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android / platform / external / pytorch / fb45383ed6 / . / caffe2 / utils / math_cpu.cc
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// Implements the math functions for CPU.
// The implementation in this file allows us to route the underlying numerical
// computation library to different backends. Notably:
// (1) For all BLAS-related functions, one can explicitly request a BLAS backend
// such as MKL, openblas or Atlas. To see the set of supported backends
// currently provided, check //third_party/blas/.
// (2) If one chooses to link against MKL, we utilize MKL's vector math library
// (VML) for a few functions such as Exp and Log.
// (3) Fallback implementations are provided in Eigen for cross-platform
// support. Since Eigen is a header-only library and supports a number of
// platforms, it allows one to quickly port Caffe2 to different platforms
// where BLAS may not be present.
#include <algorithm>
#include <atomic>
#include <chrono>
#include <random>
#include <unordered_set>
#include "caffe2/utils/math.h"
#include "caffe2/utils/cpu_neon.h"
#include "caffe2/core/context.h"
#include "Eigen/Core"
#include "Eigen/Dense"
#ifdef CAFFE2_USE_MKL
#include <mkl.h>
#endif // CAFFE2_USE_MKL
#if defined(_MSC_VER)
#include <process.h>
#endif
namespace caffe2 {
namespace math {
////////////////////////////////////////////////////////////////////////////////
// BLAS alternatives.
// Depending on whether we have specified an external BLAS library or not, we
// will delegate the Caffe math functions that are BLAS-related to either the
// CBLAS call or the Eigen implementation.
////////////////////////////////////////////////////////////////////////////////
#ifdef CAFFE2_USE_EIGEN_FOR_BLAS
// Caffe2 gemm provides a simpler interface to the gemm functions, with the
// limitation that the data has to be contiguous in memory.
//
// The gemm call implements the following operation:
//
// C = alpha * op(A) * op(B) + beta * C
//
// where op(A) has size M x K, op(B) has size K x N, and C has size M x N. Each
// of A, B, and C are matrices and alpha and beta are scalars. Note that the
// most common use case of gemm will involve setting alpha to 1 and beta to 0.
//
// op(A) and op(B) represent the transformations that are done to A and B before
// the matrix multiply; depending on the flags set, op(A) is equal to A or A^T
// (transpose) if the argument TransA or TransB is set to CblasNoTrans or
// CblasTrans, respectively, for each of A and B.
template <>
void Gemm<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB,
const int M,
const int N,
const int K,
const float alpha,
const float* A,
const float* B,
const float beta,
float* C,
CPUContext* context,
TensorProto::DataType math_type) {
auto C_mat = EigenMatrixMap<float>(C, N, M);
if (beta == 0) {
C_mat.setZero();
} else {
C_mat *= beta;
}
switch (TransA) {
case CblasNoTrans: {
switch (TransB) {
case CblasNoTrans:
C_mat.noalias() += alpha * (
ConstEigenMatrixMap<float>(B, N, K) *
ConstEigenMatrixMap<float>(A, K, M));
return;
case CblasTrans:
C_mat.noalias() += alpha * (
ConstEigenMatrixMap<float>(B, K, N).transpose() *
ConstEigenMatrixMap<float>(A, K, M));
return;
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for TransB";
}
}
case CblasTrans: {
switch (TransB) {
case CblasNoTrans:
C_mat.noalias() += alpha * (
ConstEigenMatrixMap<float>(B, N, K) *
ConstEigenMatrixMap<float>(A, M, K).transpose());
return;
case CblasTrans:
C_mat.noalias() += alpha * (
ConstEigenMatrixMap<float>(B, K, N).transpose() *
ConstEigenMatrixMap<float>(A, M, K).transpose());
return;
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for TransB";
}
}
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for TransA";
}
}
template <>
void GemmEx<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB,
const int M,
const int N,
const int K,
const float alpha,
const float* A,
const int lda,
const float* B,
const int ldb,
const float beta,
float* C,
const int ldc,
CPUContext*) {
using OuterStride = Eigen::OuterStride<Eigen::Dynamic>;
using StridedMap = Eigen::Map<Eigen::MatrixXf, 0, OuterStride>;
using ConstStridedMap = Eigen::Map<const Eigen::MatrixXf, 0, OuterStride>;
auto C_mat = StridedMap(C, N, M, OuterStride(ldc));
if (beta == 0) {
C_mat.setZero();
} else {
C_mat *= beta;
}
switch (TransA) {
case CblasNoTrans: {
switch (TransB) {
case CblasNoTrans:
C_mat.noalias() +=
alpha * (ConstStridedMap(B, N, K, OuterStride(ldb)) *
ConstStridedMap(A, K, M, OuterStride(lda)));
return;
case CblasTrans:
C_mat.noalias() +=
alpha * (ConstStridedMap(B, K, N, OuterStride(ldb)).transpose() *
ConstStridedMap(A, K, M, OuterStride(lda)));
return;
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for TransB";
}
}
case CblasTrans: {
switch (TransB) {
case CblasNoTrans:
C_mat.noalias() +=
alpha * (ConstStridedMap(B, N, K, OuterStride(ldb)) *
ConstStridedMap(A, M, K, OuterStride(lda)).transpose());
return;
case CblasTrans:
C_mat.noalias() +=
alpha * (ConstStridedMap(B, K, N, OuterStride(ldb)).transpose() *
ConstStridedMap(A, M, K, OuterStride(lda)).transpose());
return;
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for TransB";
}
}
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for TransA";
}
}
template <>
void Gemv<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const int M,
const int N,
const float alpha,
const float* A,
const float* x,
const float beta,
float* y,
CPUContext* context,
TensorProto::DataType math_type) {
EigenVectorMap<float> y_vec(y, TransA == CblasNoTrans ? M : N);
if (beta == 0) {
// In Caffe2 we often do a lazy initialization, which may contain NaNs in
// the float values. As a result, if beta is 0, we explicitly do a setzero.
