forked from jiuyuan/InfiniTensor
83 lines
2.8 KiB
C++
83 lines
2.8 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/conv.h"
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#include <bitset>
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#include "test.h"
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namespace infini {
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void testConvCudnnFP16(
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const std::function<void(void *, size_t, DataType)> &generator,
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vector<float> ansVec) {
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// Construct Runtime and graph for CPU and CUDA
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Runtime cpu = NativeCpuRuntimeObj::getInstance(); // CPUruntime is singleton
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Graph gCpu = make_ref<GraphObj>(cpu);
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Runtime cuda = make_ref<CudaRuntimeObj>();
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Graph gCuda = make_ref<GraphObj>(cuda);
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// Set input data on CPU in a CPU Graph
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Tensor i0Cpu = gCpu->addTensor({1, 3, 4, 4}, DataType::Float16);
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Tensor w0Cpu = gCpu->addTensor({2, 3, 3, 3}, DataType::Float16);
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// Malloc data for all tensors in a graph. Do we need implicit allocation?
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gCpu->dataMalloc();
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i0Cpu->setData(generator);
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w0Cpu->setData(generator);
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// Copy input tensors from CPU to CUDA
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Tensor i0Cuda = gCuda->cloneTensor(i0Cpu);
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Tensor w0Cuda = gCuda->cloneTensor(w0Cpu);
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// Build CUDA graph
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auto conv =
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gCuda->addOp<ConvObj>(i0Cuda, w0Cuda, nullptr, 1, 1, 2, 1, 1, 2);
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// allocate CUDA memory
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gCuda->dataMalloc();
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i0Cuda->setData(generator);
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w0Cuda->setData(generator);
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// Execute on CUDA
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cuda->run(gCuda);
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// copy output from CUDA to CPU
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auto o0Cpu = gCpu->cloneTensor(conv->getOutput());
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// check results on CPU
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EXPECT_TRUE(o0Cpu->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(cuDNN_Conv_FP16, run) {
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testConvCudnnFP16(IncrementalGenerator(),
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vector<float>{48, 48, 72, 72, 48, 48, 72, 72});
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}
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TEST(cuDNN_Conv_FP16, tune) {
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Runtime cpu = NativeCpuRuntimeObj::getInstance(); // CPUruntime is singleton
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Graph gCpu = make_ref<GraphObj>(cpu);
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Runtime cuda = make_ref<CudaRuntimeObj>();
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Graph gCuda = make_ref<GraphObj>(cuda);
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// Set input data on CPU in a CPU Graph
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Tensor i0Cpu = gCpu->addTensor({1, 3, 224, 224}, DataType::Float16);
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Tensor w0Cpu = gCpu->addTensor({2, 3, 3, 3}, DataType::Float16);
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// Malloc data for all tensors in a graph. Do we need implicit allocation?
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gCpu->dataMalloc();
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i0Cpu->setData(IncrementalGenerator());
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w0Cpu->setData(IncrementalGenerator());
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// Copy input tensors from CPU to CUDA
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Tensor i0Cuda = gCuda->cloneTensor(i0Cpu);
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Tensor w0Cuda = gCuda->cloneTensor(w0Cpu);
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// Build CUDA graph
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auto conv =
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gCuda->addOp<ConvObj>(i0Cuda, w0Cuda, nullptr, 1, 1, 1, 1, 1, 1);
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// allocate CUDA memory
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gCuda->dataMalloc();
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i0Cuda->setData(IncrementalGenerator());
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w0Cuda->setData(IncrementalGenerator());
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// Execute on CUDA
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bool tune = true;
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cuda->run(gCuda, tune);
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}
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} // namespace infini
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