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pytorch/caffe2/utils/math_cpu.cc
Xiaomeng Yang 265fa0ce4d Move math::Axpy function to elementwise lib (#18316) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18316

Move math::Axpy function to elementwise lib

i-am-not-moving-c2-to-c10

Reviewed By: houseroad

Differential Revision: D14574697

fbshipit-source-id: 7cfbb2da295c8966c5328bd6b577cce2638eea62
2019-03-26 12:19:19 -07:00

2694 lines
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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 "caffe2/utils/math.h"
#include <algorithm>
#include <array>
#include <atomic>
#include <chrono>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <functional>
#include <limits>
#include <numeric>
#include <random>
#include <tuple>
#include <unordered_set>
#include <vector>
#include "caffe2/core/context.h"
#include "caffe2/utils/cpu_neon.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/fixed_divisor.h"
#include "Eigen/Core"
#include "Eigen/Dense"
#ifdef CAFFE2_USE_MKL
#include <mkl.h>
#endif // CAFFE2_USE_MKL
#ifdef CAFFE2_USE_HPTT
#include <hptt.h>
#endif // CAFFE2_USE_HPTT
#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 <>
C10_EXPORT void Gemm<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
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 (trans_A) {
case CblasNoTrans: {
switch (trans_B) {
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 trans_B";
}
}
case CblasTrans: {
switch (trans_B) {
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 trans_B";
}
}
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for trans_A";
}
}
template <>
C10_EXPORT void GemmEx<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
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*) {
EigenOuterStridedMatrixMap<float> C_mat(C, N, M, EigenOuterStride(ldc));
if (beta == 0) {
C_mat.setZero();
} else {
C_mat *= beta;
}
switch (trans_A) {
case CblasNoTrans: {
switch (trans_B) {
case CblasNoTrans:
C_mat.noalias() += alpha *
(ConstEigenOuterStridedMatrixMap<float>(
B, N, K, EigenOuterStride(ldb)) *
ConstEigenOuterStridedMatrixMap<float>(
A, K, M, EigenOuterStride(lda)));
return;
case CblasTrans:
C_mat.noalias() += alpha *
(ConstEigenOuterStridedMatrixMap<float>(
B, K, N, EigenOuterStride(ldb))
.transpose() *
ConstEigenOuterStridedMatrixMap<float>(
A, K, M, EigenOuterStride(lda)));
return;
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for trans_B";
}
}
case CblasTrans: {
switch (trans_B) {
case CblasNoTrans:
C_mat.noalias() += alpha *
(ConstEigenOuterStridedMatrixMap<float>(
B, N, K, EigenOuterStride(ldb)) *
ConstEigenOuterStridedMatrixMap<float>(
A, M, K, EigenOuterStride(lda))
.transpose());
return;
case CblasTrans:
C_mat.noalias() += alpha *
(ConstEigenOuterStridedMatrixMap<float>(
B, K, N, EigenOuterStride(ldb))
.transpose() *
ConstEigenOuterStridedMatrixMap<float>(
A, M, K, EigenOuterStride(lda))
.transpose());
return;
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for trans_B";
}
}
default:
LOG(FATAL) << "Unexpected CBLAS_TRANSPOSE for trans_A";
}
}
template <>
C10_EXPORT void Gemv<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
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, trans_A == 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 (trans_A) {
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_DOT(T) \
template <> \
C10_EXPORT 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
#else // CAFFE2_USE_EIGEN_FOR_BLAS
template <>
C10_EXPORT void Gemm<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
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*/) {
const int lda = (trans_A == CblasNoTrans) ? K : M;
const int ldb = (trans_B == CblasNoTrans) ? N : K;
cblas_sgemm(
CblasRowMajor,
trans_A,
trans_B,
M,
N,
K,
alpha,
A,
lda,
B,
ldb,
beta,
C,
N);
}
template <>
C10_EXPORT void GemmEx<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
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,
trans_A,
trans_B,
M,
N,
K,
alpha,
A,
lda,
B,
ldb,
beta,
C,
ldc);
}
template <>
C10_EXPORT void Gemv<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
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, trans_A, M, N, alpha, A, N, x, 1, beta, y, 1);
}
#define CAFFE2_SPECIALIZED_DOT(T, prefix) \
template <> \
C10_EXPORT 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
#endif // CAFFE2_USE_EIGEN_FOR_BLAS
template <>
C10_EXPORT void GemmBatched<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
const int batch_size,
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 */) {
#ifdef CAFFE2_USE_MKL
(void)context;
const int lda = (trans_A == CblasNoTrans) ? K : M;
const int ldb = (trans_B == CblasNoTrans) ? N : K;
const int ldc = N;
cblas_sgemm_batch(
CblasRowMajor,
&trans_A,
&trans_B,
&M,
&N,
&K,
&alpha,
A,
&lda,
B,
&ldb,
&beta,
C,
&ldc,
1,
&batch_size);
#else // CAFFE2_USE_MKL
// loop over matrices in the batch
for (int i = 0; i < batch_size; ++i) {
math::Gemm<float, CPUContext>(
trans_A, trans_B, M, N, K, alpha, A[i], B[i], beta, C[i], context);
}
#endif // CAFFE2_USE_MKL
}
template <>
C10_EXPORT void GemmStridedBatched<float, CPUContext>(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
const int batch_size,
const int M,
const int N,
const int K,
const float alpha,
const float* A,
const int A_stride,
const float* B,
const int B_stride,
const float beta,
float* C,
const int C_stride,
CPUContext* context,
TensorProto::DataType /* math_type */) {
#ifdef CAFFE2_USE_MKL
(void)context;
const int lda = (trans_A == CblasNoTrans) ? K : M;
const int ldb = (trans_B == CblasNoTrans) ? N : K;
const int ldc = N;
std::vector<const float*> A_array(batch_size);
std::vector<const float*> B_array(batch_size);
std::vector<float*> C_array(batch_size);
for (int i = 0; i < batch_size; ++i) {
A_array[i] = A + i * A_stride;
B_array[i] = B + i * B_stride;
C_array[i] = C + i * C_stride;
}
cblas_sgemm_batch(
CblasRowMajor,
&trans_A,
&trans_B,
&M,
&N,
&K,
&alpha,
A_array.data(),
&lda,
B_array.data(),
&ldb,
&beta,
C_array.data(),
&ldc,
1,
&batch_size);
#else // CAFFE2_USE_MKL
// loop over matrices in the batch
for (int i = 0; i < batch_size; ++i) {
math::Gemm<float, CPUContext>(
trans_A, trans_B, M, N, K, alpha, A, B, beta, C, context);
A += A_stride;
B += B_stride;
C += C_stride;
}
#endif
}
////////////////////////////////////////////////////////////////////////////////
// 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.
