367,6 @@ auto create_args(int argc, char* argv[])
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return std::make_tuple(result, arg_parser);
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}
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template <typename PrecType,
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typename FlatmmConfig,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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int run_flatmm_example_with_layouts(int argc,
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char* argv[],
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const ALayout a_layout = ALayout{},
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const BLayout b_layout = BLayout{},
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[[maybe_unused]] const CLayout c_layout = CLayout{})
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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using ADataType = typename GemmBasicTypeConfig<PrecType>::ADataType;
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using BDataType = typename GemmBasicTypeConfig<PrecType>::BDataType;
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using CDataType = typename GemmBasicTypeConfig<PrecType>::CDataType;
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using AccDataType = typename GemmBasicTypeConfig<PrecType>::AccDataType;
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ck_tile::index_t M = arg_parser.get_int("m");
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ck_tile::index_t N = arg_parser.get_int("n");
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ck_tile::index_t K = arg_parser.get_int("k");
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ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
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ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
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ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
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ck_tile::index_t kbatch = arg_parser.get_int("split_k");
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int n_warmup = arg_parser.get_int("warmup");
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int n_repeat = arg_parser.get_int("repeat");
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ck_tile::index_t init_method = arg_parser.get_int("init");
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// persistent not added
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stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
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stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
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stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
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ck_tile::HostTensor<ADataType> a_host(
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ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
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ck_tile::HostTensor<BDataType> b_origin_host(
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ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
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ck_tile::HostTensor<CDataType> c_rslt_host(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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// TODO: add different init types
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if(init_method == 0)
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{
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ck_tile::FillUniformDistribution<ADataType>{-.5f, .5f}(a_host);
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ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_origin_host);
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}
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else if(init_method == 1)
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{
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ck_tile::FillMonotonicSeq<ADataType>{}(a_host);
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ck_tile::FillMonotonicSeq<BDataType>{}(b_origin_host);
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}
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else if(init_method == 2)
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{
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ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_host);
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ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_origin_host);
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}
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else
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{
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a_host.SetZero();
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b_origin_host.SetZero();
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}
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ck_tile::DeviceMem a_dev_buf(a_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_dev_buf(c_rslt_host.get_element_space_size_in_bytes());
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a_dev_buf.ToDevice(a_host.data());
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c_rslt_host.SetZero();
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// do pre-shuffle
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ck_tile::HostTensor<BDataType> b_shuffle_host = shuffle_b<FlatmmConfig>(b_origin_host);
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ck_tile::DeviceMem b_shuffle_dev_buf(b_shuffle_host.get_element_space_size_in_bytes());
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b_shuffle_dev_buf.ToDevice(b_shuffle_host.data());
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invoke_flatmm<FlatmmConfig,
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ADataType,
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BDataType,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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ck_tile::tuple<>,
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CLayout>(a_dev_buf,
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b_shuffle_dev_buf,
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c_dev_buf,
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M,
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N,
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K,
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stride_A,
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stride_B,
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stride_C,
