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This commit is contained in:
Gael Guennebaud 2016-02-23 11:09:05 +01:00
commit 91e1375ba9
13 changed files with 269 additions and 342 deletions
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26
Eigen/src/Core/arch/CUDA/PacketMathHalf.h
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@ -52,14 +52,19 @@ __device__ half operator /= (half& a, const half& b) {
a = a / b; a = a / b;
return a; return a;
} }
__device__ half __shfl_xor(half a, int) {
assert(false && "tbd"); namespace std {
return a; __device__ half abs(const half& a) {
half result;
result.x = a.x & 0x7FFF;
return result;
}
} }
namespace Eigen { namespace Eigen {
namespace internal { namespace internal {
template<> struct is_arithmetic<half> { enum { value = true }; };
template<> struct is_arithmetic<half2> { enum { value = true }; }; template<> struct is_arithmetic<half2> { enum { value = true }; };
template<> struct packet_traits<half> : default_packet_traits template<> struct packet_traits<half> : default_packet_traits
@ -214,17 +219,20 @@ template<> EIGEN_DEVICE_FUNC inline half predux_mul<half2>(const half2& a) {
} }
template<> EIGEN_DEVICE_FUNC inline half2 pabs<half2>(const half2& a) { template<> EIGEN_DEVICE_FUNC inline half2 pabs<half2>(const half2& a) {
assert(false && "tbd"); half2 result;
return half2(); result.x = a.x & 0x7FFF7FFF;
return result;
} }
EIGEN_DEVICE_FUNC inline void EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<half2,2>& kernel) { ptranspose(PacketBlock<half2,2>& kernel) {
assert(false && "tbd"); half a1 = __low2half(kernel.packet[0]);
// half tmp = kernel.packet[0].y; half a2 = __high2half(kernel.packet[0]);
// kernel.packet[0].y = kernel.packet[1].x; half b1 = __low2half(kernel.packet[1]);
// kernel.packet[1].x = tmp; half b2 = __high2half(kernel.packet[1]);
kernel.packet[0] = __halves2half2(a1, b1);
kernel.packet[1] = __halves2half2(a2, b2);
} }
} // end namespace internal } // end namespace internal