////////////////////////////////////////////////////////////////////////////////
namespace {
template <typename T>
C10_EXPORT void BroadcastImpl(
const int X_ndim,
const int* X_dims,
const int Y_ndim,
const int* Y_dims,
const T alpha,
const T* X,
T* Y,
CPUContext* context) {
CAFFE_ENFORCE_LE(X_ndim, Y_ndim);
std::vector<int> X_dims_vector(Y_ndim);
const int d = Y_ndim - X_ndim;
std::fill(X_dims_vector.begin(), X_dims_vector.begin() + d, 1);
for (int i = d; i < Y_ndim; ++i) {
CAFFE_ENFORCE(X_dims[i - d] == 1 || X_dims[i - d] == Y_dims[i]);
X_dims_vector[i] = X_dims[i - d];
}
X_dims = X_dims_vector.data();
const int Y_size =
std::accumulate(Y_dims, Y_dims + Y_ndim, 1, std::multiplies<int>());
std::vector<int> index(Y_ndim, 0);
for (int Y_index = 0; Y_index < Y_size; ++Y_index) {
const int X_index = utils::GetIndexFromDims(Y_ndim, X_dims, index.data());
Y[Y_index] = X[X_index];
utils::IncreaseIndexInDims(Y_ndim, Y_dims, index.data());
}
Scale<T, T, CPUContext>(Y_size, alpha, Y, Y, context);
}
} // namespace
#define CAFFE2_SPECIALIZED_BROADCAST(T) \
template <> \
C10_EXPORT void Broadcast<T, CPUContext>( \
const int X_ndim, \
const int* X_dims, \
const int Y_ndim, \
const int* Y_dims, \
const T alpha, \
const T* X, \
T* Y, \
CPUContext* context) { \
BroadcastImpl<T>(X_ndim, X_dims, Y_ndim, Y_dims, alpha, X, Y, context); \
}
CAFFE2_SPECIALIZED_BROADCAST(std::int32_t)
CAFFE2_SPECIALIZED_BROADCAST(std::int64_t)
CAFFE2_SPECIALIZED_BROADCAST(float)
CAFFE2_SPECIALIZED_BROADCAST(double)
#undef CAFFE2_SPECIALIZED_BROADCAST
#define CAFFE2_SPECIALIZED_INV_STD(T) \
template <> \
void InvStd<T, CPUContext>( \
const int N, \
const T epsilon, \
const T* var, \
T* inv_std, \
CPUContext* context) { \
EigenVectorArrayMap<T>(inv_std, N) = \
(ConstEigenVectorArrayMap<T>(var, N) + epsilon).rsqrt(); \
}
CAFFE2_SPECIALIZED_INV_STD(float)
#undef CAFFE2_SPECIALIZED_INV_STD
#define CAFFE2_SPECIALIZED_ROWWISEMAX(T) \
template <> \
C10_EXPORT 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 <> \
C10_EXPORT 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_MAXIMUM(T) \
template <> \
C10_EXPORT 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
// The actual implementation uses eigen which is column major, so notice the
// row/column swap in the actual implementation.