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kbatch,
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n_warmup,
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n_repeat);
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c_dev_buf.FromDevice(c_rslt_host.data());
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bool pass = true;
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if(arg_parser.get_int("v") == 1)
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{
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ck_tile::HostTensor<CDataType> c_ref_host(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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c_ref_host.SetZero();
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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a_host, b_origin_host, c_ref_host);
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const float max_accumulated_value =
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*std::max_element(c_ref_host.mData.begin(), c_ref_host.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_rslt_host,
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c_ref_host,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
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<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
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<< std::endl;
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std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
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}
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else if(arg_parser.get_int("v") == 2)
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{
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ck_tile::DeviceMem b_origin_dev_buf(b_origin_host.get_element_space_size_in_bytes());
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b_origin_dev_buf.ToDevice(b_origin_host.data());
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ck_tile::HostTensor<CDataType> c_gpu_ref_host(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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ck_tile::DeviceMem c_gpu_ref_dev_buf(c_gpu_ref_host.get_element_space_size_in_bytes());
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c_gpu_ref_host.SetZero();
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c_gpu_ref_dev_buf.SetZero();
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ADataType* d_A;
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BDataType* d_B;
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CDataType* d_C;
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ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
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ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
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ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
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ck_tile::hip_check_error(hipMemcpy(
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d_A, a_dev_buf.GetDeviceBuffer(), M * K * sizeof(ADataType), hipMemcpyHostToDevice));
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ck_tile::hip_check_error(hipMemcpy(d_B,
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b_origin_dev_buf.GetDeviceBuffer(),
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N * K * sizeof(BDataType),
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hipMemcpyHostToDevice));
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ck_tile::reference_gemm_gpu<ADataType,
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BDataType,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
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ck_tile::hip_check_error(hipMemcpy(c_gpu_ref_dev_buf.GetDeviceBuffer(),
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d_C,
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M * N * sizeof(CDataType),
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hipMemcpyDeviceToHost));
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ck_tile::hip_check_error(hipFree(d_A));
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ck_tile::hip_check_error(hipFree(d_B));
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ck_tile::hip_check_error(hipFree(d_C));
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c_gpu_ref_dev_buf.FromDevice(c_gpu_ref_host.data());
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const float max_accumulated_value =
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*std::max_element(c_gpu_ref_host.mData.begin(), c_gpu_ref_host.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_rslt_host,
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c_gpu_ref_host,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
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<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
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<< std::endl;
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std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
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}
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return pass;
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}
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template <template <typename PreType> typename FlatmmConfig>
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int run_flatmm_example(int argc, char* argv[])
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{
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@@ -167,120 +167,6 @@ struct is_8bit_type
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{
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};
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// template <typename DataType>
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// struct GemmConfig
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// {
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// #if defined(USING_MFMA_16x16x128_F8) //MI350 FP8 16X16
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 256;
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// static constexpr ck_tile::index_t K_Tile = 256;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 16;
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// static constexpr ck_tile::index_t N_Warp_Tile = 16;
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// static constexpr ck_tile::index_t K_Warp_Tile = 128;
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// #elif defined(USING_MFMA_32x32x64_F8) //MI350 FP8 32X32 (need tune)
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 128;