226
bench/tensors/tensor_benchmarks.h
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@ -15,7 +15,7 @@ using Eigen::TensorMap;
// TODO(bsteiner): also templatize on the input type since we have users // TODO(bsteiner): also templatize on the input type since we have users
// for int8 as well as floats. // for int8 as well as floats.
template <typename Device> class BenchmarkSuite { template <typename Device, typename T> class BenchmarkSuite {
public: public:
BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n) BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n)
: m_(m), k_(k), n_(n), device_(device) { : m_(m), k_(k), n_(n), device_(device) {
@ -37,7 +37,7 @@ template <typename Device> class BenchmarkSuite {
eigen_assert(m_ == k_ && k_ == n_); eigen_assert(m_ == k_ && k_ == n_);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
device_.memcpy(c_, a_, m_ * m_ * sizeof(float)); device_.memcpy(c_, a_, m_ * m_ * sizeof(T));
} }
// Record the number of values copied per second // Record the number of values copied per second
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters); finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
@ -45,13 +45,15 @@ template <typename Device> class BenchmarkSuite {
void typeCasting(int num_iters) { void typeCasting(int num_iters) {
eigen_assert(m_ == n_); eigen_assert(m_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, k_}}; Eigen::array<TensorIndex, 2> sizes;
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> A(a_, sizes); sizes[0] = m_;
sizes[1] = k_;
const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> A(a_, sizes);
TensorMap<Tensor<int, 2, 0, TensorIndex>, Eigen::Aligned> B((int*)b_, sizes); TensorMap<Tensor<int, 2, 0, TensorIndex>, Eigen::Aligned> B((int*)b_, sizes);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
B.device(device_) = A.cast<int>(); B.device(device_) = A.template cast<int>();
} }
// Record the number of values copied per second // Record the number of values copied per second
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters); finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
@ -59,8 +61,10 @@ template <typename Device> class BenchmarkSuite {
void random(int num_iters) { void random(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_); eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}}; Eigen::array<TensorIndex, 2> sizes;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes); sizes[0] = m_;
sizes[1] = m_;
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -72,10 +76,12 @@ template <typename Device> class BenchmarkSuite {
void slicing(int num_iters) { void slicing(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_); eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}}; Eigen::array<TensorIndex, 2> sizes;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes); sizes[0] = m_;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes); sizes[1] = m_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2); const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2);
const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0); const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0);
@ -100,10 +106,13 @@ template <typename Device> class BenchmarkSuite {
} }
void rowChip(int num_iters) { void rowChip(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}}; Eigen::array<TensorIndex, 2> input_size;
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size); input_size[0] = k_;
const Eigen::array<TensorIndex, 1> output_size = {{n_}}; input_size[1] = n_;
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size); const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
Eigen::array<TensorIndex, 1> output_size;
output_size[0] = n_;
TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -114,10 +123,13 @@ template <typename Device> class BenchmarkSuite {
} }
void colChip(int num_iters) { void colChip(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size= {{k_, n_}}; Eigen::array<TensorIndex, 2> input_size;
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size); input_size[0] = k_;
const Eigen::array<TensorIndex, 1> output_size = {{n_}}; input_size[1] = n_;
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size); const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
Eigen::array<TensorIndex, 1> output_size;
output_size[0] = n_;
TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -129,12 +141,18 @@ template <typename Device> class BenchmarkSuite {
void shuffling(int num_iters) { void shuffling(int num_iters) {
eigen_assert(m_ == n_); eigen_assert(m_ == n_);
const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}}; Eigen::array<TensorIndex, 2> size_a;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a); size_a[0] = m_;
const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}}; size_a[1] = k_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
Eigen::array<TensorIndex, 2> size_b;
size_b[0] = k_;
size_b[1] = m_;
TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
const Eigen::array<int, 2> shuffle = {{1, 0}}; Eigen::array<int, 2> shuffle;
shuffle[0] = 1;
shuffle[1] = 0;
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -146,10 +164,14 @@ template <typename Device> class BenchmarkSuite {
void padding(int num_iters) { void padding(int num_iters) {
eigen_assert(m_ == k_); eigen_assert(m_ == k_);
const Eigen::array<TensorIndex, 2> size_a = {{m_, k_-3}}; Eigen::array<TensorIndex, 2> size_a;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a); size_a[0] = m_;
const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}}; size_a[1] = k_-3;
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
Eigen::array<TensorIndex, 2> size_b;
size_b[0] = k_;
size_b[1] = m_;
TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings; Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0); paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
@ -165,12 +187,18 @@ template <typename Device> class BenchmarkSuite {
void striding(int num_iters) { void striding(int num_iters) {
eigen_assert(m_ == k_); eigen_assert(m_ == k_);
const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}}; Eigen::array<TensorIndex, 2> size_a;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a); size_a[0] = m_;
const Eigen::array<TensorIndex, 2> size_b = {{m_, k_ / 2}}; size_a[1] = k_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
Eigen::array<TensorIndex, 2> size_b;
size_b[0] = m_;
size_b[1] = k_/2;
TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, size_b);
const Eigen::array<TensorIndex, 2> strides = {{1, 2}}; Eigen::array<TensorIndex, 2> strides;
strides[0] = 1;
strides[1] = 2;
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -181,13 +209,19 @@ template <typename Device> class BenchmarkSuite {
} }
void broadcasting(int num_iters) { void broadcasting(int num_iters) {
const Eigen::array<TensorIndex, 2> size_a = {{m_, 1}}; Eigen::array<TensorIndex, 2> size_a;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a); size_a[0] = m_;
const Eigen::array<TensorIndex, 2> size_c = {{m_, n_}}; size_a[1] = 1;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, size_a);
Eigen::array<TensorIndex, 2> size_c;
size_c[0] = m_;
size_c[1] = n_;
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, size_c);
#ifndef EIGEN_HAS_INDEX_LIST #ifndef EIGEN_HAS_INDEX_LIST
const Eigen::array<int, 2> broadcast = {{1, n_}}; Eigen::array<int, 2> broadcast;
broadcast[0] = 1;
broadcast[1] = n_;
#else #else
// Take advantage of cxx11 to give the compiler information it can use to // Take advantage of cxx11 to give the compiler information it can use to