#define DELEGATE_EIGEN_2D_BROADCAST_1ST_BINARY_FUNCTION(T, Func, expr) \
template <> \
C10_EXPORT void Rowwise##Func<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
if (C == B) { \
EigenArrayMap<T>(C, cols, rows).colwise() expr## = \
ConstEigenVectorArrayMap<T>(A, cols); \
} else { \
EigenArrayMap<T>(C, cols, rows) = \
ConstEigenArrayMap<T>(B, cols, rows) \
.colwise() expr ConstEigenVectorArrayMap<T>(A, cols); \
} \
} \
template <> \
C10_EXPORT void Colwise##Func<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
if (C == B) { \
EigenArrayMap<T>(C, cols, rows).rowwise() expr## = \
ConstEigenVectorArrayMap<T>(A, rows).transpose(); \
} else { \
EigenArrayMap<T>(C, cols, rows) = \
ConstEigenArrayMap<T>(B, cols, rows) \
.rowwise() expr ConstEigenVectorArrayMap<T>(A, rows) \
.transpose(); \
} \
}
#define DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION(T, Func, expr) \
template <> \
C10_EXPORT void Rowwise##Func<T, CPUContext, false>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
if (C == A) { \
EigenArrayMap<T>(C, cols, rows).colwise() expr## = \
ConstEigenVectorArrayMap<T>(B, cols); \
} else { \
EigenArrayMap<T>(C, cols, rows) = \
ConstEigenArrayMap<T>(A, cols, rows) \
.colwise() expr ConstEigenVectorArrayMap<T>(B, cols); \
} \
} \
template <> \
C10_EXPORT void Colwise##Func<T, CPUContext, false>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
if (C == A) { \
EigenArrayMap<T>(C, cols, rows).rowwise() expr## = \
ConstEigenVectorArrayMap<T>(B, rows).transpose(); \
} else { \
EigenArrayMap<T>(C, cols, rows) = \
ConstEigenArrayMap<T>(A, cols, rows) \
.rowwise() expr ConstEigenVectorArrayMap<T>(B, rows) \
.transpose(); \
} \
}
#define DELEGATE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(T, Func, expr) \
DELEGATE_EIGEN_2D_BROADCAST_1ST_BINARY_FUNCTION(T, Func, expr) \
DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION(T, Func, expr)
#define DEFINE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(Func, expr) \
DELEGATE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(float, Func, expr) \
DELEGATE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(double, Func, expr) \
DELEGATE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(std::int32_t, Func, expr) \
DELEGATE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(std::int64_t, Func, expr)
DEFINE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(Add, +)
DEFINE_EIGEN_2D_BROADCAST_BINARY_FUNCTION(Mul, *)
#undef DEFINE_EIGEN_2D_BROADCAST_BINARY_FUNCTION
#undef DELEGATE_EIGEN_2D_BROADCAST_BINARY_FUNCTION
#define DEFINE_EIGEN_2D_BROADCAST_SUB_FUNCTION(T) \
template <> \
C10_EXPORT void RowwiseSub<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
EigenArrayMap<T>(C, cols, rows) = \
(-ConstEigenArrayMap<T>(B, cols, rows)).colwise() + \
ConstEigenVectorArrayMap<T>(A, cols); \
} \
template <> \
C10_EXPORT void ColwiseSub<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
EigenArrayMap<T>(C, cols, rows) = \
(-ConstEigenArrayMap<T>(B, cols, rows)).rowwise() + \
ConstEigenVectorArrayMap<T>(A, rows).transpose(); \
} \
DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION(T, Sub, -)
DEFINE_EIGEN_2D_BROADCAST_SUB_FUNCTION(float)
DEFINE_EIGEN_2D_BROADCAST_SUB_FUNCTION(double)
DEFINE_EIGEN_2D_BROADCAST_SUB_FUNCTION(std::int32_t)
DEFINE_EIGEN_2D_BROADCAST_SUB_FUNCTION(std::int64_t)
#undef DEFINE_EIGEN_2D_BROADCAST_SUB_FUNCTION
#define DEFINE_EIGEN_2D_BROADCAST_DIV_FUNCTION(T) \
template <> \
C10_EXPORT void RowwiseDiv<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
EigenArrayMap<T>(C, cols, rows) = \
ConstEigenArrayMap<T>(B, cols, rows).inverse().colwise() * \
ConstEigenVectorArrayMap<T>(A, cols); \
} \
template <> \
C10_EXPORT void ColwiseDiv<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
EigenArrayMap<T>(C, cols, rows) = \
ConstEigenArrayMap<T>(B, cols, rows).inverse().rowwise() * \
ConstEigenVectorArrayMap<T>(A, rows).transpose(); \
} \
DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION(T, Div, /)
DEFINE_EIGEN_2D_BROADCAST_DIV_FUNCTION(float)
DEFINE_EIGEN_2D_BROADCAST_DIV_FUNCTION(double)
DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION(std::int32_t, Div, /)
DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION(std::int64_t, Div, /)
#undef DEFINE_EIGEN_2D_BROADCAST_DIV_FUNCTION
#undef DELEGATE_EIGEN_2D_BROADCAST_1ST_BINARY_FUNCTION
#undef DELEGATE_EIGEN_2D_BROADCAST_2ND_BINARY_FUNCTION
template <>
C10_EXPORT 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 C10_DEFINE_BINARY_OP
#undef CAFFE2_INSTANTIATE_BINARY_OP
#define CAFFE2_SPECIALIZED_CPU_ADD_STRIPED_BATCH(T) \
template <> \
C10_EXPORT 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
namespace {
template <typename TIn, typename TOut, class BinaryOperator, bool kBroadcast1st>
C10_EXPORT void RowwiseBinaryOp(
const int rows,
const int cols,
const BinaryOperator& op,
const TIn* A,
const TIn* B,
TOut* C) {
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < cols; ++j) {
const int C_index = i * cols + j;
const int A_index = kBroadcast1st ? j : C_index;
const int B_index = kBroadcast1st ? C_index : j;