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// static constexpr ck_tile::index_t K_Tile = 128;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 32;
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// static constexpr ck_tile::index_t N_Warp_Tile = 32;
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// static constexpr ck_tile::index_t K_Warp_Tile = 64;
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// #elif defined(USING_MFMA_16x16x32_F16) //MI350 FP16 16X16 (need tune)
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 128;
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// static constexpr ck_tile::index_t K_Tile = 128;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 16;
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// static constexpr ck_tile::index_t N_Warp_Tile = 16;
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// static constexpr ck_tile::index_t K_Warp_Tile = 32;
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// #elif defined(USING_MFMA_32x32x16_F16) //MI350 FP16 32X32 (need tune)
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 128;
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// static constexpr ck_tile::index_t K_Tile = 128;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 32;
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// static constexpr ck_tile::index_t N_Warp_Tile = 32;
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// static constexpr ck_tile::index_t K_Warp_Tile = 16;
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// #elif defined(USING_MFMA_16x16x32_F8) //MI300 FP8 16X16
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// static constexpr ck_tile::index_t M_Tile = 16;
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// static constexpr ck_tile::index_t N_Tile = 64;
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// static constexpr ck_tile::index_t K_Tile = 256;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 16;
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// static constexpr ck_tile::index_t N_Warp_Tile = 16;
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// static constexpr ck_tile::index_t K_Warp_Tile = 64;
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// #elif defined(USING_MFMA_32x32x16_F8) //MI300 FP8 32X32 (need tune)
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 256;
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// static constexpr ck_tile::index_t K_Tile = 128;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 8;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 32;
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// static constexpr ck_tile::index_t N_Warp_Tile = 32;
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// static constexpr ck_tile::index_t K_Warp_Tile = 32;
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// #elif defined(USING_MFMA_16x16x16_F16) //MI300 FP16 16X16 (need tune)
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 128;
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// static constexpr ck_tile::index_t K_Tile = 128;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 16;
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// static constexpr ck_tile::index_t N_Warp_Tile = 16;
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// static constexpr ck_tile::index_t K_Warp_Tile = 32;
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// #elif defined(USING_MFMA_32x32x8_F16) //MI300 FP16 32X32 (need tune)
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 128;
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// static constexpr ck_tile::index_t K_Tile = 128;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 32;
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// static constexpr ck_tile::index_t N_Warp_Tile = 32;
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// static constexpr ck_tile::index_t K_Warp_Tile = 16;
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// #else
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// static constexpr ck_tile::index_t M_Tile = 128;
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// static constexpr ck_tile::index_t N_Tile = 256;
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// static constexpr ck_tile::index_t K_Tile = 256;
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// static constexpr ck_tile::index_t M_Warp = 1;
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// static constexpr ck_tile::index_t N_Warp = 4;
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// static constexpr ck_tile::index_t K_Warp = 1;
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// static constexpr ck_tile::index_t M_Warp_Tile = 16;
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// static constexpr ck_tile::index_t N_Warp_Tile = 16;
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// static constexpr ck_tile::index_t K_Warp_Tile = 128;
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// #endif
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// };
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template <typename FlatmmConfig,
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typename ADataType,
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typename BDataType,
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192
example/ck_tile/18_flatmm/run_flatmm_example.inc
Normal file
192
example/ck_tile/18_flatmm/run_flatmm_example.inc
Normal file
@@ -0,0 +1,192 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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template <typename PrecType,
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typename FlatmmConfig,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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int run_flatmm_example_with_layouts(int argc,
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char* argv[],
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const ALayout a_layout = ALayout{},
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const BLayout b_layout = BLayout{},
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[[maybe_unused]] const CLayout c_layout = CLayout{})
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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using ADataType = typename GemmBasicTypeConfig<PrecType>::ADataType;
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using BDataType = typename GemmBasicTypeConfig<PrecType>::BDataType;
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using CDataType = typename GemmBasicTypeConfig<PrecType>::CDataType;
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using AccDataType = typename GemmBasicTypeConfig<PrecType>::AccDataType;