// optimize the code. // optimize the code.
@ -205,10 +239,12 @@ template <typename Device> class BenchmarkSuite {
void coeffWiseOp(int num_iters) { void coeffWiseOp(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_); eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}}; Eigen::array<TensorIndex, 2> sizes;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes); sizes[0] = m_;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes); sizes[1] = m_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -221,10 +257,12 @@ template <typename Device> class BenchmarkSuite {
void algebraicFunc(int num_iters) { void algebraicFunc(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_); eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}}; Eigen::array<TensorIndex, 2> sizes;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes); sizes[0] = m_;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes); sizes[1] = m_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -237,10 +275,12 @@ template <typename Device> class BenchmarkSuite {
void transcendentalFunc(int num_iters) { void transcendentalFunc(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_); eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}}; Eigen::array<TensorIndex, 2> sizes;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes); sizes[0] = m_;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes); sizes[1] = m_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizes);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -253,13 +293,16 @@ template <typename Device> class BenchmarkSuite {
// Row reduction // Row reduction
void rowReduction(int num_iters) { void rowReduction(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}}; Eigen::array<TensorIndex, 2> input_size;
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size); input_size[0] = k_;
input_size[1] = n_;
const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
const Eigen::array<TensorIndex, 1> output_size = {{n_}}; const Eigen::array<TensorIndex, 1> output_size = {{n_}};
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size); TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
#ifndef EIGEN_HAS_INDEX_LIST #ifndef EIGEN_HAS_INDEX_LIST
const Eigen::array<TensorIndex, 1> sum_along_dim = {{0}}; Eigen::array<TensorIndex, 1> sum_along_dim;
sum_along_dim[0] = 0;
#else #else
// Take advantage of cxx11 to give the compiler information it can use to // Take advantage of cxx11 to give the compiler information it can use to
// optimize the code. // optimize the code.
@ -277,15 +320,18 @@ template <typename Device> class BenchmarkSuite {
// Column reduction // Column reduction
void colReduction(int num_iters) { void colReduction(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}}; Eigen::array<TensorIndex, 2> input_size;
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B( input_size[0] = k_;
input_size[1] = n_;
const TensorMap<Tensor<T, 2, 0, TensorIndex>, Eigen::Aligned> B(
b_, input_size); b_, input_size);
const Eigen::array<TensorIndex, 1> output_size = {{k_}}; const Eigen::array<TensorIndex, 1> output_size = {{k_}};
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C( TensorMap<Tensor<T, 1, 0, TensorIndex>, Eigen::Aligned> C(
c_, output_size); c_, output_size);
#ifndef EIGEN_HAS_INDEX_LIST #ifndef EIGEN_HAS_INDEX_LIST
const Eigen::array<TensorIndex, 1> sum_along_dim = {{1}}; Eigen::array<TensorIndex, 1> sum_along_dim;
sum_along_dim = 1;
#else #else
// Take advantage of cxx11 to give the compiler information it can use to // Take advantage of cxx11 to give the compiler information it can use to
// optimize the code. // optimize the code.
@ -303,16 +349,23 @@ template <typename Device> class BenchmarkSuite {
// do a contraction which is equivalent to a matrix multiplication // do a contraction which is equivalent to a matrix multiplication
void contraction(int num_iters) { void contraction(int num_iters) {
const Eigen::array<TensorIndex, 2> sizeA = {{m_, k_}}; Eigen::array<TensorIndex, 2> sizeA;
const Eigen::array<TensorIndex, 2> sizeB = {{k_, n_}}; sizeA[0] = m_;
const Eigen::array<TensorIndex, 2> sizeC = {{m_, n_}}; sizeA[1] = k_;
Eigen::array<TensorIndex, 2> sizeB;
sizeB[0] = k_;
sizeB[1] = n_;
Eigen::array<TensorIndex, 2> sizeC;
sizeC[0] = m_;
sizeC[1] = n_;
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA); const TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, sizeA);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB); const TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, sizeB);
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC); TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, sizeC);
typedef typename Tensor<float, 2>::DimensionPair DimPair; typedef typename Tensor<T, 2>::DimensionPair DimPair;
const Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; Eigen::array<DimPair, 1> dims;
dims[0] = DimPair(1, 0);
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -324,14 +377,21 @@ template <typename Device> class BenchmarkSuite {
} }
void convolution(int num_iters, int kernel_x, int kernel_y) { void convolution(int num_iters, int kernel_x, int kernel_y) {
const Eigen::array<TensorIndex, 2> input_sizes = {{m_, n_}}; Eigen::array<TensorIndex, 2> input_sizes;
TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes); input_sizes[0] = m_;
const Eigen::array<TensorIndex, 2> kernel_sizes = {{kernel_x, kernel_y}}; input_sizes[1] = n_;
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes); TensorMap<Tensor<T, 2>, Eigen::Aligned> A(a_, input_sizes);
const Eigen::array<TensorIndex, 2> result_sizes = Eigen::array<TensorIndex, 2> kernel_sizes;
{{m_ - kernel_x + 1, n_ - kernel_y + 1}}; kernel_sizes[0] = kernel_x;
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes); kernel_sizes[1] = kernel_y;
Eigen::array<Tensor<float, 2>::Index, 2> dims = {{0, 1}}; TensorMap<Tensor<T, 2>, Eigen::Aligned> B(b_, kernel_sizes);
Eigen::array<TensorIndex, 2> result_sizes;
result_sizes[0] = m_ - kernel_x + 1;
result_sizes[1] = n_ - kernel_y + 1;
TensorMap<Tensor<T, 2>, Eigen::Aligned> C(c_, result_sizes);
Eigen::array<TensorIndex, 2> dims;
dims[0] = 0;
dims[1] = 1;
StartBenchmarkTiming(); StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) { for (int iter = 0; iter < num_iters; ++iter) {
@ -345,15 +405,15 @@ template <typename Device> class BenchmarkSuite {
private: private:
void initialize() { void initialize() {
a_ = (float *) device_.allocate(m_ * k_ * sizeof(float)); a_ = (T *) device_.allocate(m_ * k_ * sizeof(T));
b_ = (float *) device_.allocate(k_ * n_ * sizeof(float)); b_ = (T *) device_.allocate(k_ * n_ * sizeof(T));
c_ = (float *) device_.allocate(m_ * n_ * sizeof(float)); c_ = (T *) device_.allocate(m_ * n_ * sizeof(T));
// Initialize the content of the memory pools to prevent asan from // Initialize the content of the memory pools to prevent asan from
// complaining. // complaining.
device_.memset(a_, 12, m_ * k_ * sizeof(float)); device_.memset(a_, 12, m_ * k_ * sizeof(T));
device_.memset(b_, 23, k_ * n_ * sizeof(float)); device_.memset(b_, 23, k_ * n_ * sizeof(T));
device_.memset(c_, 31, m_ * n_ * sizeof(float)); device_.memset(c_, 31, m_ * n_ * sizeof(T));
//BenchmarkUseRealTime(); //BenchmarkUseRealTime();
} }
@ -372,9 +432,9 @@ template <typename Device> class BenchmarkSuite {
TensorIndex m_; TensorIndex m_;
TensorIndex k_; TensorIndex k_;
TensorIndex n_; TensorIndex n_;
float* a_; T* a_;
float* b_; T* b_;
float* c_; T* c_;
Device device_; Device device_;
}; };
#endif // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_ #endif // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_