y_vec.setZero();
} else {
y_vec *= beta;
}
switch (TransA) {
case CblasNoTrans: {
y_vec.noalias() += alpha * (
ConstEigenMatrixMap<float>(A, N, M).transpose() *
ConstEigenVectorMap<float>(x, N));
return;
}
case CblasTrans: {
y_vec.noalias() += alpha * (
ConstEigenMatrixMap<float>(A, N, M) *
ConstEigenVectorMap<float>(x, M));
return;
}
default:
LOG(FATAL) << "Gemv float found an unexpected CBLAS_TRANSPOSE input.";
}
}
#define CAFFE2_SPECIALIZED_SCALE(T) \
template <> \
void Scale<T, CPUContext>( \
const int n, const float alpha, const T* x, T* y, CPUContext* context) { \
EigenVectorMap<T>(y, n) = ConstEigenVectorMap<T>(x, n) * alpha; \
} \
template <> \
void Scale<T, CPUContext>( \
const int n, \
const float* alpha, \
const T* x, \
T* y, \
CPUContext* context) { \
EigenVectorMap<T>(y, n) = ConstEigenVectorMap<T>(x, n) * (*alpha); \
}
CAFFE2_SPECIALIZED_SCALE(float)
#undef CAFFE2_SPECIALIZED_SCALE
#define CAFFE2_SPECIALIZED_DOT(T) \
template<> \
void Dot<T, CPUContext>( \
const int N, const T* a, const T* b, T* y, \
CPUContext* context) { \
*y = ConstEigenVectorMap<T>(a, N).dot(ConstEigenVectorMap<T>(b, N)); \
}
CAFFE2_SPECIALIZED_DOT(float)
#undef CAFFE2_SPECIALIZED_DOT
#define CAFFE2_SPECIALIZED_AXPY(T) \
template <> \
void Axpy<T, CPUContext>( \
const int N, const T alpha, const T* x, T* Y, CPUContext* context) { \
EigenVectorMap<T>(Y, N) += ConstEigenVectorMap<T>(x, N) * alpha; \
} \
template <> \
void Axpy<T, CPUContext>( \
const int N, const T* alpha, const T* x, T* Y, CPUContext* context) { \
EigenVectorMap<T>(Y, N) += ConstEigenVectorMap<T>(x, N) * (*alpha); \
}
CAFFE2_SPECIALIZED_AXPY(float)
#undef CAFFE2_SPECIALIZED_AXPY
#define CAFFE2_SPECIALIZED_AXPBY(T) \
template <> \
void Axpby<T, CPUContext>(const int N, const T alpha, const T* x, \
const T beta, T* y, CPUContext* context) { \
EigenVectorMap<T> y_vec(y, N); \
y_vec = y_vec * beta + ConstEigenVectorMap<T>(x, N) * alpha; \
}
CAFFE2_SPECIALIZED_AXPBY(float)
#undef CAFFE2_SPECIALIZED_AXPBY
#else // CAFFE2_USE_EIGEN_FOR_BLAS
template <>
void Gemm<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB,
const int M,
const int N,
const int K,
const float alpha,
const float* A,
const float* B,
const float beta,
float* C,
CPUContext* /*context*/,
TensorProto::DataType /*math_type*/) {
int lda = (TransA == CblasNoTrans) ? K : M;
int ldb = (TransB == CblasNoTrans) ? N : K;
cblas_sgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B, ldb,
beta, C, N);
}
template <>
void GemmEx<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB,
const int M,
const int N,
const int K,
const float alpha,
const float* A,
const int lda,
const float* B,
const int ldb,
const float beta,
float* C,
const int ldc,
CPUContext* /*context*/) {
cblas_sgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <>
void Gemv<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const int M,
const int N,
const float alpha,
const float* A,
const float* x,
const float beta,
float* y,
CPUContext* /*context*/,
TensorProto::DataType /*math_type*/) {
cblas_sgemv(CblasRowMajor, TransA, M, N, alpha, A, N, x, 1, beta, y, 1);
}
#define CAFFE2_SPECIALIZED_SCALE(T, prefix) \
template <> \
void Scale<T, CPUContext>( \
const int n, const float alpha, const T* x, T* y, CPUContext*) { \
if (y != x) \
cblas_##prefix##copy(n, x, 1, y, 1); \
cblas_##prefix##scal(n, static_cast<float>(alpha), y, 1); \
} \
template <> \
void Scale<T, CPUContext>( \
const int n, const float* alpha, const T* x, T* y, CPUContext*) { \
if (y != x) \
cblas_##prefix##copy(n, x, 1, y, 1); \
cblas_##prefix##scal(n, static_cast<float>(*alpha), y, 1); \
}
CAFFE2_SPECIALIZED_SCALE(float, s)
#undef CAFFE2_SPECIALIZED_SCALE
#define CAFFE2_SPECIALIZED_DOT(T, prefix) \
template <> \
void Dot<T, CPUContext>( \
const int N, const T* a, const T* b, T* y, CPUContext*) { \
*y = cblas_##prefix##dot(N, a, 1, b, 1); \
}
CAFFE2_SPECIALIZED_DOT(float, s)
#undef CAFFE2_SPECIALIZED_DOT
#define CAFFE2_SPECIALIZED_AXPY(T, prefix) \
template <> \
void Axpy<T, CPUContext>( \
const int N, const T alpha, const T* x, T* y, CPUContext*) { \
cblas_##prefix##axpy(N, alpha, x, 1, y, 1); \
} \
template <> \
void Axpy<T, CPUContext>( \
const int N, const T* alpha, const T* x, T* y, CPUContext*) { \
cblas_##prefix##axpy(N, *alpha, x, 1, y, 1); \
}
CAFFE2_SPECIALIZED_AXPY(float, s)
#undef CAFFE2_SPECIALIZED_AXPY
// cblas_[sd]axpby is not a standard blas function, and if MKL is not present,
// we will need to implement it.