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ck_tile::index_t M = arg_parser.get_int("m");
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ck_tile::index_t N = arg_parser.get_int("n");
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ck_tile::index_t K = arg_parser.get_int("k");
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ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
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ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
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ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
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ck_tile::index_t kbatch = arg_parser.get_int("split_k");
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int n_warmup = arg_parser.get_int("warmup");
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int n_repeat = arg_parser.get_int("repeat");
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ck_tile::index_t init_method = arg_parser.get_int("init");
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// persistent not added
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stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
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stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
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stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
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ck_tile::HostTensor<ADataType> a_host(
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||||
ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
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||||
ck_tile::HostTensor<BDataType> b_origin_host(
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ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
|
||||
ck_tile::HostTensor<CDataType> c_rslt_host(
|
||||
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
||||
|
||||
// TODO: add different init types
|
||||
if(init_method == 0)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{-.5f, .5f}(a_host);
|
||||
ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_origin_host);
|
||||
}
|
||||
else if(init_method == 1)
|
||||
{
|
||||
ck_tile::FillMonotonicSeq<ADataType>{}(a_host);
|
||||
ck_tile::FillMonotonicSeq<BDataType>{}(b_origin_host);
|
||||
}
|
||||
else if(init_method == 2)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_host);
|
||||
ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_origin_host);
|
||||
}
|
||||
else
|
||||
{
|
||||
a_host.SetZero();
|
||||
b_origin_host.SetZero();
|
||||
}
|
||||
|
||||
ck_tile::DeviceMem a_dev_buf(a_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem c_dev_buf(c_rslt_host.get_element_space_size_in_bytes());
|
||||
|
||||
a_dev_buf.ToDevice(a_host.data());
|
||||
c_rslt_host.SetZero();
|
||||
|
||||
// do pre-shuffle
|
||||
ck_tile::HostTensor<BDataType> b_shuffle_host = shuffle_b<FlatmmConfig>(b_origin_host);
|
||||
ck_tile::DeviceMem b_shuffle_dev_buf(b_shuffle_host.get_element_space_size_in_bytes());
|
||||
b_shuffle_dev_buf.ToDevice(b_shuffle_host.data());
|
||||
|
||||
invoke_flatmm<FlatmmConfig,
|
||||
ADataType,
|
||||
BDataType,
|
||||
ck_tile::tuple<>,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
ck_tile::tuple<>,
|
||||
CLayout>(a_dev_buf,
|
||||
b_shuffle_dev_buf,
|
||||
c_dev_buf,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
stride_A,
|
||||
stride_B,
|
||||
stride_C,
|
||||
kbatch,
|
||||
n_warmup,
|
||||
n_repeat);
|
||||
|
||||
c_dev_buf.FromDevice(c_rslt_host.data());
|
||||
bool pass = true;
|
||||
|
||||
if(arg_parser.get_int("v") == 1)
|
||||
{
|
||||
ck_tile::HostTensor<CDataType> c_ref_host(
|
||||
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
||||
c_ref_host.SetZero();
|
||||
|
||||
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
|
||||
a_host, b_origin_host, c_ref_host);
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(c_ref_host.mData.begin(), c_ref_host.mData.end());
|
||||
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
|
||||
K, kbatch, max_accumulated_value);
|
||||
pass = ck_tile::check_err(c_rslt_host,
|
||||
c_ref_host,
|
||||
"Error: Incorrect results!",
|
||||
rtol_atol.at(ck_tile::number<0>{}),
|
||||
rtol_atol.at(ck_tile::number<1>{}));
|
||||
|
||||
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
||||
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
||||
<< std::endl;
|
||||
std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
|
||||
}
|
||||
else if(arg_parser.get_int("v") == 2)
|
||||
{
|
||||
ck_tile::DeviceMem b_origin_dev_buf(b_origin_host.get_element_space_size_in_bytes());
|
||||
b_origin_dev_buf.ToDevice(b_origin_host.data());
|
||||
|
||||
ck_tile::HostTensor<CDataType> c_gpu_ref_host(
|
||||
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
||||
ck_tile::DeviceMem c_gpu_ref_dev_buf(c_gpu_ref_host.get_element_space_size_in_bytes());
|
||||
c_gpu_ref_host.SetZero();
|
||||
c_gpu_ref_dev_buf.SetZero();
|
||||
|
||||
ADataType* d_A;
|
||||
BDataType* d_B;
|
||||
CDataType* d_C;
|
||||
|
||||
ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
|
||||
ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
|
||||
ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
|
||||
|
||||
ck_tile::hip_check_error(hipMemcpy(
|
||||
d_A, a_dev_buf.GetDeviceBuffer(), M * K * sizeof(ADataType), hipMemcpyHostToDevice));
|
||||
ck_tile::hip_check_error(hipMemcpy(d_B,
|
||||
b_origin_dev_buf.GetDeviceBuffer(),
|
||||
N * K * sizeof(BDataType),
|
||||
hipMemcpyHostToDevice));
|
||||
|
||||
ck_tile::reference_gemm_gpu<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
|
||||
|
||||
ck_tile::hip_check_error(hipMemcpy(c_gpu_ref_dev_buf.GetDeviceBuffer(),
|
||||
d_C,
|
||||
M * N * sizeof(CDataType),
|
||||
hipMemcpyDeviceToHost));
|
||||
|
||||
ck_tile::hip_check_error(hipFree(d_A));
|
||||
ck_tile::hip_check_error(hipFree(d_B));
|
||||
ck_tile::hip_check_error(hipFree(d_C));
|
||||
|
||||
c_gpu_ref_dev_buf.FromDevice(c_gpu_ref_host.data());
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(c_gpu_ref_host.mData.begin(), c_gpu_ref_host.mData.end());
|
||||
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
|
||||
K, kbatch, max_accumulated_value);
|
||||
pass = ck_tile::check_err(c_rslt_host,
|
||||
c_gpu_ref_host,
|
||||
"Error: Incorrect results!",
|
||||
rtol_atol.at(ck_tile::number<0>{}),
|
||||
rtol_atol.at(ck_tile::number<1>{}));
|
||||
|
||||
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
||||
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
||||
<< std::endl;
|
||||
std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
|
||||
}
|
||||
|
||||
return pass;
|
||||
}
|
||||
Reference in New Issue
Block a user