C[C_index] = op(A[A_index], B[B_index]);
}
}
}
template <typename TIn, typename TOut, class BinaryOperator, bool kBroadcast1st>
C10_EXPORT void ColwiseBinaryOp(
const int rows,
const int cols,
const BinaryOperator& op,
const TIn* A,
const TIn* B,
TOut* C) {
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < cols; ++j) {
const int C_index = i * cols + j;
const int A_index = kBroadcast1st ? i : C_index;
const int B_index = kBroadcast1st ? C_index : i;
C[C_index] = op(A[A_index], B[B_index]);
}
}
}
template <typename TIn, typename TOut, class BinaryOperator>
C10_EXPORT void BroadcastBinaryOpImpl(
const int ndim,
const int* A_dims,
const int* B_dims,
const int* C_dims,
const BinaryOperator& op,
const TIn* A,
const TIn* B,
TOut* C) {
std::vector<int> index(ndim, 0);
const int C_size =
std::accumulate(C_dims, C_dims + ndim, 1, std::multiplies<int>());
for (int C_index = 0; C_index < C_size; ++C_index) {
const int A_index = utils::GetIndexFromDims(ndim, A_dims, index.data());
const int B_index = utils::GetIndexFromDims(ndim, B_dims, index.data());
C[C_index] = op(A[A_index], B[B_index]);
utils::IncreaseIndexInDims(ndim, C_dims, index.data());
}
}
} // namespace
#define DELEGATE_2D_BROADCAST_BINARY_FUNCTION(TIn, TOut, Func, Op) \
template <> \
C10_EXPORT void Rowwise##Func<TIn, CPUContext, true>( \
const int rows, \
const int cols, \
const TIn* A, \
const TIn* B, \
TOut* C, \
CPUContext*) { \
RowwiseBinaryOp<TIn, TOut, Op<TIn>, true>(rows, cols, Op<TIn>(), A, B, C); \
} \
template <> \
C10_EXPORT void Rowwise##Func<TIn, CPUContext, false>( \
const int rows, \
const int cols, \
const TIn* A, \
const TIn* B, \
TOut* C, \
CPUContext*) { \
RowwiseBinaryOp<TIn, TOut, Op<TIn>, false>( \
rows, cols, Op<TIn>(), A, B, C); \
} \
template <> \
C10_EXPORT void Colwise##Func<TIn, CPUContext, true>( \
const int rows, \
const int cols, \
const TIn* A, \
const TIn* B, \
TOut* C, \
CPUContext*) { \
ColwiseBinaryOp<TIn, TOut, Op<TIn>, true>(rows, cols, Op<TIn>(), A, B, C); \
} \
template <> \
C10_EXPORT void Colwise##Func<TIn, CPUContext, false>( \
const int rows, \
const int cols, \
const TIn* A, \
const TIn* B, \
TOut* C, \
CPUContext*) { \
ColwiseBinaryOp<TIn, TOut, Op<TIn>, false>( \
rows, cols, Op<TIn>(), A, B, C); \
}
#define DEFINE_2D_COMPARE_FUNCTION(Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(float, bool, Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(double, bool, Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(std::int32_t, bool, Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(std::int64_t, bool, Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(bool, bool, Func, Op)
DEFINE_2D_COMPARE_FUNCTION(EQ, std::equal_to)
DEFINE_2D_COMPARE_FUNCTION(NE, std::not_equal_to)
DEFINE_2D_COMPARE_FUNCTION(LT, std::less)
DEFINE_2D_COMPARE_FUNCTION(LE, std::less_equal)
DEFINE_2D_COMPARE_FUNCTION(GT, std::greater)
DEFINE_2D_COMPARE_FUNCTION(GE, std::greater_equal)
#undef DEFINE_2D_COMPARE_FUNCTION
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(bool, bool, And, std::logical_and)
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(bool, bool, Or, std::logical_or)
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(bool, bool, Xor, std::bit_xor)
#define DEFINE_2D_BROADCAST_BITWISE_BINARY_FUNCTION(Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(bool, bool, Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(std::int32_t, std::int32_t, Func, Op) \
DELEGATE_2D_BROADCAST_BINARY_FUNCTION(std::int64_t, std::int64_t, Func, Op)
DEFINE_2D_BROADCAST_BITWISE_BINARY_FUNCTION(BitwiseAnd, std::bit_and)
DEFINE_2D_BROADCAST_BITWISE_BINARY_FUNCTION(BitwiseOr, std::bit_or)
DEFINE_2D_BROADCAST_BITWISE_BINARY_FUNCTION(BitwiseXor, std::bit_xor)
#undef DEFINE_2D_BROADCAST_BITWISE_BINARY_FUNCTION
#undef DELEGATE_2D_BROADCAST_BINARY_FUNCTION
#define DEFINE_2D_BROADCAST_1ST_DIV_FUNCTION(T) \
template <> \
C10_EXPORT void RowwiseDiv<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
RowwiseBinaryOp<T, T, std::divides<T>, true>( \
rows, cols, std::divides<T>(), A, B, C); \
} \
template <> \
C10_EXPORT void ColwiseDiv<T, CPUContext, true>( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
CPUContext*) { \
ColwiseBinaryOp<T, T, std::divides<T>, true>( \
rows, cols, std::divides<T>(), A, B, C); \
}
DEFINE_2D_BROADCAST_1ST_DIV_FUNCTION(std::int32_t)
DEFINE_2D_BROADCAST_1ST_DIV_FUNCTION(std::int64_t)
#undef DEFINE_2D_BROADCAST_1ST_DIV_FUNCTION
#define DELEGATE_BROADCAST_BINARY_FUNCTION(TIn, TOut, Func, Op) \
template <> \
C10_EXPORT void Func<TIn, CPUContext>( \
const int A_ndim, \
const int* A_dims, \
const int B_ndim, \
const int* B_dims, \
const TIn* A, \
const TIn* B, \
TOut* C, \
CPUContext* context) { \
const int ndim = std::max(A_ndim, B_ndim); \
std::vector<int> A_dims_array(ndim); \
std::vector<int> B_dims_array(ndim); \
std::vector<int> C_dims_array(ndim); \
utils::ComputeBroadcastBinaryOpDims( \
A_ndim, \
A_dims, \
B_ndim, \
B_dims, \
A_dims_array.data(), \
B_dims_array.data(), \
C_dims_array.data()); \
if (A_dims_array == B_dims_array) { \
const int size = std::accumulate( \