#ifdef CAFFE2_USE_MKL
#define CAFFE2_SPECIALIZED_AXPBY(T, prefix) \
template <> \
void Axpby<T, CPUContext>( \
const int N, \
const T alpha, \
const T* x, \
const T beta, \
T* y, \
CPUContext*) { \
cblas_##prefix##axpby(N, alpha, x, 1, beta, y, 1); \
}
#else // CAFFE2_USE_MKL
#define CAFFE2_SPECIALIZED_AXPBY(T, prefix) \
template <> \
void Axpby<T, CPUContext>( \
const int N, \
const T alpha, \
const T* x, \
const T beta, \
T* y, \
CPUContext*) { \
cblas_##prefix##scal(N, beta, y, 1); \
cblas_##prefix##axpy(N, alpha, x, 1, y, 1); \
}
#endif // CAFFE2_USE_MKL
CAFFE2_SPECIALIZED_AXPBY(float, s)
#undef CAFFE2_SPECIALIZED_AXPBY
#endif // CAFFE2_USE_EIGEN_FOR_BLAS
template <>
void GemmBatched<float, CPUContext>(
const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB,
const int A_size,
const int A_batches,
const int B_size,
const int B_batches,
const int M,
const int N,
const int K,
const float alpha,
const float* A,
const float* B,
const float beta,
float* C,
CPUContext* context,
Tensor<CPUContext>*, /* scratch */
TensorProto::DataType /* math_type */) {
auto a_offset = A_size / A_batches;
auto b_offset = B_size / B_batches;
auto y_offset = M * N;
// loop over matrices in the batch
for (int i = 0; i < A_batches; ++i) {
math::Gemm<float, CPUContext>(
TransA,
TransB,
M,
N,
K,
1,
A + a_offset * i,
B + b_offset * i,
0,
C + y_offset * i,
context);
}
}
////////////////////////////////////////////////////////////////////////////////
// MKL VML alternatives.
// Depending on whether we are using MKL, we will delegate the Caffe math
// functions that are VML-related to either the VML call or the Eigen
// implementation. If you are setting the flags (such as AVX) right for your CPU
// architecture, usually Eigen will deliver a throughput as fast as the VML
// functions.
////////////////////////////////////////////////////////////////////////////////
#ifdef CAFFE2_USE_MKL
#define DELEGATE_SIMPLE_UNARY_FUNCTION(T, Funcname, OriginalFunc, ...) \
template <> \
void Funcname<T, CPUContext>(const int N, const T* x, T* y, CPUContext*) { \
OriginalFunc(N, x, y, ##__VA_ARGS__); \
}
DELEGATE_SIMPLE_UNARY_FUNCTION(
float,
Exp,
vmsExp,
VML_HA | VML_FTZDAZ_OFF | VML_ERRMODE_IGNORE)
DELEGATE_SIMPLE_UNARY_FUNCTION(
double,
Exp,
vmdExp,
VML_HA | VML_FTZDAZ_OFF | VML_ERRMODE_IGNORE)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Log, vsLn)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, Log, vdLn)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Cos, vsCos)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, Cos, vdCos)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Sin, vsSin)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, Sin, vdSin)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Abs, vsAbs)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, Abs, vdAbs)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Sqrt, vsSqrt)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, Sqrt, vdSqrt)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, InvSqrt, vsInvSqrt)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, InvSqrt, vdInvSqrt)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Sqr, vsSqr)
DELEGATE_SIMPLE_UNARY_FUNCTION(double, Sqr, vdSqr)
#undef DELEGATE_SIMPLE_UNARY_FUNCTION
#define DELEGATE_SINCOS_FUNCTION(T, OriginalFunc) \
template <> \
void SinCos<T, CPUContext>( \
const int N, const T* a, T* ys, T* yc, CPUContext*) { \
OriginalFunc(N, a, ys, yc); \
}
DELEGATE_SINCOS_FUNCTION(float, vsSinCos)
DELEGATE_SINCOS_FUNCTION(double, vdSinCos)
#undef DELEGATE_SINCOS_FUNCTION
#define DELEGATE_POWX_FUNCTION(T, OriginalFunc) \
template <> \
void Powx<T, CPUContext>(const int N, const T* a, T b, T* y, CPUContext*) { \
OriginalFunc(N, a, b, y); \
}
DELEGATE_POWX_FUNCTION(float, vsPowx)
DELEGATE_POWX_FUNCTION(double, vdPowx)
#undef DELEGATE_POWX_FUNCTION
#define DELEGATE_SIMPLE_BINARY_FUNCTION(T, Funcname, OriginalFunc) \
template <> \
void Funcname<T, CPUContext>( \
const int N, const T* a, const T* b, T* y, CPUContext*) { \
OriginalFunc(N, a, b, y); \
}
DELEGATE_SIMPLE_BINARY_FUNCTION(float, Add, vsAdd)
DELEGATE_SIMPLE_BINARY_FUNCTION(double, Add, vdAdd)
DELEGATE_SIMPLE_BINARY_FUNCTION(float, Sub, vsSub)
DELEGATE_SIMPLE_BINARY_FUNCTION(double, Sub, vdSub)
DELEGATE_SIMPLE_BINARY_FUNCTION(float, Mul, vsMul)
DELEGATE_SIMPLE_BINARY_FUNCTION(double, Mul, vdMul)
DELEGATE_SIMPLE_BINARY_FUNCTION(float, Div, vsDiv)
DELEGATE_SIMPLE_BINARY_FUNCTION(double, Div, vdDiv)
#undef DELEGATE_SIMPLE_BINARY_FUNCTION
#else // CAFFE2_USE_MKL
#define DELEGATE_SIMPLE_UNARY_FUNCTION(T, Funcname, expr) \
template <> \
void Funcname<T, CPUContext>(const int N, const T* x, T* y, CPUContext*) { \
EigenVectorMap<T>(y, N) = ConstEigenVectorMap<T>(x, N).array().expr(); \