10
bench/tensors/tensor_benchmarks_cpu.cc
View File

@ -13,7 +13,7 @@ Eigen::ThreadPoolDevice device(&pool, threads);
static void BM_##FUNC##_##THREADS##T(int iters, int N) { \ static void BM_##FUNC##_##THREADS##T(int iters, int N) { \
StopBenchmarkTiming(); \ StopBenchmarkTiming(); \
CREATE_THREAD_POOL(THREADS); \ CREATE_THREAD_POOL(THREADS); \
BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \ BenchmarkSuite<Eigen::ThreadPoolDevice, float> suite(device, N); \
suite.FUNC(iters); \ suite.FUNC(iters); \
} \ } \
BENCHMARK_RANGE(BM_##FUNC##_##THREADS##T, 10, 5000); BENCHMARK_RANGE(BM_##FUNC##_##THREADS##T, 10, 5000);
@ -81,15 +81,15 @@ BM_FuncCPU(colReduction, 12);
// Contractions // Contractions
#define BM_FuncWithInputDimsCPU(FUNC, D1, D2, D3, THREADS) \ #define BM_FuncWithInputDimsCPU(FUNC, D1, D2, D3, THREADS) \
static void BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T(int iters, int N) {\ static void BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T(int iters, int N) { \
StopBenchmarkTiming(); \ StopBenchmarkTiming(); \
if (THREADS == 1) { \ if (THREADS == 1) { \
Eigen::DefaultDevice device; \ Eigen::DefaultDevice device; \
BenchmarkSuite<Eigen::DefaultDevice> suite(device, D1, D2, D3); \ BenchmarkSuite<Eigen::DefaultDevice, float> suite(device, D1, D2, D3); \
suite.FUNC(iters); \ suite.FUNC(iters); \
} else { \ } else { \
CREATE_THREAD_POOL(THREADS); \ CREATE_THREAD_POOL(THREADS); \
BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, D1, D2, D3); \ BenchmarkSuite<Eigen::ThreadPoolDevice, float> suite(device, D1, D2, D3); \
suite.FUNC(iters); \ suite.FUNC(iters); \
} \ } \
} \ } \
@ -138,7 +138,7 @@ BM_FuncWithInputDimsCPU(contraction, N, N, 1, 16);
static void BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T(int iters, int N) { \ static void BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T(int iters, int N) { \
StopBenchmarkTiming(); \ StopBenchmarkTiming(); \
CREATE_THREAD_POOL(THREADS); \ CREATE_THREAD_POOL(THREADS); \
BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \ BenchmarkSuite<Eigen::ThreadPoolDevice, float> suite(device, N); \
suite.FUNC(iters, DIM1, DIM2); \ suite.FUNC(iters, DIM1, DIM2); \
} \ } \
BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T, 128, 5000); BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T, 128, 5000);

