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update cuda_conv_fp16

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zhangyue207 2023-07-27 09:28:27 +08:00
parent 1b51bc54bb
commit 0f63e1bb0f
1 changed files with 90 additions and 0 deletions
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test/kernels/cuda/test_cuda_conv_fp16.cc Normal file
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#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "cuda/cuda_runtime.h"
#include "cuda/cuda_utility.h"
#include "operators/conv.h"
#include <bitset>
#include "test.h"
namespace infini {
void testConvCudnnFP16(
const std::function<void(void *, size_t, DataType)> &generator,
vector<uint16_t> ansVec) {
union {
float f32;
uint32_t u32;
} x;
x.u32 = 1234;
x.u32 |= 127 << 23;
std::cout << x.f32 << std::endl;
// Construct Runtime and graph for CPU and CUDA
Runtime cpu = NativeCpuRuntimeObj::getInstance(); // CPUruntime is singleton
Graph gCpu = make_ref<GraphObj>(cpu);
Runtime cuda = make_ref<CudaRuntimeObj>();
Graph gCuda = make_ref<GraphObj>(cuda);
// Set input data on CPU in a CPU Graph
Tensor i0Cpu = gCpu->addTensor({1, 3, 4, 4}, DataType::Float16);
Tensor w0Cpu = gCpu->addTensor({2, 3, 3, 3}, DataType::Float16);
// Malloc data for all tensors in a graph. Do we need implicit allocation?
gCpu->dataMalloc();
i0Cpu->setData(generator);
w0Cpu->setData(generator);
// Copy input tensors from CPU to CUDA
Tensor i0Cuda = gCuda->cloneTensor(i0Cpu);
Tensor w0Cuda = gCuda->cloneTensor(w0Cpu);
// Build CUDA graph
auto conv =
gCuda->addOp<ConvObj>(i0Cuda, w0Cuda, nullptr, 1, 1, 2, 1, 1, 2);
// allocate CUDA memory
gCuda->dataMalloc();
// Execute on CUDA
cuda->run(gCuda);
// copy output from CUDA to CPU
auto o0Cpu = gCpu->cloneTensor(conv->getOutput());
// check results on CPU
o0Cpu->printData();
EXPECT_TRUE(o0Cpu->equalData(ansVec));
// print a tensor/operator/graph by print()
gCuda->print();
}
TEST(cuDNN_Conv_FP16, run) {
// testConvCudnnFP16(OneGenerator(),
// vector<float>{12, 12, 18, 18, 12, 12, 18, 18});
testConvCudnnFP16(
IncrementalGenerator(),
vector<uint16_t>{4794, 4386, 8199, 7506, 11274, 10542, 20835, 19656});
}
TEST(cuDNN_Conv_FP16, tune) {
Runtime cpu = NativeCpuRuntimeObj::getInstance(); // CPUruntime is singleton
Graph gCpu = make_ref<GraphObj>(cpu);
Runtime cuda = make_ref<CudaRuntimeObj>();
Graph gCuda = make_ref<GraphObj>(cuda);
// Set input data on CPU in a CPU Graph
Tensor i0Cpu = gCpu->addTensor({1, 3, 224, 224}, DataType::Float16);
Tensor w0Cpu = gCpu->addTensor({2, 3, 3, 3}, DataType::Float16);
// Malloc data for all tensors in a graph. Do we need implicit allocation?
gCpu->dataMalloc();
i0Cpu->setData(IncrementalGenerator());
w0Cpu->setData(IncrementalGenerator());
// Copy input tensors from CPU to CUDA
Tensor i0Cuda = gCuda->cloneTensor(i0Cpu);
Tensor w0Cuda = gCuda->cloneTensor(w0Cpu);
// Build CUDA graph
auto conv =
gCuda->addOp<ConvObj>(i0Cuda, w0Cuda, nullptr, 1, 1, 1, 1, 1, 1);
// allocate CUDA memory
gCuda->dataMalloc();
// Execute on CUDA
bool tune = true;
cuda->run(gCuda, tune);
}
} // namespace infini