}
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Exp, exp)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Log, log)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Cos, cos)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Sin, sin)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Abs, abs)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Sqrt, sqrt)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, InvSqrt, rsqrt)
DELEGATE_SIMPLE_UNARY_FUNCTION(float, Sqr, square)
#undef DELEGATE_SIMPLE_UNARY_FUNCTION
#define DELEGATE_SINCOS_FUNCTION(T) \
template <> \
void SinCos<T, CPUContext>( \
const int N, const T* x, T* ys, T* yc, CPUContext*) { \
EigenVectorMap<T>(ys, N) = ConstEigenVectorMap<T>(x, N).array().sin(); \
EigenVectorMap<T>(yc, N) = ConstEigenVectorMap<T>(x, N).array().cos(); \
}
DELEGATE_SINCOS_FUNCTION(float)
DELEGATE_SINCOS_FUNCTION(double)
#undef DELEGATE_SINCOS_FUNCTION
#define DELEGATE_POWX_FUNCTION(T) \
template <> \
void Powx<T, CPUContext>(const int N, const T* a, T b, T* y, CPUContext*) { \
EigenVectorMap<T>(y, N) = ConstEigenVectorMap<T>(a, N).array().pow(b); \
}
DELEGATE_POWX_FUNCTION(float)
#undef DELEGATE_POWX_FUNCTION
#endif // CAFFE2_USE_MKL
#define EIGEN_SIMPLE_BINARY_FUNCTION(T, Funcname, expr) \
template <> \
void Funcname<T, CPUContext>( \
const int N, const T* a, const T* b, T* y, \
CPUContext*) { \
EigenVectorMap<T>(y, N) = \
ConstEigenVectorMap<T>(a, N).array() expr \
ConstEigenVectorMap<T>(b, N).array(); \
}
#ifdef CAFFE2_USE_MKL
#define DEFINE_SIMPLE_BINARY_FUNCTION(Funcname, expr) \
EIGEN_SIMPLE_BINARY_FUNCTION(int32_t, Funcname, expr) \
EIGEN_SIMPLE_BINARY_FUNCTION(int64_t, Funcname, expr)
#else
#define DEFINE_SIMPLE_BINARY_FUNCTION(Funcname, expr) \
EIGEN_SIMPLE_BINARY_FUNCTION(float, Funcname, expr) \
EIGEN_SIMPLE_BINARY_FUNCTION(int32_t, Funcname, expr) \
EIGEN_SIMPLE_BINARY_FUNCTION(int64_t, Funcname, expr)
#endif
DEFINE_SIMPLE_BINARY_FUNCTION(Add, +)
DEFINE_SIMPLE_BINARY_FUNCTION(Sub, -)
DEFINE_SIMPLE_BINARY_FUNCTION(Mul, *)
DEFINE_SIMPLE_BINARY_FUNCTION(Div, /)
#undef EIGEN_SIMPLE_BINARY_FUNCTION
#undef DEFINE_FLOAT_BINARY_FUNCTION
////////////////////////////////////////////////////////////////////////////////
// Common math functions being used in Caffe that do not have a BLAS or MKL
// equivalent. For all these functions, we will simply implement them either via
// Eigen or via custom code.
////////////////////////////////////////////////////////////////////////////////
#define CAFFE2_SPECIALIZED_REDUCEMIN(T) \
template <> \
void ReduceMin<T, CPUContext>( \
const int N, \
const T* x, \
T* y, \
Tensor<CPUContext>* /*scratch_ptr*/, \
CPUContext* /*context*/) { \
*y = *std::min_element(x, x + N); \
}
CAFFE2_SPECIALIZED_REDUCEMIN(float)
#undef CAFFE2_SPECIALIZED_REDUCEMIN
#define CAFFE2_SPECIALIZED_REDUCEMAX(T) \
template <> \
void ReduceMax<T, CPUContext>( \
const int N, \
const T* x, \
T* y, \
Tensor<CPUContext>* /*scratch_ptr*/, \
CPUContext* /*context*/) { \
*y = *std::max_element(x, x + N); \
}
CAFFE2_SPECIALIZED_REDUCEMAX(float)
CAFFE2_SPECIALIZED_REDUCEMAX(int32_t)
CAFFE2_SPECIALIZED_REDUCEMAX(int64_t)
#undef CAFFE2_SPECIALIZED_REDUCEMAX
#define CAFFE2_SPECIALIZED_ROWWISEMAX(T) \
template <> \
void RowwiseMax<T, CPUContext>( \
const int N, const int D, const T* x, T* y, CPUContext*) { \
EigenVectorMap<T>(y, N) = \
ConstEigenMatrixMap<T>(x, D, N).colwise().maxCoeff(); \
}
CAFFE2_SPECIALIZED_ROWWISEMAX(float)
#undef CAFFE2_SPECIALIZED_ROWWISEMAX
#define CAFFE2_SPECIALIZED_COLWISEMAX(T) \
template <> \
void ColwiseMax<T, CPUContext>( \
const int N, const int D, const T* x, T* y, CPUContext*) { \
EigenVectorMap<T>(y, D) = \
ConstEigenMatrixMap<T>(x, D, N).rowwise().maxCoeff(); \
}
CAFFE2_SPECIALIZED_COLWISEMAX(float)
#undef CAFFE2_SPECIALIZED_COLWISEMAX
#define CAFFE2_SPECIALIZED_ELEMWISEMAX(T) \
template <> \
void ElemwiseMax<T, CPUContext>( \
const int N, const T* x, const T* y, T* z, CPUContext* /*context*/) { \
std::transform(x, x + N, y, z, [](const T& x_i, const T& y_i) { \
return std::max(x_i, y_i); \
}); \
}
CAFFE2_SPECIALIZED_ELEMWISEMAX(float)
#undef CAFFE2_SPECIALIZED_ELEMWISEMAX
#define CAFFE2_SPECIALIZED_MAXIMUM(T) \
template <> \
void Maximum<T, CPUContext>( \
const int N, const float alpha, const T* x, T* y, CPUContext* context) { \
std::transform( \
x, x + N, y, [&alpha](const T& x_i) { return std::max(x_i, alpha); }); \
}
CAFFE2_SPECIALIZED_MAXIMUM(float)
#undef CAFFE2_SPECIALIZED_MAXIMUM
// AddToRow and AddToCol adds the corresponding row/col vector b to the matrix a
// of shape M x N. The actual implementation uses eigen which is column major,
// so notice the row/column swap in the actual implementation.
