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Adding sycl backend for TensorPadding.h; disbaling __unit128 for sycl in TensorIntDiv.h; disabling cashsize for sycl in tensorDeviceDefault.h; adding sycl backend for StrideSliceOP ; removing sycl compiler warning for creating an array of size 0 in CXX11Meta.h; cleaning up the sycl backend code.

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This commit is contained in:
Mehdi Goli
2016-12-01 13:02:27 +00:00
parent a70393fd02
commit 79aa2b784e
17 changed files with 506 additions and 81 deletions
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25
unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
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@@ -200,9 +200,6 @@ struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
/// creating the shared memory for calculating reduction.
/// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can
/// recursively apply reduction on it in order to reduce the whole.
// Dims dims= self.xprDims();
//Op functor = reducer;
dev.parallel_for_setup(num_coeffs_to_preserve, tileSize, range, GRange);
dev.sycl_queue().submit([&](cl::sycl::handler &cgh) {
// create a tuple of accessors from Evaluator
@@ -214,28 +211,6 @@ struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
TensorSycl::internal::ReductionFunctor<HostExpr, PlaceHolderExpr, FunctorExpr, Tuple_of_Acc, Dims, Op, typename Self::Index>
(output_accessor, functors, tuple_of_accessors, self.xprDims(), reducer, range));
// [=](cl::sycl::nd_item<1> itemID) {
// typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
// auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
/// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
/// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
/// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
// const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor);
/// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
/// the device_evaluator is detectable and recognisable on the device.
// typedef Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice> DeviceSelf;
// auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
// auto output_accessor_ptr =ConvertToActualTypeSycl(typename DeviceSelf::CoeffReturnType, output_accessor);
/// const cast added as a naive solution to solve the qualifier drop error
// auto globalid=itemID.get_global_linear_id();
// if (globalid< range) {
// typename DeviceSelf::CoeffReturnType accum = functor.initialize();
// GenericDimReducer<DeviceSelf::NumReducedDims-1, DeviceSelf, Op>::reduce(device_self_evaluator, device_self_evaluator.firstInput(static_cast<typename DevExpr::Index>(globalid)),const_cast<Op&>(functor), &accum);
// functor.finalize(accum);
// output_accessor_ptr[globalid]= accum;
// }
// });
});
dev.synchronize();
return false;