6
bench/tensors/tensor_benchmarks_gpu.cu
View File

@ -12,7 +12,7 @@
StopBenchmarkTiming(); \ StopBenchmarkTiming(); \
Eigen::CudaStreamDevice stream; \ Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \ Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice> suite(device, N); \ BenchmarkSuite<Eigen::GpuDevice, float> suite(device, N); \
cudaDeviceSynchronize(); \ cudaDeviceSynchronize(); \
suite.FUNC(iters); \ suite.FUNC(iters); \
} \ } \
@ -41,7 +41,7 @@ BM_FuncGPU(colReduction);
StopBenchmarkTiming(); \ StopBenchmarkTiming(); \
Eigen::CudaStreamDevice stream; \ Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \ Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice> suite(device, D1, D2, D3); \ BenchmarkSuite<Eigen::GpuDevice, float> suite(device, D1, D2, D3); \
cudaDeviceSynchronize(); \ cudaDeviceSynchronize(); \
suite.FUNC(iters); \ suite.FUNC(iters); \
} \ } \
@ -60,7 +60,7 @@ BM_FuncWithInputDimsGPU(contraction, N, N, 64);
StopBenchmarkTiming(); \ StopBenchmarkTiming(); \
Eigen::CudaStreamDevice stream; \ Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \ Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice> suite(device, N); \ BenchmarkSuite<Eigen::GpuDevice, float> suite(device, N); \
cudaDeviceSynchronize(); \ cudaDeviceSynchronize(); \
suite.FUNC(iters, DIM1, DIM2); \ suite.FUNC(iters, DIM1, DIM2); \
} \ } \

1
unsupported/Eigen/CXX11/Tensor
View File

@ -58,6 +58,7 @@ typedef unsigned __int64 uint64_t;
#endif #endif
#ifdef EIGEN_USE_GPU #ifdef EIGEN_USE_GPU
#include <iostream>
#include <cuda_runtime.h> #include <cuda_runtime.h>
#if defined(__CUDACC__) #if defined(__CUDACC__)
#include <curand_kernel.h> #include <curand_kernel.h>

17
unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
View File

@ -165,6 +165,18 @@ class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprT
typename XprType::Nested m_xpr; typename XprType::Nested m_xpr;
}; };
template <bool SameType, typename Eval, typename Scalar> struct ConversionSubExprEval {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static bool run(Eval& impl, Scalar*) {
impl.evalSubExprsIfNeeded(NULL);
return true;
}
};
template <typename Eval, typename Scalar> struct ConversionSubExprEval<true, Eval, Scalar> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static bool run(Eval& impl, Scalar* data) {
return impl.evalSubExprsIfNeeded(data);
}
};
@ -195,10 +207,9 @@ struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data)
{ {
m_impl.evalSubExprsIfNeeded(NULL); return ConversionSubExprEval<internal::is_same<TargetType, SrcType>::value, TensorEvaluator<ArgType, Device>, Scalar>::run(m_impl, data);
return true;
} }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()

16
unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
View File

@ -230,10 +230,10 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
pos_j_base_powered[1] = pos_j_base; pos_j_base_powered[1] = pos_j_base;
if (line_len > 2) { if (line_len > 2) {
const ComplexScalar pos_j_base_sq = pos_j_base * pos_j_base; const ComplexScalar pos_j_base_sq = pos_j_base * pos_j_base;
for (int i = 2; i < line_len + 1; ++i) { for (int j = 2; j < line_len + 1; ++j) {
pos_j_base_powered[i] = pos_j_base_powered[i - 1] * pos_j_base_powered[j] = pos_j_base_powered[j - 1] *
pos_j_base_powered[i - 1] / pos_j_base_powered[j - 1] /
pos_j_base_powered[i - 2] * pos_j_base_sq; pos_j_base_powered[j - 2] * pos_j_base_sq;
} }
} }
} }
@ -468,7 +468,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
template <int Dir> template <int Dir>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge( EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge(
ComplexScalar* data, int n, int n_power_of_2) { ComplexScalar* data, Index n, Index n_power_of_2) {
// Original code: // Original code:
// RealScalar wtemp = std::sin(M_PI/n); // RealScalar wtemp = std::sin(M_PI/n);
// RealScalar wpi = -std::sin(2 * M_PI/n); // RealScalar wpi = -std::sin(2 * M_PI/n);
@ -482,9 +482,9 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
const ComplexScalar wp_one_2 = wp_one * wp_one; const ComplexScalar wp_one_2 = wp_one * wp_one;
const ComplexScalar wp_one_3 = wp_one_2 * wp_one; const ComplexScalar wp_one_3 = wp_one_2 * wp_one;
const ComplexScalar wp_one_4 = wp_one_3 * wp_one; const ComplexScalar wp_one_4 = wp_one_3 * wp_one;
const int n2 = n / 2; const Index n2 = n / 2;
ComplexScalar w(1.0, 0.0); ComplexScalar w(1.0, 0.0);
for (int i = 0; i < n2; i += 4) { for (Index i = 0; i < n2; i += 4) {
ComplexScalar temp0(data[i + n2] * w); ComplexScalar temp0(data[i + n2] * w);
ComplexScalar temp1(data[i + 1 + n2] * w * wp_one); ComplexScalar temp1(data[i + 1 + n2] * w * wp_one);
ComplexScalar temp2(data[i + 2 + n2] * w * wp_one_2); ComplexScalar temp2(data[i + 2 + n2] * w * wp_one_2);
@ -507,7 +507,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
template <int Dir> template <int Dir>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_1D_Butterfly( EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_1D_Butterfly(
ComplexScalar* data, int n, int n_power_of_2) { ComplexScalar* data, Index n, Index n_power_of_2) {
eigen_assert(isPowerOfTwo(n)); eigen_assert(isPowerOfTwo(n));
if (n > 8) { if (n > 8) {
compute_1D_Butterfly<Dir>(data, n / 2, n_power_of_2 - 1); compute_1D_Butterfly<Dir>(data, n / 2, n_power_of_2 - 1);