C_dims_array.cbegin(), \
C_dims_array.cend(), \
1, \
std::multiplies<int>()); \
Func<TIn, CPUContext>(size, A, B, C, context); \
return; \
} \
int rows; \
int cols; \
bool broadcast_1st; \
if (utils::IsRowwiseBroadcastBinaryOp( \
ndim, \
A_dims_array.data(), \
B_dims_array.data(), \
&rows, \
&cols, \
&broadcast_1st)) { \
if (broadcast_1st) { \
Rowwise##Func<TIn, CPUContext, true>(rows, cols, A, B, C, context); \
} else { \
Rowwise##Func<TIn, CPUContext, false>(rows, cols, A, B, C, context); \
} \
return; \
} \
if (utils::IsColwiseBroadcastBinaryOp( \
ndim, \
A_dims_array.data(), \
B_dims_array.data(), \
&rows, \
&cols, \
&broadcast_1st)) { \
if (broadcast_1st) { \
Colwise##Func<TIn, CPUContext, true>(rows, cols, A, B, C, context); \
} else { \
Colwise##Func<TIn, CPUContext, false>(rows, cols, A, B, C, context); \
} \
return; \
} \
int pre; \
int mid; \
int nxt; \
if (utils::IsBothEndsBroadcastBinaryOp( \
ndim, \
A_dims_array.data(), \
B_dims_array.data(), \
&pre, \
&mid, \
&nxt, \
&broadcast_1st)) { \
const int stride = mid * nxt; \
for (int i = 0; i < pre; ++i) { \
if (broadcast_1st) { \
Colwise##Func<TIn, CPUContext, true>( \
mid, nxt, A, B + i * stride, C + i * stride, context); \
} else { \
Colwise##Func<TIn, CPUContext, false>( \
mid, nxt, A + i * stride, B, C + i * stride, context); \
} \
} \
return; \
} \
BroadcastBinaryOpImpl( \
ndim, \
A_dims_array.data(), \
B_dims_array.data(), \
C_dims_array.data(), \
Op<TIn>(), \
A, \
B, \
C); \
}
#define DEFINE_BROADCAST_COMPARE_FUNCTION(Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(float, bool, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(double, bool, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(std::int32_t, bool, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(std::int64_t, bool, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(bool, bool, Func, Op)
DEFINE_BROADCAST_COMPARE_FUNCTION(EQ, std::equal_to)
DEFINE_BROADCAST_COMPARE_FUNCTION(NE, std::not_equal_to)
DEFINE_BROADCAST_COMPARE_FUNCTION(LT, std::less)
DEFINE_BROADCAST_COMPARE_FUNCTION(LE, std::less_equal)
DEFINE_BROADCAST_COMPARE_FUNCTION(GT, std::greater)
DEFINE_BROADCAST_COMPARE_FUNCTION(GE, std::greater_equal)
#undef DEFINE_BROADCAST_COMPARE_FUNCTION
#define DEFINE_BROADCAST_BINARY_FUNCTION(Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(float, float, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(double, double, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(std::int32_t, std::int32_t, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(std::int64_t, std::int64_t, Func, Op)
DEFINE_BROADCAST_BINARY_FUNCTION(Add, std::plus)
DEFINE_BROADCAST_BINARY_FUNCTION(Sub, std::minus)
DEFINE_BROADCAST_BINARY_FUNCTION(Mul, std::multiplies)
DEFINE_BROADCAST_BINARY_FUNCTION(Div, std::divides)
#undef DEFINE_BROADCAST_BINARY_FUNCTION
DELEGATE_BROADCAST_BINARY_FUNCTION(bool, bool, And, std::logical_and)
DELEGATE_BROADCAST_BINARY_FUNCTION(bool, bool, Or, std::logical_or)
DELEGATE_BROADCAST_BINARY_FUNCTION(bool, bool, Xor, std::bit_xor)
#define DEFINE_BROADCAST_BITWISE_BINARY_FUNCTION(Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(bool, bool, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(std::int32_t, std::int32_t, Func, Op) \
DELEGATE_BROADCAST_BINARY_FUNCTION(std::int64_t, std::int64_t, Func, Op)
DEFINE_BROADCAST_BITWISE_BINARY_FUNCTION(BitwiseAnd, std::bit_and)
DEFINE_BROADCAST_BITWISE_BINARY_FUNCTION(BitwiseOr, std::bit_or)
DEFINE_BROADCAST_BITWISE_BINARY_FUNCTION(BitwiseXor, std::bit_xor)
#undef DEFINE_BITWISE_BROADCAST_BINARY_FUNCTION
#undef DELEGATE_BROADCAST_BINARY_FUNCTION
#define CAFFE2_RAND_UNIFORM_REAL(T) \
template <> \
C10_EXPORT void RandUniform<T, CPUContext>( \
const size_t n, const T a, const T b, T* r, CPUContext* context) { \
std::uniform_real_distribution<T> distribution(a, b); \
for (size_t i = 0; i < n; ++i) { \
r[i] = distribution(context->RandGenerator()); \
} \
}
CAFFE2_RAND_UNIFORM_REAL(float);
CAFFE2_RAND_UNIFORM_REAL(double);
#undef CAFFE2_RAND_UNIFORM_REAL
#define CAFFE2_RAND_UNIFORM_CHAR(T) \
template <> \
C10_EXPORT void RandUniform<T, CPUContext>( \
const size_t n, const T a, const T b, T* r, CPUContext* context) { \
std::uniform_int_distribution<short> distribution((short)a, (short)b); \
for (size_t i = 0; i < n; ++i) { \
r[i] = static_cast<T>(distribution(context->RandGenerator())); \
} \
}
CAFFE2_RAND_UNIFORM_CHAR(int8_t);
CAFFE2_RAND_UNIFORM_CHAR(uint8_t);
#undef CAFFE2_RAND_UNIFORM_CHAR
#define CAFFE2_RAND_UNIFORM_INT(T) \
template <> \
C10_EXPORT void RandUniform<T, CPUContext>( \
const size_t n, const T a, const T b, T* r, CPUContext* context) { \
std::uniform_int_distribution<T> distribution(a, b); \
for (size_t i = 0; i < n; ++i) { \
r[i] = distribution(context->RandGenerator()); \
} \
}
CAFFE2_RAND_UNIFORM_INT(int16_t);
CAFFE2_RAND_UNIFORM_INT(int32_t);
CAFFE2_RAND_UNIFORM_INT(int64_t);
CAFFE2_RAND_UNIFORM_INT(uint16_t);
CAFFE2_RAND_UNIFORM_INT(uint32_t);
CAFFE2_RAND_UNIFORM_INT(uint64_t);
#undef CAFFE2_RAND_UNIFORM_INT
// This is not uniformly distributed between a and b.
