forked from jiuyuan/InfiniTensor
172 lines
6.9 KiB
C++
172 lines
6.9 KiB
C++
#include "core/graph.h"
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#include "core/kernel.h"
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#include "core/perf_engine.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 "test.h"
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namespace infini {
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void testConvTransposedCudnn(
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const std::function<void(void *, size_t, DataType)> &generator,
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vector<float> ansVec) {
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const auto &[N, C, H, W, F, R, S] = tuple{1, 1, 2, 2, 1, 4, 4};
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const int stride = 1, padding = 0, dilation = 1;
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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({N, F, H, H}, DataType::Float32);
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Tensor w0Cpu = gCpu->addTensor({F, C, R, S}, DataType::Float32);
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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 = gCuda->addOp<ConvTransposed2dObj>(i0Cuda, w0Cuda, nullptr,
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padding, padding, stride,
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stride, dilation, dilation);
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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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}
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void testConvTransposedNHWCCudnn(
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const std::function<void(void *, size_t, DataType)> &generator,
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vector<float> ansVec) {
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const auto &[N, C, H, W, F, R, S] = tuple{1, 1, 2, 2, 2, 4, 4};
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const int stride = 1, padding = 0, dilation = 1;
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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({N, H, W, F}, DataType::Float32);
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Tensor w0Cpu = gCpu->addTensor({F, R, S, C}, DataType::Float32);
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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 = gCuda->addOp<ConvTransposed2dNHWCObj>(
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i0Cuda, w0Cuda, nullptr, padding, padding, stride, stride, dilation,
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dilation);
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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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}
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TEST(cuDNN_ConvTransposed, run) {
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testConvTransposedCudnn(IncrementalGenerator(),
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vector<float>{0., 0., 1., 2., 3., 0., 6.,
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12., 18., 16., 8., 30., 36., 42.,
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32., 16., 54., 60., 66., 48., 24.,
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62., 67., 72., 45.});
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}
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TEST(cuDNN_ConvTransposedNHWC, run) {
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testConvTransposedNHWCCudnn(IncrementalGenerator(),
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vector<float>{16, 65, 71, 77, 63, 100, 290,
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318, 346, 234, 140, 402, 430, 458,
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306, 180, 514, 542, 570, 378, 188,
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465, 487, 509, 307});
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}
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TEST(cuDNN_ConvTransposed, run1) {
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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, 2, 3, 3}, DataType::Float32);
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Tensor w0Cpu = gCpu->addTensor({2, 2, 3, 3}, DataType::Float32);
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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<ConvTransposed2dObj>(i0Cuda, w0Cuda, nullptr, 0, 0);
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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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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(vector<float>{
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162, 351, 569, 413, 224, 405, 876, 1417, 1024, 553,
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747, 1611, 2598, 1869, 1005, 639, 1368, 2191, 1564, 835,
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396, 843, 1343, 953, 506, 243, 531, 866, 629, 341,
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621, 1344, 2173, 1564, 841, 1152, 2475, 3975, 2841, 1518,
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963, 2052, 3271, 2320, 1231, 585, 1239, 1964, 1385, 731}));
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}
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TEST(cuDNN_ConvTransposed, 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, 448, 2, 2}, DataType::Float32);
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Tensor w0Cpu = gCpu->addTensor({448, 256, 4, 4}, DataType::Float32);
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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 = gCuda->addOp<ConvTransposed2dObj>(i0Cuda, w0Cuda, nullptr);
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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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// check record
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auto kernelAttrs = KernelAttrs{Device::CUDA, conv->getOpType().underlying(),
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DataType::Float32};
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auto perfKey = PerfEngine::Key{kernelAttrs, conv->getOpPerfKey()};
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std::optional<PerfRecord> perfData =
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PerfEngine::getInstance().getPerfData(perfKey);
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ASSERT_TRUE(perfData.has_value());
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}
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} // namespace infini
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