55
unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
View File

@ -167,7 +167,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
IsAligned = false, IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout, Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = NumDims == 5, CoordAccess = false,
RawAccess = false RawAccess = false
}; };
@ -437,59 +437,6 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
Index rowInflateStride() const { return m_row_inflate_strides; } Index rowInflateStride() const { return m_row_inflate_strides; }
Index colInflateStride() const { return m_col_inflate_strides; } Index colInflateStride() const { return m_col_inflate_strides; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
// Location of the first element of the patch.
// ColMajor
// 0: d, 1: patch_rows, 2: patch_cols, 3: number of patches, 4: number of batches
// RowMajor
// 0: number of batches, 1: number of patches, 2: patch_cols , 3: patch_rows, 4: d
const Index patch2DIndex = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 3 : 1];
array<Index, NumDims-1> inputCoords;
Index input_col_idx = patch2DIndex / m_fastInputColsEff;
Index inputCol = input_col_idx + coords[1] * m_in_row_strides - m_rowPaddingTop;
Index inputRow = patch2DIndex - input_col_idx * m_input_cols_eff + coords[2] * m_in_col_strides - m_colPaddingLeft;
const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
inputCoords[0] = coords[0]; // depth
inputCoords[1] = origInputCol;
inputCoords[2] = origInputRow;
inputCoords[3] = coords[4]; // batch
} else {
inputCoords[3] = coords[4]; // depth
inputCoords[2] = origInputCol;
inputCoords[1] = origInputRow;
inputCoords[0] = coords[0]; // batch
}
// If the computed coordinates are outside the original image perimeter, return 0.
if (inputCol < 0 || inputCol >= m_input_cols_eff || inputRow < 0 || inputRow >= m_input_rows_eff ||
((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides)) ||
((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
return Scalar(m_paddingValue);
}
if (TensorEvaluator<ArgType, Device>::CoordAccess) {
return m_impl.coeff(inputCoords);
} else {
Index inputIndex;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
inputIndex =
inputCoords[3] * m_patchInputStride +
inputCoords[2] * m_colInputStride +
inputCoords[1] * m_rowInputStride +
inputCoords[0];
} else {
inputIndex =
inputCoords[1] * m_patchInputStride +
inputCoords[2] * m_colInputStride +
inputCoords[3] * m_rowInputStride +
inputCoords[4];
}
return m_impl.coeff(inputIndex);
}
}
protected: protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{ {

22
unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
View File

@ -318,7 +318,7 @@ struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Devi
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout, Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess, CoordAccess = false,
RawAccess = false RawAccess = false
}; };
@ -457,15 +457,6 @@ struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Devi
} }
} }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords)
{
array<Index, NumDims> inputCoords;
for (int i = 0; i < NumDims; ++i) {
inputCoords = coords[i] + this->m_offsets[i];
}
return m_impl.coeff(inputCoords);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const {
Scalar* result = m_impl.data(); Scalar* result = m_impl.data();
if (result) { if (result) {
@ -547,7 +538,7 @@ struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout, Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess, CoordAccess = false,
RawAccess = false RawAccess = false
}; };
@ -608,15 +599,6 @@ struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
} }
} }
} }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(const array<Index, NumDims>& coords)
{
array<Index, NumDims> inputCoords;
for (int i = 0; i < NumDims; ++i) {
inputCoords = coords[i] + this->m_offsets[i];
}
return this->m_impl.coeffRef(inputCoords);
}
}; };