// It takes advantage of normal distribution to generate numbers
// with mean = sum / n.
// Ideally the algorithm should be generating n numbers between 0 and 1,
// sum them up as scaled_sum, and use sum / scaled_sum to adjust the values
// to between a and b.
// The algorithm is non-trivial given the adjustment would be different towards
// each value.
#define CAFFE2_RAND_FIXED_SUM(T) \
template <> \
C10_EXPORT void RandFixedSum<T, CPUContext>( \
const size_t n, \
const T a, \
const T b, \
const T sum, \
T* r, \
CPUContext* context) { \
CAFFE_ENFORCE_GE(a, 0); \
CAFFE_ENFORCE_GE(sum / (double)n, a); \
CAFFE_ENFORCE_LE(sum / (double)n, b); \
T current_sum = 0; \
T remaining_sum = sum; \
for (size_t i = 0; i < n; ++i) { \
auto remaining_numbers = n - 1 - i; \
double mean = (sum - current_sum) / (remaining_numbers + 1); \
double stdev = std::min(mean - a, b - mean); \
std::normal_distribution<double> distribution{mean, stdev / 4.0}; \
T value, remaining_sum_test; \
do { \
value = distribution(context->RandGenerator()); \
remaining_sum_test = remaining_sum - value; \
} while (value < a || remaining_sum_test < a * remaining_numbers || \
value > b || remaining_sum_test > b * remaining_numbers); \
r[i] = value; \
CAFFE_ENFORCE(a <= value && value <= b); \
current_sum += value; \
remaining_sum -= value; \
CAFFE_ENFORCE_GE(remaining_sum, a* remaining_numbers); \
CAFFE_ENFORCE_LE(remaining_sum, b* remaining_numbers); \
} \
r[n - 1] += remaining_sum; \
current_sum += remaining_sum; \
CAFFE_ENFORCE(a <= r[n - 1] && r[n - 1] <= b); \
CAFFE_ENFORCE_EQ(current_sum, sum); \
}
CAFFE2_RAND_FIXED_SUM(float);
CAFFE2_RAND_FIXED_SUM(double);
CAFFE2_RAND_FIXED_SUM(int8_t);
CAFFE2_RAND_FIXED_SUM(int16_t);
CAFFE2_RAND_FIXED_SUM(int32_t);
CAFFE2_RAND_FIXED_SUM(int64_t);
CAFFE2_RAND_FIXED_SUM(uint8_t);
CAFFE2_RAND_FIXED_SUM(uint16_t);
CAFFE2_RAND_FIXED_SUM(uint32_t);
CAFFE2_RAND_FIXED_SUM(uint64_t);
#undef CAFFE2_RAND_FIXED_SUM
template <class Type, class Val_t, class Ind_t, class Context_t, bool cdf_app>
Ind_t generate_stack_distance(
std::vector<Ind_t>& cum_val,
std::vector<Val_t>& cum_dis,
std::vector<Ind_t>& cum_map,
Ind_t max_i,
Ind_t i,
Context_t* context) {
/* Description:
Inverse Transform Sampling method to generate values for random variable X
that is described by the cumulative distribution F (cum_val,cum_dis).
Notice, that we may choose to use the inverse map of F (cum_map) as an
approximation to avoid searching. Also, scaling the probability so that
the values are within max_i refs, because stack distance can not be >
than the # of already generated refs (max_i).
*/
Ind_t j, k, n;
Val_t u, f, fi;
// generate a random number u in [0,1] from a uniform distribution U
math::RandUniform<Val_t, Context_t>(1, 0, 1, &u, context);
// scale the random number u to be within range [0,f(i)], if needed
if (i < max_i) {
// approach 2: allows gaps in the distribution
j = (std::upper_bound(cum_val.begin(), cum_val.end(), i) -
cum_val.begin()) -
1;
fi = cum_dis[j];
u *= fi;
}
// 2. compute the stack distance value of x, s.t. F(x)=u
// notice that the cumulative distribution F increases monotonically up to 1
if (cdf_app) {
// look up cum_val corresponding to u <= cum_dis[j]
k = cum_map.size();
n = (Ind_t)round(u * k);
j = cum_map[n];
return cum_val[j];
} else {
// iterate until you find the cum_val corresponding to u <= cum_dis[j]
for (j = 0; j < cum_dis.size(); j++) {
f = cum_dis[j];
if (u <= f) {
return cum_val[j];
}
}
return cum_val[j - 1];
}
}
template <class Type, class Val_t, class Ind_t, class Context_t, bool cdf_app>
C10_EXPORT void generate_trace_lru(
std::vector<Ind_t>& uni_ref,
std::vector<Ind_t>& cum_val,
std::vector<Val_t>& cum_dis,
std::vector<Ind_t>& cum_map,
Context_t* context,
Ind_t cache_line_size,
Ind_t n,
Type min,
Type max,
Type* syn_ref) {
/* Description:
Generate synthetic trace from a list of unique accesses uni_ref, and
cumulative distribution of distances (cum_val,cum_dis) between them.
Also, there is an option to use cum_map approximation to avoid searching.
