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Add support for asynchronous evaluation of tensor casting expressions.

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This commit is contained in:
Rasmus Munk Larsen 2019-09-19 13:54:49 -07:00
parent 28b6786498
commit 1d5af0693c
2 changed files with 37 additions and 6 deletions
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31
unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
View File

@ -178,6 +178,27 @@ template <typename Eval, typename EvalPointerType> struct ConversionSubExprEval<
}
};
#ifdef EIGEN_USE_THREADS
template <bool SameType, typename Eval, typename EvalPointerType,
typename EvalSubExprsCallback>
struct ConversionSubExprEvalAsync {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(
Eval& impl, EvalPointerType, EvalSubExprsCallback done) {
impl.evalSubExprsIfNeededAsync(nullptr, std::move(done));
}
};
template <typename Eval, typename EvalPointerType,
typename EvalSubExprsCallback>
struct ConversionSubExprEvalAsync<true, Eval, EvalPointerType,
EvalSubExprsCallback> {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(
Eval& impl, EvalPointerType data, EvalSubExprsCallback done) {
impl.evalSubExprsIfNeededAsync(data, std::move(done));
}
};
#endif
namespace internal {
template <typename SrcType, typename TargetType, bool IsSameT>
@ -299,6 +320,16 @@ struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device>
return ConversionSubExprEval<IsSameType, TensorEvaluator<ArgType, Device>, EvaluatorPointerType>::run(m_impl, data);
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType data, EvalSubExprsCallback done) {
ConversionSubExprEvalAsync<IsSameType, TensorEvaluator<ArgType, Device>,
EvaluatorPointerType,
EvalSubExprsCallback>::run(m_impl, data, std::move(done));
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
{
m_impl.cleanup();

12
unsupported/test/cxx11_tensor_thread_pool.cpp
View File

@ -40,19 +40,19 @@ void test_multithread_elementwise()
{
Tensor<float, 3> in1(200, 30, 70);
Tensor<float, 3> in2(200, 30, 70);
Tensor<float, 3> out(200, 30, 70);
Tensor<double, 3> out(200, 30, 70);
in1.setRandom();
in2.setRandom();
Eigen::ThreadPool tp(internal::random<int>(3, 11));
Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
out.device(thread_pool_device) = in1 + in2 * 3.14f;
out.device(thread_pool_device) = (in1 + in2 * 3.14f).cast<double>();
for (int i = 0; i < 200; ++i) {
for (int j = 0; j < 30; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f);
VERIFY_IS_APPROX(out(i, j, k), static_cast<double>(in1(i, j, k) + in2(i, j, k) * 3.14f));
}
}
}
@ -62,7 +62,7 @@ void test_async_multithread_elementwise()
{
Tensor<float, 3> in1(200, 30, 70);
Tensor<float, 3> in2(200, 30, 70);
Tensor<float, 3> out(200, 30, 70);
Tensor<double, 3> out(200, 30, 70);
in1.setRandom();
in2.setRandom();
@ -71,13 +71,13 @@ void test_async_multithread_elementwise()
Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
Eigen::Barrier b(1);
out.device(thread_pool_device, [&b]() { b.Notify(); }) = in1 + in2 * 3.14f;
out.device(thread_pool_device, [&b]() { b.Notify(); }) = (in1 + in2 * 3.14f).cast<double>();
b.Wait();
for (int i = 0; i < 200; ++i) {
for (int j = 0; j < 30; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f);
VERIFY_IS_APPROX(out(i, j, k), static_cast<double>(in1(i, j, k) + in2(i, j, k) * 3.14f));
}
}
}