32
unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
View File

@ -151,27 +151,27 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
for (int i = NumDims - 1; i > 0; --i) { for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i]; const Index idx = index / m_outputStrides[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) { if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
index -= idx * m_outputStrides[i]; index -= idx * m_outputStrides[i];
} }
if (index < m_padding[0].first || index >= m_dimensions[0] - m_padding[0].second) { if (index < m_padding[0].first || index >= m_dimensions[0] - m_padding[0].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex += (index - m_padding[0].first); inputIndex += (index - m_padding[0].first);
} else { } else {
for (int i = 0; i < NumDims - 1; ++i) { for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i+1]; const Index idx = index / m_outputStrides[i+1];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) { if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
index -= idx * m_outputStrides[i+1]; index -= idx * m_outputStrides[i+1];
} }
if (index < m_padding[NumDims-1].first || if (index < m_padding[NumDims-1].first ||
index >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) { index >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex += (index - m_padding[NumDims-1].first); inputIndex += (index - m_padding[NumDims-1].first);
} }
@ -194,14 +194,14 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
{ {
const Index idx = coords[0]; const Index idx = coords[0];
if (idx < m_padding[0].first || idx >= m_dimensions[0] - m_padding[0].second) { if (idx < m_padding[0].first || idx >= m_dimensions[0] - m_padding[0].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex = idx - m_padding[0].first; inputIndex = idx - m_padding[0].first;
} }
for (int i = 1; i < NumDims; ++i) { for (int i = 1; i < NumDims; ++i) {
const Index idx = coords[i]; const Index idx = coords[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) { if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
} }
@ -209,14 +209,14 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
{ {
const Index idx = coords[NumDims-1]; const Index idx = coords[NumDims-1];
if (idx < m_padding[NumDims-1].first || idx >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) { if (idx < m_padding[NumDims-1].first || idx >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex = idx - m_padding[NumDims-1].first; inputIndex = idx - m_padding[NumDims-1].first;
} }
for (int i = NumDims - 2; i >= 0; --i) { for (int i = NumDims - 2; i >= 0; --i) {
const Index idx = coords[i]; const Index idx = coords[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) { if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0); return internal::scalar_cast_op<int, Scalar>()(0);
} }
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
} }
@ -245,11 +245,11 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
if (last < lastPaddedLeft) { if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= firstPaddedRight && last < lastPaddedRight) { else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= lastPaddedLeft && last < firstPaddedRight) { else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones. // all the coefficient are between the 2 padding zones.
@ -271,11 +271,11 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
if (last < lastPaddedLeft) { if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= firstPaddedRight && last < lastPaddedRight) { else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= lastPaddedLeft && last < firstPaddedRight) { else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones. // all the coefficient are between the 2 padding zones.
@ -304,11 +304,11 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
if (last < lastPaddedLeft) { if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= firstPaddedRight && last < lastPaddedRight) { else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= lastPaddedLeft && last < firstPaddedRight) { else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones. // all the coefficient are between the 2 padding zones.
@ -330,11 +330,11 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
if (last < lastPaddedLeft) { if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= firstPaddedRight && last < lastPaddedRight) { else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone. // all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0)); return internal::pset1<PacketReturnType>(internal::scalar_cast_op<int, Scalar>()(0));
} }
else if (first >= lastPaddedLeft && last < firstPaddedRight) { else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones. // all the coefficient are between the 2 padding zones.

52
unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
View File

@ -93,7 +93,7 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
IsAligned = false, IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout, Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = true, CoordAccess = false,
RawAccess = false RawAccess = false
}; };
@ -248,56 +248,6 @@ struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
} }
} }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
Index patch_coord_idx = Layout == ColMajor ? NumDims - 1 : 0;
// Location of the first element of the patch.
const Index patchIndex = coords[patch_coord_idx];
if (TensorEvaluator<ArgType, Device>::CoordAccess) {
array<Index, NumDims-1> inputCoords;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = coords[i];
inputCoords[i] = coords[i] + patchIdx;
}
} else {
for (int i = 0; i < NumDims - 2; ++i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = coords[i+1];
inputCoords[i] = coords[i+1] + patchIdx;
}
}
Index coords_idx = Layout == ColMajor ? 0 : NumDims - 1;
inputCoords[0] = (patchIndex + coords[coords_idx]);
return m_impl.coeff(inputCoords);
}
else {
Index inputIndex = 0;
if (Layout == ColMajor) {
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = coords[i];
inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
}
} else {
for (int i = 0; i < NumDims - 2; ++i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = coords[i+1];
inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
}
}
Index coords_idx = Layout == ColMajor ? 0 : NumDims - 1;
inputIndex += (patchIndex + coords[coords_idx]);
return m_impl.coeff(inputIndex);
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected: protected:

