forked from jiuyuan/InfiniTensor
90 lines
3.3 KiB
C++
90 lines
3.3 KiB
C++
#include "core/graph.h"
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#include "core/kernel.h"
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#include "core/runtime.h"
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#include "cuda/cuda_runtime.h"
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#include "cuda/cuda_utility.h"
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#include "operators/matmul.h"
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#include "test.h"
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namespace infini {
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using ExpectOutput = vector<float>;
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void testMatmulCuda(
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const std::function<void(void *, size_t, DataType)> &generatorA,
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const std::function<void(void *, size_t, DataType)> &generatorB,
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bool transA, bool transB, const Shape &shapeA, const Shape &shapeB,
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const ExpectOutput &ansVec) {
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auto cpuRuntime = NativeCpuRuntimeObj::getInstance();
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Graph gCpu = make_ref<GraphObj>(cpuRuntime);
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auto ACpu = gCpu->addTensor(shapeA, DataType::Float32);
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auto BCpu = gCpu->addTensor(shapeB, DataType::Float32);
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gCpu->dataMalloc();
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ACpu->setData(generatorA);
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BCpu->setData(generatorB);
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auto cudaRuntime = make_ref<CudaRuntimeObj>();
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auto gCuda = make_ref<GraphObj>(cudaRuntime);
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auto ACuda = gCuda->cloneTensor(ACpu);
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auto BCuda = gCuda->cloneTensor(BCpu);
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auto matmul =
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gCuda->addOp<MatmulObj>(ACuda, BCuda, nullptr, transA, transB);
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// allocate CUDA memory
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gCuda->dataMalloc();
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ACuda->setData(generatorA);
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BCuda->setData(generatorB);
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cudaRuntime->run(gCuda);
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auto CCpu = gCpu->cloneTensor(matmul->getOutput());
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// CCpu->printData();
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// check results on CPU
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EXPECT_TRUE(CCpu->equalData(ansVec));
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// print a tensor/operator/graph by print()
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// gCuda->print();
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}
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TEST(cuBLAS_Matmul, run) {
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testMatmulCuda(IncrementalGenerator(), OneGenerator(), false, false,
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Shape{1, 3, 5}, Shape{1, 5, 2},
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ExpectOutput{10, 10, 35, 35, 60, 60});
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testMatmulCuda(IncrementalGenerator(), IncrementalGenerator(), true, false,
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Shape{2, 3, 4}, Shape{2, 3, 2},
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ExpectOutput{40, 52, 46, 61, 52, 70, 58, 79, 400, 448, 424,
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475, 448, 502, 472, 529});
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testMatmulCuda(
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IncrementalGenerator(), IncrementalGenerator(), false, false,
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Shape{2, 3, 5}, Shape{5, 2},
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ExpectOutput{60, 70, 160, 195, 260, 320, 360, 445, 460, 570, 560, 695});
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testMatmulCuda(IncrementalGenerator(), IncrementalGenerator(), true, false,
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Shape{2, 5, 3}, Shape{5, 2},
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ExpectOutput{180, 210, 200, 235, 220, 260, 480, 585, 500,
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610, 520, 635});
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testMatmulCuda(IncrementalGenerator(), IncrementalGenerator(), false, false,
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Shape{3, 5}, Shape{5, 2},
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ExpectOutput{60, 70, 160, 195, 260, 320});
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}
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TEST(cuBLAS_Matmul, tune) {
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// Matmul([A^T,B,act=0],A=597,B=595,C=598,bmnk=[1,4,4096,448])
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const int B = 1, M = 4, N = 4096, K = 448;
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const bool transA = true, transB = false;
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auto cudaRuntime = make_ref<CudaRuntimeObj>();
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Graph g = make_ref<GraphObj>(cudaRuntime);
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auto a = g->addTensor(transA ? Shape{B, K, M} : Shape{B, M, K});
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auto b = g->addTensor(transB ? Shape{B, N, K} : Shape{B, K, N});
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// allocate CUDA memory
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g->dataMalloc();
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a->setData(IncrementalGenerator());
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b->setData(IncrementalGenerator());
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auto matmul = g->addOp<MatmulObj>(a, b, nullptr, transA, transB);
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matmul->print();
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double time = cudaRuntime->getPerfTime(g);
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EXPECT_GT(time, 1e-3);
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EXPECT_LT(time, 1);
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cudaRuntime->run(g, true);
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}
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}; // namespace infini
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