#define DELEGATE_BROADCAST_BINARY_FUNCTION(T, Funcname, expr) \
template <> \
void Funcname##ToRow<T, CPUContext>( \
const int M, const int N, const T* a, const T* b, T* y, CPUContext*) { \
EigenArrayMap<T>(y, N, M) = ConstEigenArrayMap<T>(a, N, M).colwise() \
expr ConstEigenVectorArrayMap<T>(b, N); \
} \
/* inplace versions */ \
template <> \
void Funcname##ToRow<T, CPUContext>( \
const int M, const int N, const T* x, T* y, CPUContext*) { \
EigenArrayMap<T>(y, N, M).colwise() expr## = \
ConstEigenVectorArrayMap<T>(x, N); \
} \
template <> \
void Funcname##ToCol<T, CPUContext>( \
const int M, const int N, const T* x, T* y, CPUContext*) { \
EigenArrayMap<T>(y, N, M).rowwise() expr## = \
ConstEigenVectorArrayMap<T>(x, M).transpose(); \
}
#define DEFINE_BROADCAST_BINARY_FUNCTION(name, op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(int32_t, name, op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(int64_t, name, op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(float, name, op) \
DEFINE_BROADCAST_BINARY_FUNCTION(Add, +)
DEFINE_BROADCAST_BINARY_FUNCTION(Sub, -)
DEFINE_BROADCAST_BINARY_FUNCTION(Mul, *)
DEFINE_BROADCAST_BINARY_FUNCTION(Div, /)
#undef DEFINE_BROADCAST_BINARY_FUNCTION
#undef DELEGATE_BROADCAST_BINARY_FUNCTION
#define CAFFE2_SPECIALIZED_SET(T) \
template <> \
void Set<T, CPUContext>(const TIndex N, const T alpha, T* Y, CPUContext*) { \
if (alpha == (T)0) { \
memset(Y, 0, N * sizeof(T)); \
} else { \
EigenVectorMap<T>(Y, N).setConstant(alpha); \
} \
}
CAFFE2_SPECIALIZED_SET(float);
CAFFE2_SPECIALIZED_SET(double);
CAFFE2_SPECIALIZED_SET(int8_t);
CAFFE2_SPECIALIZED_SET(int16_t);
CAFFE2_SPECIALIZED_SET(int);
CAFFE2_SPECIALIZED_SET(int64_t);
CAFFE2_SPECIALIZED_SET(bool);
CAFFE2_SPECIALIZED_SET(char);
CAFFE2_SPECIALIZED_SET(uint8_t);
CAFFE2_SPECIALIZED_SET(uint16_t);
#undef CAFFE2_SPECIALIZED_SET
#define CAFFE2_INSTANTIATE_BINARY_OP(name, op, T) \
template <> \
void name<T, CPUContext>( \
const int n, const T* a, const T* b, bool* y, CPUContext*) { \
for (int i = 0; i < n; ++i) { \
y[i] = a[i] op b[i]; \
} \
} \
template <> \
void name##ToRow<T, CPUContext>( \
const int m, \
const int n, \
const T* a, \
const T* b, \
bool* y, \
CPUContext*) { \
for (int i = 0; i < n * m; ++i) { \
y[i] = a[i] op b[i % n]; \
} \
}
#define CAFFE2_DEFINE_BINARY_OP(name, op) \
CAFFE2_INSTANTIATE_BINARY_OP(name, op, float) \
CAFFE2_INSTANTIATE_BINARY_OP(name, op, int32_t) \
CAFFE2_INSTANTIATE_BINARY_OP(name, op, int64_t)
CAFFE2_DEFINE_BINARY_OP(LT, <);
CAFFE2_DEFINE_BINARY_OP(LE, <=);
CAFFE2_DEFINE_BINARY_OP(GT, >);
CAFFE2_DEFINE_BINARY_OP(GE, >=);
CAFFE2_INSTANTIATE_BINARY_OP(Or, |, bool);
CAFFE2_INSTANTIATE_BINARY_OP(And, &, bool);
CAFFE2_INSTANTIATE_BINARY_OP(Xor, ^, bool);
template <>
void Not<bool, CPUContext>(
const int n,
const bool* x,
bool* y,
CPUContext* /*context*/) {
for (int i = 0; i < n; ++i) {
y[i] = !x[i];
}
}
#undef CAFFE2_DEFINE_BINARY_OP
#undef CAFFE2_INSTANTIATE_BINARY_OP
#define CAFFE2_SPECIALIZED_CPU_ADD_STRIPED_BATCH(T) \
template <> \
void AddStripedBatch( \
const int N, \
const T* first, \
T* y, \
const int stripe, \
const int batch, \
CPUContext* context) { \
for (int j = 0; j < batch; j++) { \
Add<T, CPUContext>(N, first + j * stripe, y, y, context); \
} \
}
CAFFE2_SPECIALIZED_CPU_ADD_STRIPED_BATCH(float);
#undef CAFFE2_SPECIALIZED_CPU_ADD_STRIPED_BATCH
template <>
void RandUniform<float, CPUContext>(
const int n, const float a, const float b, float* r,
CPUContext* context) {
std::uniform_real_distribution<float> distribution(a, b);
for (int i = 0; i < n; ++i) {
r[i] = distribution(context->RandGenerator());
}
}
template <>
void RandUniform<int, CPUContext>(
const int n, const int a, const int b, int* r,
CPUContext* context) {
std::uniform_int_distribution<int> distribution(a, b);
for (int i = 0; i < n; ++i) {
r[i] = distribution(context->RandGenerator());
}
}
#define CAFFE2_SPECIALIZED_RAND_UNIFORM_UNIQUE(T) \
template <> \
void RandUniformUnique<T, CPUContext>( \
const size_t n, \
const T a, \
const T b, \
T* r, \
const size_t m, \
const T* avoid, \
CPUContext* context) { \
CAFFE_ENFORCE_LE( \
n, b - a - m + 1, "Cannot satisfy the unique requirement"); \
std::unordered_set<T> avoid_set(n); \
if (m) { \
avoid_set.insert(avoid, avoid + m); \
CAFFE_ENFORCE_EQ(m, avoid_set.size(), "Avoid should be unique"); \
} \
std::uniform_int_distribution<T> distribution(a, b); \
T v = 0; \
for (size_t i = 0; i < n; ++i) { \
do { \
v = distribution(context->RandGenerator()); \
} while (avoid_set.count(v)); \
r[i] = v; \
avoid_set.insert(v); \
} \
}
CAFFE2_SPECIALIZED_RAND_UNIFORM_UNIQUE(int32_t);
CAFFE2_SPECIALIZED_RAND_UNIFORM_UNIQUE(int64_t);
#undef CAFFE2_SPECIALIZED_RAND_UNIFORM_UNIQUE
template <>
void RandGaussian<float, CPUContext>(
const int n, const float mean, const float std, float* r,
CPUContext* context) {
std::normal_distribution<float> distribution(mean, std);
for (int i = 0; i < n; ++i) {
r[i] = distribution(context->RandGenerator());
}
}
#define CAFFE2_SPECIALIZED_SUM(T) \
template <> \
void Sum<T, CPUContext>( \
const int N, \
const T* x, \
T* y, \
CPUContext* /* unused */, \
Tensor<CPUContext>* /* unused */) { \
*y = ConstEigenVectorMap<T>(x, N).sum(); \
}
CAFFE2_SPECIALIZED_SUM(float);
CAFFE2_SPECIALIZED_SUM(int32_t);
CAFFE2_SPECIALIZED_SUM(int64_t);
#undef CAFFE2_SPECIALIZED_SUM
template <>
void SumSqr<float, CPUContext>(
const int N,
const float* x,
float* y,
CPUContext* /*context*/ /* unused */,
Tensor<CPUContext>* /*scratch_ptr*/ /* unused */) {
*y = ConstEigenVectorMap<float>(x, N).squaredNorm();
}
template <>
void Select<float, CPUContext>(
const int N,
const int D,
const float* x,
const int* idx,
float* y,
CPUContext* /*context*/) {
for (int i = 0; i < N; ++i) {
DCHECK_LT(idx[i], D);
y[i] = x[i * D + idx[i]];
}
}
// Ported from caffe 1.
