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std::vector<float> result_buf(prod(resShape), -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {A, B, Result});
llvm_codegen.call({a_buf, b_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {A, B, Result});
ir_eval.call({a_buf, b_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
}
TEST(ExternalCall, UnaryFloat) {
using TensorFunc = std::function<at::Tensor(at::Tensor)>;
auto toExprHandleVec = [](std::vector<int64_t> v) {
auto intV = std::vector<int>(v.begin(), v.end());
return std::vector<ExprHandle>(intV.begin(), intV.end());
};
using Test = std::tuple<
std::vector<int64_t>,
std::vector<int64_t>,
TensorFunc,
std::string,
std::vector<ExprHandle>>;
std::vector<Test> tests = {};
tests.push_back(Test{// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
{1, 64, 8, 9},
{1, 64, 5, 7},
[](at::Tensor x) {
return at::adaptive_avg_pool2d(x, {5, 7});
},
"nnc_aten_adaptive_avg_pool2d",
toExprHandleVec({5, 7})});
tests.push_back(Test{// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
{100, 200},
{100},
[](at::Tensor x) { return at::mean(x, {1}); },
"nnc_aten_mean",
toExprHandleVec({1, /*keepdim=*/0})});
for (auto curTest : tests) {
std::vector<int64_t> aShape, resShape;
TensorFunc torchFunc;
std::string externCallName;
std::vector<ExprHandle> externCallArgs;
std::tie(aShape, resShape, torchFunc, externCallName, externCallArgs) =
curTest;
BufHandle A("A", toExprHandleVec(aShape), kFloat);
BufHandle ResultBuf("Result", toExprHandleVec(resShape), kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(ResultBuf, externCallName, {A}, externCallArgs));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor a = at::ones(c10::IntArrayRef(aShape), options) * 5.f;
at::Tensor ref = torchFunc(a);
auto prod = [](std::vector<int64_t> v) {
// NOLINTNEXTLINE(modernize-use-transparent-functors)
return std::accumulate(v.begin(), v.end(), 1, std::multiplies<int64_t>());
};
at::Tensor nnc_result;
std::vector<float> a_buf(prod(aShape), 5.f);
std::vector<float> result_buf(prod(resShape), -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {A, Result});
llvm_codegen.call({a_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {A, Result});
ir_eval.call({a_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
}
TEST(ExternalCall, ComputeInterop) {
// This test verifies that Tensors using external calls can be used by and can
// use Tensors built with Compute API.