forked from jiuyuan/InfiniTensor
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| Author | SHA1 | Date |
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wanghailu
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0fd8c4fbc5 |
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@ -3,8 +3,12 @@
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#include "operators/element_wise.h"
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namespace infini {
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void div_kernel(float *a, float *b, float *c, int num);
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void pow_kernel(float *a, float *b, float *c, int num);
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void div_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
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int b0, int b1, int b2, int b3,
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int c0, int c1, int c2, int c3);
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void pow_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
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int b0, int b1, int b2, int b3,
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int c0, int c1, int c2, int c3);
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void element_wise_kernel(const Operator &_op) {
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auto op = as<ElementWiseObj>(_op);
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@ -12,12 +16,17 @@ void element_wise_kernel(const Operator &_op) {
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float *const bData = (op->getInputs(1)->getRawDataPtr<float *>());
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float *const cData = (op->getOutput()->getRawDataPtr<float *>());
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auto dim = op->getInputs(0)->getDims();
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int n = dim[0], c = dim[1], h = dim[2], w = dim[3];
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auto aDim = op->getInputs(0)->getDims();
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auto bDim = op->getInputs(1)->getDims();
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auto cDim = op->getOutput()->getDims();
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if (op->getOpType() == OpType::Div)
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div_kernel(aData, bData, cData, n * c * h * w);
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div_kernel(aData, bData, cData, aDim[0], aDim[1], aDim[2], aDim[3],
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bDim[0], bDim[1], bDim[2], bDim[3],
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cDim[0], cDim[1], cDim[2], cDim[3]);
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else if (op->getOpType() == OpType::Pow)
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pow_kernel(aData, bData, cData, n * c * h * w);
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pow_kernel(aData, bData, cData, aDim[0], aDim[1], aDim[2], aDim[3],
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bDim[0], bDim[1], bDim[2], bDim[3],
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cDim[0], cDim[1], cDim[2], cDim[3]);
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else
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IT_TODO_HALT();
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}
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@ -5,34 +5,73 @@ constexpr unsigned int num_threads() { return 32 * 4; }
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constexpr int thread_work_size() { return 4; }
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constexpr int block_work_size() { return thread_work_size() * num_threads(); }
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__global__ void _div_kernel(float *x, float *y, float *z, int n) {
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__global__ void _div_kernel(float *x, float *y, float *z, int a0, int a1, int a2, int a3,
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int b0, int b1, int b2, int b3,
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int c0, int c1, int c2, int c3) {
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int index = threadIdx.x + blockIdx.x * blockDim.x;
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int stride = blockDim.x * gridDim.x;
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int n = c0 * c1 * c2 * c3;
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for (int i = index; i < n; i += stride) {
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z[i] = x[i] / y[i];
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int c0_index = i/ (c1 * c2 * c3);
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int c1_index = (i % (c1 * c2 * c3)) / (c2 * c3);
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int c2_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) / c3;
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int c3_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) % c3;
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int a0_index = c0_index % a0;
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int a1_index = c1_index % a1;
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int a2_index = c2_index % a2;
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int a3_index = c3_index % a3;
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int b0_index = c0_index % b0;
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int b1_index = c1_index % b1;
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int b2_index = c2_index % b2;
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int b3_index = c3_index % b3;
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z[i] = x[a0_index*a1*a2*a3 + a1_index*a2*a3 + a2_index*a3 + a3_index] + y[b0_index*b1*b2*b3 + b1_index*b2*b3 + b2_index*b3 + b3_index];
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}
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}
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__global__ void _pow_kernel(float *x, float *y, float *z, int n) {
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__global__ void _pow_kernel(float *x, float *y, float *z, int a0, int a1, int a2, int a3,
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int b0, int b1, int b2, int b3,
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int c0, int c1, int c2, int c3) {
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int index = threadIdx.x + blockIdx.x * blockDim.x;
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int stride = blockDim.x * gridDim.x;
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int n = c0 * c1 * c2 * c3;
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for (int i = index; i < n; i += stride) {
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z[i] = pow(x[i], y[i]);
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int c0_index = i/ (c1 * c2 * c3);
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int c1_index = (i % (c1 * c2 * c3)) / (c2 * c3);
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int c2_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) / c3;
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int c3_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) % c3;