*/
Ind_t i, j, k, sd, line_ref, mem_ref, mem_ref_within_line;
Ind_t max_sd = cum_val.back();
Ind_t l = uni_ref.size();
for (i = 0, j = 0; j < n; j++) {
// generate stack distance
sd = generate_stack_distance<Type, Val_t, Ind_t, Context_t, cdf_app>(
cum_val, cum_dis, cum_map, max_sd, i, context);
// fixed access within cache line
mem_ref_within_line = 0;
// random access within cache line
// Val_t r;
// math::RandUniform<Val_t, Context_t>(1, 0, 1, &r, context);
// mem_ref_within_line = floor(r*cache_line_size);
// generate memory reference
if (sd == 0) {
k = 0; /// new reference ///
i++;
} else {
k = l - sd; /// existing reference ///
}
line_ref = uni_ref[k]; // pop k-th element
uni_ref.erase(uni_ref.begin() + k);
uni_ref.push_back(line_ref); // append it back
mem_ref = line_ref * cache_line_size + mem_ref_within_line;
/*
//debug prints
if ((mem_ref < min) || (mem_ref > max)) {
//printf("mem_ref[%d]=%d (%ld) \n",j,mem_ref,syn_ref[j]);
std::cout << "syn_ref[" << j << "]=" << (Type)mem_ref << " ";
std::cout << "(" << mem_ref << ") ";
std::cout << "[" << min << "," << max << "]" << std::endl;
int scanf_temp;
scanf("%d",&scanf_temp);
}
*/
// patch mem_ref to be within range
// WARNING: this should not be needed if instantiation type and distribution
// choice is correct. It is remeding a symptom of earlier mistakes.
if (mem_ref < min) {
mem_ref = min;
// std::cout << "clamping (min) mem_ref=" << mem_ref << std::endl;
}
if (mem_ref > max) {
mem_ref = max; // mem_ref % max;
// std::cout << "clamping (max) mem_ref=" << mem_ref << std::endl;
}
// save generated memory reference
syn_ref[j] = (Type)mem_ref;
}
}
// Generate n values from synthetic data distribution,
// define by unique accesses and stack distances
// WARNING: can create this for all tables or per table, but in latter
// case we need to know the table id, to sample from the right distribution
#define CAFFE2_RAND_SYNTHETIC_DATA(T) \
template <> \
C10_EXPORT void RandSyntheticData<T, CPUContext>( \
const size_t n, const T a, const T b, T* r, CPUContext* context) { \
/* unique memory references */ \
std::vector<int> mem_ref = {1, 2, 3, 4, 5, 6}; \
/* cumulative distribution of distances */ \
std::vector<int> cum_val = {0, 1, 3, 4, 5}; \
std::vector<double> cum_dis = {0.55, 0.64, 0.82, 0.91, 1.0}; \
/* inverse map of cumulative distribution (for O(1) lookup) */ \
/* std::vector<int> cum_map = {0, 0, 0, 0, 0, 1, 2, 2, 3, 4}; */ \
int k = 10; /* 100; */ \
std::vector<int> cum_map(k, 0); \
for (int j = 0; j < cum_dis.size();) { \
int sz = (int)round(cum_dis[j] * k); \
for (int i = 0; i < sz; i++) { \
cum_map[j + i] = j; \
} \
j += sz; \
} \
\
/* code to generate the synthetic data from the above values */ \
const int cache_line = 1; /* 64; */ \
generate_trace_lru<T, double, int, CPUContext, false>( \
mem_ref, cum_val, cum_dis, cum_map, context, cache_line, n, a, b, r); \
}
CAFFE2_RAND_SYNTHETIC_DATA(float);
CAFFE2_RAND_SYNTHETIC_DATA(double);
CAFFE2_RAND_SYNTHETIC_DATA(int8_t);
CAFFE2_RAND_SYNTHETIC_DATA(int16_t);
CAFFE2_RAND_SYNTHETIC_DATA(int32_t);
CAFFE2_RAND_SYNTHETIC_DATA(int64_t);
CAFFE2_RAND_SYNTHETIC_DATA(uint8_t);
CAFFE2_RAND_SYNTHETIC_DATA(uint16_t);
CAFFE2_RAND_SYNTHETIC_DATA(uint32_t);
CAFFE2_RAND_SYNTHETIC_DATA(uint64_t);
#undef CAFFE2_RAND_SYNTHETIC_DATA
#define CAFFE2_SPECIALIZED_RAND_UNIFORM_UNIQUE(T) \
template <> \
C10_EXPORT 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(), "AC10_EXPORT void 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 <>
C10_EXPORT void RandGaussian<float, CPUContext>(
const size_t n,
const float mean,
const float std,
float* r,
CPUContext* context) {
std::normal_distribution<float> distribution(mean, std);
for (size_t i = 0; i < n; ++i) {
r[i] = distribution(context->RandGenerator());
}
}
#define CAFFE2_SPECIALIZED_SUM(T) \
template <> \
C10_EXPORT void Sum<T, CPUContext>( \
const int N, \
const T* x, \
T* y, \
CPUContext* /* unused */, \
Tensor* /* 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 <>
C10_EXPORT void SumSqr<float, CPUContext>(
const int N,
const float* x,
float* y,
CPUContext* /*context*/ /* unused */,
Tensor* /*scratch_ptr*/ /* unused */) {
*y = ConstEigenVectorMap<float>(x, N).squaredNorm();
}
template <>
C10_EXPORT 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]];
}
}
template <>
C10_EXPORT 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*/,
TypeMeta::Copy copy) {
if (A == nullptr || B == nullptr) {
return;
}
if (lda == N && ldb == N) {
// can coalese to a single memcpy of size M * N
if (copy) {