template <>
void Im2colNd<float, CPUContext, StorageOrder::NCHW>(
const float* data_img,
const int* im_shape,
const int* col_shape,
const int /* img_size*/,
const int /* col_size*/,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const int N,
float* data_col,
CPUContext* /* context */,
bool accumulate_output) {
int kernel_size = 1;
for (int i = 0; i < N; ++i) {
kernel_size *= kernel_shape[i];
}
const int channels_col = col_shape[0];
vector<int> d_offset(N, 0);
vector<int> d_iter(N, 0);
for (int c_col = 0; c_col < channels_col; ++c_col) {
// Loop over spatial axes in reverse order to compute a per-axis offset.
int offset = c_col;
for (int d_i = N - 1; d_i >= 0; --d_i) {
if (d_i < N - 1) {
offset /= kernel_shape[d_i + 1];
}
d_offset[d_i] = offset % kernel_shape[d_i];
}
for (bool incremented = true; incremented;) {
// Loop over spatial axes in forward order to compute the indices in the
// image and column, and whether the index lies in the padding.
int index_col = c_col;
int index_im = c_col / kernel_size;
bool is_padding = false;
for (int d_i = 0; d_i < N; ++d_i) {
const int d = d_iter[d_i];
const int d_im =
d * stride[d_i] - pad[d_i] + d_offset[d_i] * dilation[d_i];
is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1];
index_col *= col_shape[d_i + 1];
index_col += d;
index_im *= im_shape[d_i + 1];
index_im += d_im;
}
if (!accumulate_output) {
if (is_padding) {
data_col[index_col] = 0;
} else {
data_col[index_col] = data_img[index_im];
}
} else if (!is_padding) { // col2im
data_col[index_im] += data_img[index_col];
}
// Loop over spatial axes in reverse order to choose an index,
// like counting.
incremented = false;
for (int d_i = N - 1; d_i >= 0; --d_i) {
const int d_max = col_shape[d_i + 1];
DCHECK_LT(d_iter[d_i], d_max);
if (d_iter[d_i] == d_max - 1) {
d_iter[d_i] = 0;
} else { // d_iter[d_i] < d_max - 1
++d_iter[d_i];
incremented = true;
break;
}
}
} // while(incremented) {
} // for (int c = 0; c < channels_col; ++c) {
}
template <>
void Col2imNd<float, CPUContext, StorageOrder::NCHW>(
const float* data_col,
const int* img_shape,
const int* col_shape,
const int img_size,
const int col_size,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const int N,
float* data_img,
CPUContext* context) {
Set<float, CPUContext>(img_size, 0, data_img, context);
Im2colNd<float, CPUContext, StorageOrder::NCHW>(
data_col,
img_shape,
col_shape,
img_size,
col_size,
kernel_shape,
stride,
dilation,
pad,
N,
data_img,
context,
true);
}
template <>
void Im2col<float, CPUContext, StorageOrder::NCHW>(
const float* data_im,
const int channels,
const int height,
const int width,
const int kernel_h,
const int kernel_w,
const int dilation_h,
const int dilation_w,
const int pad_t,
const int pad_l,
const int pad_b,
const int pad_r,
const int stride_h,
const int stride_w,
float* data_col,
CPUContext* /*context*/) {
const int output_h =
(height + pad_b + pad_t - (dilation_h * (kernel_h - 1) + 1)) / stride_h +
1;
const int output_w =
(width + pad_l + pad_r - (dilation_w * (kernel_w - 1) + 1)) / stride_w +
1;
// Fast path for zero padding and no dilation
// From Torch, THNN_(unfolded_copy)
if (dilation_h == 1 && dilation_w == 1 && pad_l == 0 && pad_r == 0 &&
pad_t == 0 && pad_b == 0) {
for (auto k = 0; k < channels * kernel_h * kernel_w; k++) {
const auto nip = k / (kernel_h * kernel_w);
const auto rest = k % (kernel_h * kernel_w);
const auto kh = rest / kernel_w;
const auto kw = rest % kernel_w;
auto* dst = data_col + nip * (kernel_h * kernel_w * output_h * output_w) +
kh * (kernel_w * output_h * output_w) + kw * (output_h * output_w);
const auto* src = data_im + nip * (height * width);
for (auto y = 0; y < output_h; y++) {
const auto iy = y * stride_h + kh;
const auto ix = kw;
if (stride_w == 1) {
memcpy(
dst + (y * output_w),
src + (iy * width + ix),
sizeof(float) * output_w);
} else {
for (auto x = 0; x < output_w; x++) {
memcpy(
dst + (y * output_w + x),
src + (iy * width + ix + x * stride_w),
sizeof(float));
}
}
}
}
return;
}
// Fast path for equal padding
if (pad_l == pad_r && pad_t == pad_b) {
// From Intel, https://github.com/BVLC/caffe/pull/3536
const int pad_h = pad_t;
const int pad_w = pad_l;
const int channel_size = height * width;
for (int channel = channels; channel--; data_im += channel_size) {
for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
int input_row = -pad_h + kernel_row * dilation_h;
for (int output_rows = output_h; output_rows; output_rows--) {
if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
for (int output_cols = output_w; output_cols; output_cols--) {
*(data_col++) = 0;
}
} else {
int input_col = -pad_w + kernel_col * dilation_w;
for (int output_col = output_w; output_col; output_col--) {
if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
*(data_col++) = data_im[input_row * width + input_col];
} else {
*(data_col++) = 0;
}
input_col += stride_w;
}
}
input_row += stride_h;
}
}
}
}
return;
}
// Baseline
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