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int a0_index = c0_index % a0;
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int a1_index = c1_index % a1;
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int a2_index = c2_index % a2;
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int a3_index = c3_index % a3;
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int b0_index = c0_index % b0;
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int b1_index = c1_index % b1;
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int b2_index = c2_index % b2;
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int b3_index = c3_index % b3;
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z[i] = pow(x[a0_index*a1*a2*a3 + a1_index*a2*a3 + a2_index*a3 + a3_index], y[b0_index*b1*b2*b3 + b1_index*b2*b3 + b2_index*b3 + b3_index]);
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}
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}
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namespace infini {
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void div_kernel(float *a, float *b, float *c, int num) {
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void div_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
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int b0, int b1, int b2, int b3,
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int c0, int c1, int c2, int c3) {
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int blocksize = block_work_size();
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int gridsize = (num + block_work_size() - 1) / block_work_size();
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_div_kernel<<<blocksize, gridsize>>>(a, b, c, num);
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int gridsize = (c0*c1*c2*c3 + block_work_size() - 1) / block_work_size();
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_div_kernel<<<blocksize, gridsize>>>(a, b, c, a0, a1, a2, a3, b0, b1, b2, b3, c0, c1, c2, c3);
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}
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void pow_kernel(float *a, float *b, float *c, int num) {
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void pow_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
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int b0, int b1, int b2, int b3,
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int c0, int c1, int c2, int c3) {
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int blocksize = block_work_size();
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int gridsize = (num + block_work_size() - 1) / block_work_size();
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_pow_kernel<<<blocksize, gridsize>>>(a, b, c, num);
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int gridsize = (c0*c1*c2*c3 + block_work_size() - 1) / block_work_size();
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_pow_kernel<<<blocksize, gridsize>>>(a, b, c, a0, a1, a2, a3, b0, b1, b2, b3, c0, c1, c2, c3);
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}
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}; // namespace infini
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@ -12,12 +12,10 @@ ElementWiseObj::inferShape(const TensorVec &inputs) const {
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// For now,we only process the same dims here, broardcast will be considered
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// in the opt layer.
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const auto A = inputs[0], B = inputs[1];
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if (A->getDims().size() != B->getDims().size() ||
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A->getDims() != B->getDims())
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if (A->getDims().size() != B->getDims().size())
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return {};
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return {{A->getDims()}};
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/*
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//return {{A->getDims()}};
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int n = A->getDims().size();
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Shape shape;
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for (int i = 0; i < n; i++) {
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@ -28,7 +26,7 @@ ElementWiseObj::inferShape(const TensorVec &inputs) const {
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auto dimI = dimA > dimB ? dimA : dimB;
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shape.emplace_back(dimI);
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}
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return {{shape}};*/
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return {{shape}};
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}
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std::string ElementWiseObj::toString() const {
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@ -14,69 +14,25 @@ TEST(ElementWise, ShapeInference) {
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Runtime runtime = CpuRuntimeObj::getInstance();
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{
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Graph g = make_ref<GraphObj>(runtime);
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Tensor i0 = g->addTensor({2, 3, 3, 4}, DataType::UInt32);
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Tensor i1 = g->addTensor({2, 3, 3, 4}, DataType::UInt32);
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Tensor i0 = g->addTensor({1, 4, 1, 6}, DataType::UInt32);
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Tensor i1 = g->addTensor({3, 1, 5, 1}, DataType::UInt32);
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auto op = g->addOp<AddObj>(i0, i1, nullptr);
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EXPECT_EQ(op->getOutput()->getDims(), (Shape{2, 3, 3, 4}));
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EXPECT_EQ(op->getOutput()->getDims(), (Shape{3, 4, 5, 6}));
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}
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}
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/*
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template <typename T>
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void test_element_wise(
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const std::function<void(void *, size_t, DataType)> &generator,
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const vector<uint32_t> &ans) {
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Runtime runtime = CpuRuntimeObj::getInstance();
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Graph g = make_ref<GraphObj>(runtime);
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Tensor i0 = g->addTensor({1, 3, 2, 2}, DataType::UInt32);
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Tensor i1 = g->addTensor({2, 3, 1, 2}, DataType::UInt32);
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auto op = g->addOp<T>(i0, i1, nullptr);
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g->dataMalloc();
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i0->setData(generator);
