1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
|
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
/*****************************************************************
* TensorSyclPlaceHolderExpr.h
*
* \brief:
* This is the specialisation of the placeholder expression based on the
* operation type
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
namespace Eigen {
namespace internal {
template<typename OP, typename CoeffReturnType> struct syclGenericBufferReducer{
template<typename BufferTOut, typename BufferTIn>
static void run(OP op, BufferTOut& bufOut, ptrdiff_t out_offset, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){
do {
auto f = [length, local, op, out_offset, &bufOut, &bufI](cl::sycl::handler& h) mutable {
cl::sycl::nd_range<1> r{cl::sycl::range<1>{std::max(length, local)},
cl::sycl::range<1>{std::min(length, local)}};
/* Two accessors are used: one to the buffer that is being reduced,
* and a second to local memory, used to store intermediate data. */
auto aI =bufI.template get_access<cl::sycl::access::mode::read_write>(h);
auto aOut =bufOut.template get_access<cl::sycl::access::mode::write>(h);
typedef decltype(aI) InputAccessor;
typedef decltype(aOut) OutputAccessor;
typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write,cl::sycl::access::target::local> LocalAccessor;
LocalAccessor scratch(cl::sycl::range<1>(local), h);
/* The parallel_for invocation chosen is the variant with an nd_item
* parameter, since the code requires barriers for correctness. */
h.parallel_for(r, TensorSycl::internal::GenericKernelReducer<CoeffReturnType, OP, OutputAccessor, InputAccessor, LocalAccessor>(op, aOut, out_offset, aI, scratch, length, local));
};
dev.sycl_queue().submit(f);
dev.asynchronousExec();
/* At this point, you could queue::wait_and_throw() to ensure that
* errors are caught quickly. However, this would likely impact
* performance negatively. */
length = length / local;
} while (length > 1);
}
};
template<typename CoeffReturnType> struct syclGenericBufferReducer<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType>{
template<typename BufferTOut, typename BufferTIn>
static void run(Eigen::internal::MeanReducer<CoeffReturnType>, BufferTOut& bufOut,ptrdiff_t out_offset, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){
syclGenericBufferReducer<Eigen::internal::SumReducer<CoeffReturnType>, CoeffReturnType>::run(Eigen::internal::SumReducer<CoeffReturnType>(),
bufOut, out_offset, bufI, dev, length, local);
}
};
/// Self is useless here because in expression construction we are going to treat reduction as a leafnode.
/// we want to take reduction child and then build a construction and apply the full reducer function on it. Fullreducre applies the
/// reduction operation on the child of the reduction. once it is done the reduction is an empty shell and can be thrown away and treated as
// a leafNode.
template <typename Self, typename Op, bool Vectorizable>
struct FullReducer<Self, Op, const Eigen::SyclDevice, Vectorizable> {
typedef typename Self::CoeffReturnType CoeffReturnType;
static const bool HasOptimizedImplementation = false;
static void run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output) {
typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
typedef Eigen::TensorSycl::internal::FunctorExtractor<TensorEvaluator<HostExpr, const Eigen::SyclDevice> > FunctorExpr;
FunctorExpr functors = TensorSycl::internal::extractFunctors(self.impl());
int red_factor =256; /// initial reduction. If the size is less than red_factor we only creates one thread.
size_t inputSize =self.impl().dimensions().TotalSize();
size_t rng = inputSize/red_factor; // the total number of thread initially is half the size of the input
size_t remaining = inputSize% red_factor;
if(rng ==0) {
red_factor=1;
};
size_t tileSize =dev.sycl_queue().get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
size_t GRange=std::max((size_t )1, rng);
// convert global range to power of 2 for redecution
GRange--;
GRange |= GRange >> 1;
GRange |= GRange >> 2;
GRange |= GRange >> 4;
GRange |= GRange >> 8;
GRange |= GRange >> 16;
#if __x86_64__ || __ppc64__ || _WIN64
GRange |= GRange >> 32;
#endif
GRange++;
size_t outTileSize = tileSize;
/// if the shared memory is less than the GRange, we set shared_mem size to the TotalSize and in this case one kernel would be created for recursion to reduce all to one.
if (GRange < outTileSize) outTileSize=GRange;
/// 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.
auto temp_global_buffer =cl::sycl::buffer<CoeffReturnType, 1>(cl::sycl::range<1>(GRange));
typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims;
// Dims dims= self.xprDims();
//Op functor = reducer;
dev.sycl_queue().submit([&](cl::sycl::handler &cgh) {
// this is a workaround for gcc 4.8 bug
typedef decltype(TensorSycl::internal::createTupleOfAccessors(cgh, self.impl())) TupleType;
// create a tuple of accessors from Evaluator
TupleType tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
auto tmp_global_accessor = temp_global_buffer. template get_access<cl::sycl::access::mode::read_write, cl::sycl::access::target::global_buffer>(cgh);
typedef decltype(tmp_global_accessor) OutAccessor;
cgh.parallel_for( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(outTileSize)),
TensorSycl::internal::FullReductionKernelFunctor<CoeffReturnType, OutAccessor, HostExpr, FunctorExpr, Op, Dims, size_t, TupleType>
(tmp_global_accessor, rng, remaining, red_factor, reducer, self.xprDims(), functors, tuple_of_accessors));
});
dev.asynchronousExec();
// getting final out buffer at the moment the created buffer is true because there is no need for assign
auto out_buffer =dev.get_sycl_buffer(output);
ptrdiff_t out_offset = dev.get_offset(output);
/// This is used to recursively reduce the tmp value to an element of 1;
syclGenericBufferReducer<Op, CoeffReturnType>::run(reducer, out_buffer, out_offset, temp_global_buffer,dev, GRange, outTileSize);
}
};
template <typename Self, typename Op>
struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
typedef typename Self::CoeffReturnType CoeffReturnType;
static const bool HasOptimizedImplementation = false;
static bool run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output, typename Self::Index num_values_to_reduce, typename Self::Index num_coeffs_to_preserve) {
typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
typedef Eigen::TensorSycl::internal::FunctorExtractor<TensorEvaluator<HostExpr, const Eigen::SyclDevice> > FunctorExpr;
FunctorExpr functors = TensorSycl::internal::extractFunctors(self.impl());
typename Self::Index range, GRange, tileSize;
typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims;
// getting final out buffer at the moment the created buffer is true because there is no need for assign
/// 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.
dev.parallel_for_setup(num_coeffs_to_preserve, tileSize, range, GRange);
dev.sycl_queue().submit([&](cl::sycl::handler &cgh) {
// this is workaround for gcc 4.8 bug.
typedef decltype(TensorSycl::internal::createTupleOfAccessors(cgh, self.impl())) Tuple_of_Acc;
// create a tuple of accessors from Evaluator
Tuple_of_Acc tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
auto output_accessor = dev.template get_sycl_accessor<cl::sycl::access::mode::write>(cgh, output);
ptrdiff_t out_offset = dev.get_offset(output);
Index red_size = (num_values_to_reduce!=0)? num_values_to_reduce : static_cast<Index>(1);
cgh.parallel_for( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)),
TensorSycl::internal::ReductionFunctor<HostExpr, FunctorExpr, Tuple_of_Acc, Dims, Op, typename Self::Index>
(output_accessor, out_offset, functors, tuple_of_accessors, self.xprDims(), reducer, range, red_size));
});
dev.asynchronousExec();
return false;
}
};
} // end namespace internal
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
|
