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using ElementCompute = typename Gemm::GemmKernel::Epilogue::OutputOp::ElementCompute; /// Initialization cutlass::Distribution::Kind init_A; cutlass::Distribution::Kind init_B; cutlass::Distribution::Kind init_C; uint64_t seed; // // Methods // InterleavedTestbed( cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform, cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform, cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform, uint64_t seed_ = 2080 ): init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { } /// Helper to initialize a tensor view template bool initialize_tensor( cutlass::TensorView view, cutlass::Distribution::Kind dist_kind, uint64_t seed) { if (dist_kind == cutlass::Distribution::Uniform) { cutlass::reference::host::TensorFillRandomUniform( view, seed, 2, -2, 0); } else if (dist_kind == cutlass::Distribution::Identity) { cutlass::reference::host::TensorFillIdentity(view); } else if (dist_kind == cutlass::Distribution::Sequential) { cutlass::reference::host::BlockFillSequential( view.data(), view.capacity()); } else { // TODO: Implement the rest EXPECT_TRUE(false) << "Not implemented"; return false; } return true; } /// Executes one test bool run( cutlass::gemm::GemmCoord problem_size, ElementCompute alpha = ElementCompute(1), ElementCompute beta = ElementCompute(0)) { // // Allocate the GEMM workspace // cutlass::HostTensor< typename Gemm::ElementA, typename Gemm::LayoutA> tensor_A(problem_size.mk()); cutlass::HostTensor< typename Gemm::ElementB, typename Gemm::LayoutB> tensor_B(problem_size.kn()); cutlass::HostTensor< typename Gemm::ElementB, typename Gemm::LayoutB> tensor_B_reordered(problem_size.kn()); cutlass::HostTensor< typename Gemm::ElementC, typename Gemm::LayoutC> tensor_C(problem_size.mn()); cutlass::HostTensor< typename Gemm::ElementC, typename Gemm::LayoutC> tensor_D(problem_size.mn()); cutlass::HostTensor< typename Gemm::ElementC, typename Gemm::LayoutC> reference_D(problem_size.mn(), false); EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2019)); EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2018)); EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2017)); cutlass::reorder_column( tensor_B_reordered.host_ref(), tensor_B.host_ref(), problem_size); cutlass::reference::host::TensorCopy( reference_D.host_view(), tensor_C.host_view()); tensor_A.sync_device(); tensor_B_reordered.sync_device(); tensor_C.sync_device(); tensor_D.sync_device(); // // Initialize the GEMM operator // typename Gemm::Arguments arguments{ problem_size, tensor_A.device_ref(), tensor_B_reordered.device_ref(), tensor_C.device_ref(), tensor_D.device_ref(), {alpha, beta} }; Gemm gemm_op; cutlass::Status status = gemm_op.initialize(arguments); EXPECT_TRUE(status == cutlass::Status::kSuccess); // // Run the GEMM // status = gemm_op(); EXPECT_TRUE(status == cutlass::Status::kSuccess); // // Verify // cutlass::reference::host::Gemm< typename Gemm::ElementA, typename Gemm::LayoutA, typename Gemm::ElementB, typename Gemm::LayoutB, typename Gemm::ElementC, typename Gemm::LayoutC, ElementCompute, ElementAccumulator, typename Gemm::Operator> reference_gemm; reference_gemm( problem_size, alpha, tensor_A.host_ref(), tensor_B.host_ref(), beta, reference_D.host_ref(), ElementAccumulator(0) ); tensor_D.sync_host(); EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0); EXPECT_GT(cutlass::reference::host::TensorNorm(reference_D.host_view()), 0); bool passed = cutlass::reference::host::TensorEquals( reference_D.host_view(), tensor_D.host_view()); EXPECT_TRUE(passed); if (!passed) { std::stringstream fname; fname << "error_Gemm_device_" << problem_size.m() << "x" << problem_size.n() << "x" << problem_size.k() << "_" << Gemm::ThreadblockShape::kM << "x" << Gemm::ThreadblockShape::kN << "x" << Gemm::ThreadblockShape::kK << "_" << Gemm::WarpShape::kM << "x" << Gemm::WarpShape::kN << "x" << Gemm::WarpShape::kK << ".txt"; std::ofstream file(fname.str()); file << "problem: " << problem_size << ", alpha: " << alpha << ", beta: " << beta << "\n\n"; file << "A =\n" << tensor_A.host_view() << "\nB =\n" << tensor_B.host_view() << "\nB_reordered =\n" << tensor_B_reordered.host_view() << "\nC =\n" << tensor_C.host_view() << "\n\nReference =\n" << reference_D.host_view() << "\nComputed =\n" << tensor_D.host_view(); } return passed; } /// Runs a set of problem sizes bool run_all() { bool passed = true; int problem_size_m[] = { InterleavedK, 256 + InterleavedK, 512 + InterleavedK }; int problem_size_n[] = { InterleavedK, 256 + InterleavedK, 512 + InterleavedK }; int problem_size_k[] = { InterleavedK, 256 + InterleavedK, 512 + InterleavedK }; double problem_alpha[] = { 1.0 }; double problem_beta[] = { 2.0 }; for (int m : problem_size_m) { for (int n : problem_size_n) { for (int k : problem_size_k) { for (double alpha : problem_alpha) { for (double beta : problem_beta) { passed = run( {m, n, k}, ElementCompute(alpha), ElementCompute(beta) ); if (!passed) { return false; } } } } } } return true; } }; //////////////////////////////////////////////////////////////////////////////// } // namespace device } // namespace gemm } // namespace test ////////////////////////////////////////////////////////////////////////////////