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i1->setData(generator);
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runtime->run(g, true, true);
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// check answer
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EXPECT_TRUE(op->getOutput()->equalData(ans));
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}
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TEST(ElementWise, NaiveCPU) {
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test_element_wise<AddObj>(IncrementalGenerator(),
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vector<uint32_t>{0, 2, 2, 4, 6, 8, 8, 10,
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12, 14, 14, 16, 6, 8, 8, 10,
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12, 14, 14, 16, 18, 20, 20, 22});
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test_element_wise<SubObj>(
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IncrementalGenerator(),
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vector<uint32_t>{0, 0, 2, 2,
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2, 2, 4, 4,
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4, 4, 6, 6,
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4294967290, 4294967290, 4294967292, 4294967292,
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4294967292, 4294967292, 4294967294, 4294967294,
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4294967294, 4294967294, 0, 0});
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test_element_wise<MulObj>(
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IncrementalGenerator(),
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vector<uint32_t>{0, 1, 0, 3, 8, 15, 12, 21, 32, 45, 40, 55,
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0, 7, 12, 21, 32, 45, 48, 63, 80, 99, 100, 121});
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test_element_wise<DivObj>(OneGenerator(),
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vector<uint32_t>{
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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});
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}
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*/
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template <class T>
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void testElementWiseCudnn(
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const std::function<void(void *, size_t, DataType)> &generator,
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const Shape &shape, const ExpectOutput &ansVec) {
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const std::function<void(void *, size_t, DataType)> &generator) {
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Runtime cpuRuntime = CpuRuntimeObj::getInstance();
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auto cudaRuntime = make_ref<CudaRuntimeObj>();
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// Build input data on CPU
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Tensor acpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
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Tensor acpu = make_ref<TensorObj>(Shape{1, 1, 1, 4}, DataType::Float32, cpuRuntime);
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acpu->dataMalloc();
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acpu->setData(generator);
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Tensor bcpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
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Tensor bcpu = make_ref<TensorObj>(Shape{1, 1, 3, 1}, DataType::Float32, cpuRuntime);
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bcpu->dataMalloc();
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bcpu->setData(generator);
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@ -85,6 +41,7 @@ void testElementWiseCudnn(
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auto a = g->cloneTensor(acpu);
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auto b = g->cloneTensor(bcpu);
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auto op = g->addOp<T>(a, b, nullptr);
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auto bop = g->addOp<DivObj>(a, b, nullptr);
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// allocate CUDA memory
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g->dataMalloc();
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@ -94,29 +51,20 @@ void testElementWiseCudnn(
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// clone CUDA output to CPU
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auto c = op->getOutput();
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auto bcast = bop->getOutput();
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auto ccpu = c->clone(cpuRuntime);
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acpu->printData();
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bcpu->printData();
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ccpu->printData();
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auto bcastcpu = bcast->clone(cpuRuntime);
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bcastcpu->printData();
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// cudaPrintTensor(c);
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// check results on CPU
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EXPECT_TRUE(ccpu->equalData(ansVec));
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EXPECT_TRUE(ccpu->equalData(bcastcpu));
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}
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TEST(ElementWise, CuDNN) {
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testElementWiseCudnn<AddObj>(
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IncrementalGenerator(), Shape{1, 2, 2, 3},
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ExpectOutput{0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22});
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testElementWiseCudnn<SubObj>(
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IncrementalGenerator(), Shape{1, 2, 2, 3},
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ExpectOutput{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
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testElementWiseCudnn<MulObj>(
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IncrementalGenerator(), Shape{1, 2, 2, 3},
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ExpectOutput{0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121});
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testElementWiseCudnn<DivObj>(
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OneGenerator(), Shape{1, 2, 2, 3},
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ExpectOutput{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
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testElementWiseCudnn<PowObj>(IncrementalGenerator(), Shape{1, 2, 2, 1},
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ExpectOutput{1, 1, 4, 27});
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testElementWiseCudnn<AddObj>(IncrementalGenerator());
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}
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} // namespace infini
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