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test_ops.cpp
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test_ops.cpp
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#include <gtest/gtest.h>
#include <torch/csrc/jit/tensorexpr/eval.h>
#include <torch/csrc/jit/tensorexpr/loopnest.h>
#include <torch/csrc/jit/tensorexpr/operators/operators.h>
#include <torch/torch.h>
using namespace torch::jit::tensorexpr;
using Tensors = std::vector<Tensor>;
using Args = std::vector<CodeGen::BufferArg>;
std::unique_ptr<SimpleIREvaluator> compile(
const Args& inputs,
const Tensors& outputs) {
LoopNest nest({outputs});
nest.prepareForCodegen();
nest.simplify();
auto join = inputs;
join.insert(join.end(), outputs.begin(), outputs.end());
return std::make_unique<SimpleIREvaluator>(nest.root_stmt(), join);
}
TEST(Ops, Sum) {
constexpr int M = 8;
constexpr int N = 16;
std::vector<IntList> testDims = {{0}, {1}, {0, 1}};
std::vector<std::vector<ExprHandle>> outputShapes = {{N}, {M}, {}};
for (int idx = 0; idx < testDims.size(); idx++) {
const auto& dims = testDims[idx];
const auto& outShape = outputShapes[idx];
BufHandle a("a", {M, N}, kFloat);
Tensor b = computeSum({a, dims, false}, outShape, c10::kFloat, at::kCPU);
auto cg = compile({a}, {b});
auto at = at::arange(M * N, at::kFloat).view({M, N});
auto ref = at::sum(at, dims);
auto bt = at::empty_like(ref);
cg->call({at.data_ptr<float>(), bt.data_ptr<float>()});
ASSERT_TRUE(at::allclose(bt, ref));
}
}