75
unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
View File

@ -180,7 +180,7 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
BlockAccess = false, BlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout, Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = NumDims == 6, CoordAccess = false,
RawAccess = false RawAccess = false
}; };
@ -518,79 +518,6 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
Index rowInflateStride() const { return m_row_inflate_strides; } Index rowInflateStride() const { return m_row_inflate_strides; }
Index colInflateStride() const { return m_col_inflate_strides; } Index colInflateStride() const { return m_col_inflate_strides; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
// ColMajor
// 0: depth, 1: patch_planes, 2: patch_rows, 3: patch_cols, 4: number of patches, 5: batches
// RowMajor
// 0: batches, 1: number of patches, 2: patch_cols , 3: patch_rows, 4: patch_planes, 5: depth
const Index patch3DIndex = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 4 : 1];
const Index colOffset = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 3 : 2];
const Index rowOffset= coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 3];
const Index planeOffset = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 4];
array<Index, NumDims-1> inputCoords;
const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
if (inputCol < 0 || inputCol >= m_input_cols_eff ||
((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
return Scalar(m_paddingValue);
}
const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
if (inputRow < 0 || inputRow >= m_input_rows_eff ||
((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
return Scalar(m_paddingValue);
}
const Index planeIndex = patch3DIndex - colIndex * m_outputPlanesRows - rowIndex * m_outputRows;
const Index inputPlane = planeIndex * m_plane_strides + planeOffset * m_in_plane_strides - m_planePaddingTop;
const Index origInputPlane = (m_plane_inflate_strides == 1) ? inputPlane : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0);
if (inputPlane < 0 || inputPlane >= m_input_planes_eff ||
((m_plane_inflate_strides != 1) && (inputPlane != origInputPlane * m_plane_inflate_strides))) {
return Scalar(m_paddingValue);
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
inputCoords[0] = coords[0]; // depth
inputCoords[1] = origInputPlane;
inputCoords[2] = origInputRow;
inputCoords[3] = origInputCol;
inputCoords[4] = coords[5]; // batch
} else {
inputCoords[4] = coords[5]; // depth
inputCoords[3] = origInputPlane;
inputCoords[2] = origInputRow;
inputCoords[1] = origInputCol;
inputCoords[0] = coords[0]; // batch
}
if (TensorEvaluator<ArgType, Device>::CoordAccess) {
return m_impl.coeff(inputCoords);
} else {
Index inputIndex;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
inputIndex =
inputCoords[4] * m_otherInputStride +
inputCoords[3] * m_colInputStride +
inputCoords[2] * m_rowInputStride +
inputCoords[1] * m_planeInputStride +
inputCoords[0];
} else {
inputIndex =
inputCoords[0] * m_otherInputStride +
inputCoords[1] * m_colInputStride +
inputCoords[2] * m_rowInputStride +
inputCoords[3] * m_planeInputStride +
inputCoords[4];
}
return m_impl.coeff(inputIndex);
}
}
protected: protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{ {

41
unsupported/test/cxx11_tensor_of_float16_cuda.cu
View File

@ -55,6 +55,44 @@ void test_cuda_conversion() {
gpu_device.deallocate(d_conv); gpu_device.deallocate(d_conv);
} }
void test_cuda_unary() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
int num_elem = 101;
float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
d_float, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(
d_res_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(
d_res_float, num_elem);
gpu_float.device(gpu_device) = gpu_float.random();
gpu_res_float.device(gpu_device) = gpu_float.abs();
gpu_res_half.device(gpu_device) = gpu_float.cast<half>().abs().cast<float>();
Tensor<float, 1> half_prec(num_elem);
Tensor<float, 1> full_prec(num_elem);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float));
gpu_device.synchronize();
for (int i = 0; i < num_elem; ++i) {
std::cout << "Checking unary " << i << std::endl;
VERIFY_IS_APPROX(full_prec(i), half_prec(i));
}
gpu_device.deallocate(d_float);
gpu_device.deallocate(d_res_half);
gpu_device.deallocate(d_res_float);
}
void test_cuda_elementwise() { void test_cuda_elementwise() {
Eigen::CudaStreamDevice stream; Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream); Eigen::GpuDevice gpu_device(&stream);
@ -201,7 +239,10 @@ void test_cxx11_tensor_of_float16_cuda()
Eigen::GpuDevice device(&stream); Eigen::GpuDevice device(&stream);
if (device.majorDeviceVersion() > 5 || if (device.majorDeviceVersion() > 5 ||
(device.majorDeviceVersion() == 5 && device.minorDeviceVersion() >= 3)) { (device.majorDeviceVersion() == 5 && device.minorDeviceVersion() >= 3)) {
std::cout << "Running test on device with capability " << device.majorDeviceVersion() << "." << device.minorDeviceVersion() << std::endl;
CALL_SUBTEST_1(test_cuda_conversion()); CALL_SUBTEST_1(test_cuda_conversion());
CALL_SUBTEST_1(test_cuda_unary());
CALL_SUBTEST_1(test_cuda_elementwise()); CALL_SUBTEST_1(test_cuda_elementwise());
// CALL_SUBTEST_2(test_cuda_contractions()); // CALL_SUBTEST_2(test_cuda_contractions());
CALL_SUBTEST_3(test_cuda_reductions()); CALL_SUBTEST_3(test_cuda_reductions());