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Fuse computations into the Tensor contractions using output kernel

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This commit is contained in:
Eugene Zhulenev
2018-07-10 13:16:38 -07:00
parent 5539587b1f
commit 01fd4096d3
6 changed files with 248 additions and 37 deletions
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56
unsupported/test/cxx11_tensor_thread_pool.cpp
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@@ -232,6 +232,60 @@ void test_multithread_contraction_agrees_with_singlethread() {
}
}
// Apply Sqrt to all output elements.
struct SqrtOutputKernel {
template <typename Index, typename Scalar>
EIGEN_ALWAYS_INLINE void operator()(
const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
const TensorContractionParams&, Index, Index, Index num_rows,
Index num_cols) const {
for (int i = 0; i < num_rows; ++i) {
for (int j = 0; j < num_cols; ++j) {
output_mapper(i, j) = std::sqrt(output_mapper(i, j));
}
}
}
};
template <int DataLayout>
static void test_multithread_contraction_with_output_kernel() {
typedef Tensor<float, 1>::DimensionPair DimPair;
const int num_threads = internal::random<int>(2, 11);
ThreadPool threads(num_threads);
Eigen::ThreadPoolDevice device(&threads, num_threads);
Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31);
Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10);
Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10);
t_left.setRandom();
t_right.setRandom();
// Put trash in mat4 to verify contraction clears output memory.
t_result.setRandom();
// Add a little offset so that the results won't be close to zero.
t_left += t_left.constant(1.0f);
t_right += t_right.constant(1.0f);
typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
MapXf m_left(t_left.data(), 1500, 248);
MapXf m_right(t_right.data(), 248, 1400);
Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
// this contraction should be equivalent to a single matrix multiplication
Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
// compute results by separate methods
t_result.device(device) = t_left.contract(t_right, dims, SqrtOutputKernel());
m_result = m_left * m_right;
for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
VERIFY(&t_result.data()[i] != &m_result.data()[i]);
VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
}
}
template<int DataLayout>
void test_full_contraction() {
@@ -355,6 +409,8 @@ void test_cxx11_tensor_thread_pool()
CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<ColMajor>());
CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<RowMajor>());
CALL_SUBTEST_3(test_multithread_contraction_with_output_kernel<ColMajor>());
CALL_SUBTEST_3(test_multithread_contraction_with_output_kernel<RowMajor>());
// Exercise various cases that have been problematic in the past.
CALL_SUBTEST_4(test_contraction_corner_cases<ColMajor>());