int height_col = (height + pad_t + pad_b - dkernel_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - dkernel_w) / stride_w + 1;
int channels_col = channels * kernel_h * kernel_w;
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % kernel_w;
int h_offset = (c / kernel_w) % kernel_h;
int c_im = c / kernel_h / kernel_w;
for (int h = 0; h < height_col; ++h) {
for (int w = 0; w < width_col; ++w) {
int h_pad = h * stride_h - pad_t + h_offset * dilation_h;
int w_pad = w * stride_w - pad_l + w_offset * dilation_w;
if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
data_col[(c * height_col + h) * width_col + w] =
data_im[(c_im * height + h_pad) * width + w_pad];
else
data_col[(c * height_col + h) * width_col + w] = 0;
}
}
}
}
template <>
void Im2col<float, CPUContext, StorageOrder::NHWC>(
const float* data_im,
const int channels,
const int height,
const int width,
const int kernel_h,
const int kernel_w,
const int dilation_h,
const int dilation_w,
const int pad_t,
const int pad_l,
const int pad_b,
const int pad_r,
const int stride_h,
const int stride_w,
float* data_col,
CPUContext* /*context*/) {
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
int height_col = (height + pad_t + pad_b - dkernel_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - dkernel_w) / stride_w + 1;
int h_pad = -pad_t;
for (int h = 0; h < height_col; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < width_col; ++w) {
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h) {
for (int iw = w_pad; iw < w_pad + dkernel_w; iw += dilation_w) {
if (ih >= 0 && ih < height && iw >= 0 && iw < width) {
memcpy(data_col, data_im + (ih * width + iw) * channels,
sizeof(float) * channels);
} else {
// This should be simply padded with zero.
memset(data_col, 0, sizeof(float) * channels);
}
data_col += channels;
}
}
w_pad += stride_w;
}
h_pad += stride_h;
}
}
template <>
void Col2im<float, CPUContext, StorageOrder::NCHW>(
const float* data_col,
const int channels,
const int height,
const int width,
const int kernel_h,
const int kernel_w,
const int dilation_h,
const int dilation_w,
const int pad_t,
const int pad_l,
const int pad_b,
const int pad_r,
const int stride_h,
const int stride_w,
float* data_im,
CPUContext* context) {
const int output_h =
(height + pad_b + pad_t - (dilation_h * (kernel_h - 1) + 1)) / stride_h +
1;
const int output_w =
(width + pad_l + pad_r - (dilation_w * (kernel_w - 1) + 1)) / stride_w +
1;
Set<float, CPUContext>(height * width * channels, 0, data_im, context);
// Fast path for zero padding and no dilation
// From Torch, modified THNN_(unfolded_acc)
if (dilation_h == 1 && dilation_w == 1 && pad_l == 0 && pad_r == 0 &&
pad_t == 0 && pad_b == 0) {
for (auto k = 0; k < channels * kernel_h * kernel_w; k++) {
const auto nip = k / (kernel_h * kernel_w);
const auto rest = k % (kernel_h * kernel_w);
const auto kh = rest / kernel_w;
const auto kw = rest % kernel_w;
const auto* dst = data_col +
nip * (kernel_h * kernel_w * output_h * output_w) +
kh * (kernel_w * output_h * output_w) + kw * (output_h * output_w);
auto* src = data_im + nip * (height * width);
for (auto y = 0; y < output_h; y++) {
const auto iy = y * stride_h + kh;
const auto ix = kw;
if (stride_w == 1) {
auto offsrc = src + (iy * width + ix);
const auto offdst = dst + (y * output_w);
for (auto i = 0; i < output_w; ++i) {
offsrc[i] += offdst[i];
}
} else {
for (auto x = 0; x < output_w; x++) {
auto offsrc = src + (iy * width + ix + x * stride_w);
const auto offdst = dst + (y * output_w + x);
*offsrc += *offdst;
}
}
}
}
return;
}
// Fast path for equal padding
if (pad_l == pad_r && pad_t == pad_b) {
// From Intel, https://github.com/BVLC/caffe/pull/3536
const int pad_h = pad_t;
const int pad_w = pad_l;
const int channel_size = height * width;
for (int channel = channels; channel--; data_im += channel_size) {
for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
int input_row = -pad_h + kernel_row * dilation_h;
for (int output_rows = output_h; output_rows; output_rows--) {
if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
data_col += output_w;
} else {
int input_col = -pad_w + kernel_col * dilation_w;
for (int output_col = output_w; output_col; output_col--) {
if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
data_im[input_row * width + input_col] += *data_col;
}
data_col++;
input_col += stride_w;
}
}
input_row += stride_h;
}
}
}
}
return;
}
// Fallback
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
int height_col = (height + pad_t + pad_b - dkernel_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - dkernel_w) / stride_w + 1;
int channels_col = channels * kernel_h * kernel_w;
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % kernel_w;
int h_offset = (c / kernel_w) % kernel_h;
int c_im = c / kernel_h / kernel_w;
for (int h = 0; h < height_col; ++h) {
for (int w = 0; w < width_col; ++w) {
int h_pad = h * stride_h - pad_t + h_offset * dilation_h;
int w_pad = w * stride_w - pad_l + w_offset * dilation_w;
if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width) {
