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Ck tile GPU verification sample develop & Add the CK TILE GEMM to the CI/CD test (#1505)
* Finished the feature of gpu verification * Add the ck_tile_gemm test in the CI CD * add the include of tensor_layou in reference_gemm * Comment Addressed * split ck_tile fhma and gemm tests into separate stages * restructure the reference gemm * restructure a new reference_gemm api that could read the device mem --------- Co-authored-by: carlushuang <carlus.huang@amd.com> Co-authored-by: illsilin <Illia.Silin@amd.com>
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@@ -5,6 +5,7 @@
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/host_tensor.hpp"
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#include "ck_tile/ops/common/tensor_layout.hpp"
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#include <thread>
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namespace ck_tile {
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@@ -56,4 +57,121 @@ CK_TILE_HOST void reference_gemm(const HostTensor<ADataType>& a_m_k,
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make_ParallelTensorFunctor(f, M)(std::thread::hardware_concurrency());
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}
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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__global__ void naive_gemm_kernel(ADataType* A,
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BDataType* B,
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CDataType* C,
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ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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ck_tile::index_t strideA,
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ck_tile::index_t strideB,
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ck_tile::index_t strideC)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int row = idx / N; // Compute row index
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int col = idx % N; // Compute column index
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if(row < M && col < N)
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{
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AccDataType acc = 0.0;
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for(int k = 0; k < K; ++k)
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{
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acc += static_cast<AccDataType>(A[row * strideA + k]) *
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static_cast<AccDataType>(B[col * strideB + k]);
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}
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C[row * strideC + col] = acc; // Store as AccDataType
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}
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}
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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void reference_gemm_gpu(DeviceMem& a_device,
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DeviceMem& b_device,
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DeviceMem& c_device,
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index_t M,
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index_t N,
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index_t K,
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index_t stride_a,
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index_t stride_b,
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index_t stride_c)
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{
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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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hipError_t errA = hipMalloc(&d_A, M * K * sizeof(ADataType));
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hipError_t errB = hipMalloc(&d_B, N * K * sizeof(BDataType));
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hipError_t errC = hipMalloc(&d_C, M * N * sizeof(CDataType));
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if(errA != hipSuccess)
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{
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std::cerr << "Error allocating device memory for A: " << hipGetErrorString(errA)
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<< std::endl;
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return; // Early exit on error
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}
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if(errB != hipSuccess)
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{
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std::cerr << "Error allocating device memory for B: " << hipGetErrorString(errB)
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<< std::endl;
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return; // Early exit on error
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}
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if(errC != hipSuccess)
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{
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std::cerr << "Error allocating device memory for C: " << hipGetErrorString(errC)
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<< std::endl;
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return; // Early exit on error
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}
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errA = hipMemcpy(
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d_A, a_device.GetDeviceBuffer(), M * K * sizeof(ADataType), hipMemcpyHostToDevice);
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if(errA != hipSuccess)
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{
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std::cerr << "Error copying A to device: " << hipGetErrorString(errA) << std::endl;
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}
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errB = hipMemcpy(
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d_B, b_device.GetDeviceBuffer(), N * K * sizeof(BDataType), hipMemcpyHostToDevice);
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if(errB != hipSuccess)
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{
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std::cerr << "Error copying B to device: " << hipGetErrorString(errB) << std::endl;
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}
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int totalElements = M * N;
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int numThreadsPerBlock = 256; // Common choice for threads per block
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int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
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naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType>
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<<<numBlocks, numThreadsPerBlock>>>(d_A, d_B, d_C, M, N, K, stride_a, stride_b, stride_c);
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errC = hipMemcpy(
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c_device.GetDeviceBuffer(), d_C, M * N * sizeof(CDataType), hipMemcpyDeviceToHost);
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if(errC != hipSuccess)
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{
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std::cerr << "Error copying C to device: " << hipGetErrorString(errC) << std::endl;
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}
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errA = hipFree(d_A);
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if(errA != hipSuccess)
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{
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std::cerr << "Error free the A memory: " << hipGetErrorString(errA) << std::endl;
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}
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errB = hipFree(d_B);
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if(errB != hipSuccess)
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{
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std::cerr << "Error free the B memory: " << hipGetErrorString(errB) << std::endl;
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}
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errC = hipFree(d_C);
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if(errC != hipSuccess)
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{
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std::cerr << "Error free the C memory: " << hipGetErrorString(errC) << std::endl;
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}
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return;
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}
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} // namespace ck_tile
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