data_im[(c_im * height + h_pad) * width + w_pad] +=
data_col[(c * height_col + h) * width_col + w];
}
}
}
}
}
template <>
void Col2im<float, CPUContext, StorageOrder::NHWC>(
const float* data_col,
const int channels,
const int height,
const int width,
const int kernel_h,
const int kernel_w,
const int dilation_h,
const int dilation_w,
const int pad_t,
const int pad_l,
const int pad_b,
const int pad_r,
const int stride_h,
const int stride_w,
float* data_im,
CPUContext* context) {
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
Set<float, CPUContext>(height * width * channels, 0, data_im, context);
int height_col = (height + pad_t + pad_b - dkernel_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - dkernel_w) / stride_w + 1;
int h_pad = -pad_t;
for (int h = 0; h < height_col; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < width_col; ++w) {
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h) {
for (int iw = w_pad; iw < w_pad + dkernel_w; iw += dilation_w) {
if (ih >= 0 && ih < height && iw >= 0 && iw < width) {
auto* data_im_patch = data_im + (ih * width + iw) * channels;
Add<float, CPUContext>(
channels, data_im_patch, data_col, data_im_patch, context);
}
data_col += channels;
}
}
w_pad += stride_w;
}
h_pad += stride_h;
}
}
template <>
void BiasCHW<float, CPUContext>(
const float* bias,
const int bias_channels,
const int image_size,
float* image,
CPUContext* /*context*/) {
// Sum the per-channel bias into every image plane
for (int c = 0; c < bias_channels; ++c) {
float b = bias[c];
#ifdef __ARM_NEON__
float32x4_t vBias = vdupq_n_f32(b);
// We give alignment hints for additional speed, so handle the
// non-vectorizable prologue separately
constexpr int kVecSizeInFloat = sizeof(float32x4_t) / sizeof(float);
// FIXME: if input < kVecSizeInFloat, can't vectorize at all
int prologue =
kVecSizeInFloat -
// remainder in floats
(((uintptr_t) image) % (sizeof(float32x4_t))) / sizeof(float);
int i = 0;
// Prologue loop
for (; i < prologue; ++i) {
image[i] += b;
}
// The loop is manually unrolled by 8
constexpr int kUnroll = 8;
constexpr int kFloatsPerLoop = kUnroll * kVecSizeInFloat;
int remainder = image_size - prologue;
int vectorizable = prologue + (remainder / kFloatsPerLoop) * kFloatsPerLoop;
// Vectorizable body
for (; i < vectorizable; i += kFloatsPerLoop) {
// Manually unrolled
float32x4_t v0 = vld1q_f32_aligned(image + i + 0);
float32x4_t v1 = vld1q_f32_aligned(image + i + 4);
float32x4_t v2 = vld1q_f32_aligned(image + i + 8);
float32x4_t v3 = vld1q_f32_aligned(image + i + 12);
float32x4_t v4 = vld1q_f32_aligned(image + i + 16);
float32x4_t v5 = vld1q_f32_aligned(image + i + 20);
float32x4_t v6 = vld1q_f32_aligned(image + i + 24);
float32x4_t v7 = vld1q_f32_aligned(image + i + 28);
v0 = vaddq_f32(v0, vBias);
v1 = vaddq_f32(v1, vBias);
v2 = vaddq_f32(v2, vBias);
v3 = vaddq_f32(v3, vBias);
v4 = vaddq_f32(v4, vBias);
v5 = vaddq_f32(v5, vBias);
v6 = vaddq_f32(v6, vBias);
v7 = vaddq_f32(v7, vBias);
vst1q_f32_aligned(image + i + 0, v0);
vst1q_f32_aligned(image + i + 4, v1);
vst1q_f32_aligned(image + i + 8, v2);
vst1q_f32_aligned(image + i + 12, v3);
vst1q_f32_aligned(image + i + 16, v4);
vst1q_f32_aligned(image + i + 20, v5);
vst1q_f32_aligned(image + i + 24, v6);
vst1q_f32_aligned(image + i + 28, v7);
}
// Non-vectorizable epilogue
for (; i < image_size; ++i) {
image[i] += b;
}
#else
// Non-NEON CPU implementation
for (int i = 0; i < image_size; ++i) {
image[i] += b;
}
#endif // __ARM_NEON__
image += image_size;
}
}
template <>
void CopyMatrix<CPUContext>(
const size_t itemsize,
const int M,
const int N,
const void* A,
const int lda,
void* B,
const int ldb,
CPUContext* /*context*/) {
if (lda == N && ldb == N) {
// can coalese to a single memcpy of size M * N
memcpy(
static_cast<char*>(B), static_cast<const char*>(A), itemsize * N * M);
return;
}
for (int i = 0; i < M; ++i) {
memcpy(static_cast<char*>(B) + ldb * i * itemsize,
static_cast<const char*>(A) + lda * i * itemsize,
itemsize * N);
}
}
#define CAFFE2_SPECIALIZED_COPYVECTOR(T) \
template <> \
void CopyVector<T, CPUContext>( \
const int N, const T* src, T* dst, CPUContext* /*context*/) { \
if (src != dst && N > 0) { \
memcpy(dst, src, sizeof(T) * N); \
} \
}
CAFFE2_SPECIALIZED_COPYVECTOR(float)
#undef CAFFE2_SPECIALIZED_COPYVECTOR
uint32_t randomNumberSeed() {
// Originally copied from folly::randomNumberSeed (at 418ad4)
// modified to use chrono instead of sys/time.h
static std::atomic<uint32_t> seedInput(0);
auto tv = std::chrono::system_clock::now().time_since_epoch();
uint64_t usec = static_cast<uint64_t>(
std::chrono::duration_cast<std::chrono::microseconds>(tv).count());
uint32_t tv_sec = usec / 1000000;
uint32_t tv_usec = usec % 1000000;
const uint32_t kPrime0 = 51551;
const uint32_t kPrime1 = 61631;
const uint32_t kPrime2 = 64997;
const uint32_t kPrime3 = 111857;
return kPrime0 * (seedInput++) + kPrime1 * static_cast<uint32_t>(getpid()) +
kPrime2 * tv_sec + kPrime3 * tv_usec;
}
} // namespace math
} // namespace caffe2