copy(static_cast<const char*>(A), static_cast<char*>(B), N * M);
} else {
memcpy(
static_cast<char*>(B), static_cast<const char*>(A), itemsize * N * M);
}
return;
}
for (int i = 0; i < M; ++i) {
if (copy) {
copy(
static_cast<const char*>(A) + lda * i * itemsize,
static_cast<char*>(B) + ldb * i * itemsize,
N);
} else {
memcpy(
static_cast<char*>(B) + ldb * i * itemsize,
static_cast<const char*>(A) + lda * i * itemsize,
itemsize * N);
}
}
}
#ifdef CAFFE2_USE_MKL
#define DELEGATE_COPY_MATRIX_FUNCTION(T, Func) \
template <> \
C10_EXPORT void CopyMatrix<T, CPUContext>( \
const int M, \
const int N, \
const T* A, \
const int lda, \
T* B, \
const int ldb, \
CPUContext* /* context */) { \
Func('R', 'N', M, N, T(1), A, lda, B, ldb); \
} \
template <> \
C10_EXPORT void CopyMatrix<T, CPUContext>( \
const int M, \
const int N, \
const T* A, \
const int A_outer_stride, \
const int A_inner_stride, \
T* B, \
const int B_outer_stride, \
const int B_inner_stride, \
CPUContext* /* context */) { \
Func##2( \
'R', \
'N', \
M, \
N, \
T(1), \
A, \
A_outer_stride, \
A_inner_stride, \
B, \
B_outer_stride, \
B_inner_stride); \
}
DELEGATE_COPY_MATRIX_FUNCTION(float, mkl_somatcopy)
DELEGATE_COPY_MATRIX_FUNCTION(double, mkl_domatcopy)
#undef DELEGATE_COPY_MATRIX_FUNCTION
#endif // CAFFE2_USE_MKL
#define CAFFE2_SPECIALIZED_COPY_MATRIX(T) \
template <> \
C10_EXPORT void CopyMatrix<T, CPUContext>( \
const int M, \
const int N, \
const T* A, \
const int lda, \
T* B, \
const int ldb, \
CPUContext* /* context */) { \
if (M == 0 || N == 0) { \
return; \
} \
if (lda == N) { \
if (ldb == N) { \
std::memcpy(B, A, sizeof(T) * M * N); \
} else { \
EigenOuterStridedMatrixMap<T>(B, N, M, EigenOuterStride(ldb)) = \
ConstEigenMatrixMap<T>(A, N, M); \
} \
} else { \
if (ldb == N) { \
EigenMatrixMap<T>(B, N, M) = ConstEigenOuterStridedMatrixMap<T>( \
A, N, M, EigenOuterStride(lda)); \
} else { \
EigenOuterStridedMatrixMap<T>(B, N, M, EigenOuterStride(ldb)) = \
ConstEigenOuterStridedMatrixMap<T>( \
A, N, M, EigenOuterStride(lda)); \
} \
} \
} \
template <> \
C10_EXPORT void CopyMatrix<T, CPUContext>( \
const int M, \
const int N, \
const T* A, \
const int A_outer_stride, \
const int A_inner_stride, \
T* B, \
const int B_outer_stride, \
const int B_inner_stride, \
CPUContext* context) { \
if (A_inner_stride == 1 && B_inner_stride == 1) { \
CopyMatrix<T, CPUContext>( \
M, N, A, A_outer_stride, B, B_outer_stride, context); \
return; \
} \
EigenStridedMatrixMap<T>( \
B, N, M, EigenStride(B_outer_stride, B_inner_stride)) = \
ConstEigenStridedMatrixMap<T>( \
A, N, M, EigenStride(A_outer_stride, A_inner_stride)); \
}
#ifndef CAFFE2_USE_MKL
CAFFE2_SPECIALIZED_COPY_MATRIX(float)
CAFFE2_SPECIALIZED_COPY_MATRIX(double)
#endif // CAFFE2_USE_MKL
CAFFE2_SPECIALIZED_COPY_MATRIX(int)
CAFFE2_SPECIALIZED_COPY_MATRIX(int64_t)
CAFFE2_SPECIALIZED_COPY_MATRIX(std::uint8_t)
CAFFE2_SPECIALIZED_COPY_MATRIX(std::uint16_t)
#undef CAFFE2_SPECIALIZXED_COPY_MATRIX
namespace {
template <typename T>
C10_EXPORT void Im2ColZeroPaddingAndNoDilationNCHW(
const int C,
const int H,
const int W,
const int kernel_h,
const int kernel_w,
const int stride_h,
const int stride_w,
const T* img_data,
T* col_data,
CPUContext* context) {
const int output_h = (H - kernel_h) / stride_h + 1;
const int output_w = (W - kernel_w) / stride_w + 1;
const int output_size = output_h * output_w;
for (int c = 0; c < C; ++c) {
for (int kh = 0; kh < kernel_h; ++kh) {
for (int kw = 0; kw < kernel_w; ++kw) {
const T* src = img_data + kh * W + kw;
if (stride_w == 1) {
CopyMatrix<T, CPUContext>(
output_h,
output_w,
src,
stride_h * W,
col_data,
output_w,
context);
} else {
CopyMatrix<T, CPUContext>(
output_h,
output_w,
src,
stride_h * W,
stride_w,
col_data,
output_w,
1,
context);
}
col_data += output_size;
}
}
img_data += H * W;
}
}
template <typename T>
C10_EXPORT void Col2ImZeroPaddingAndNoDilationNCHW(
const int C,
const int H,
const int W,
const int kernel_h,
const int kernel_w,
const int stride_h,
const int stride_w,
const T* col_data,
T* img_data,
CPUContext* context) {
Set<T, CPUContext>(C * H * W, T(0), img_data, context);
const int output_h = (H - kernel_h) / stride_h + 1;
const int output_w = (W - kernel_w) / stride_w + 1;
const int output_size = output_h * output_w;
for (int c = 0; c < C; ++c) {
for (int kh = 0; kh < kernel_h; ++kh) {
for (int kw = 0; kw < kernel_w; ++kw) {
T* dst = img_data + kh * W + kw;
if (stride_w == 1) {
EigenOuterStridedArrayMap<T>(
dst, output_w, output_h, EigenOuterStride(stride_h * W)) +=
ConstEigenArrayMap<T>(col_data, output_w, output_h);
} else {
EigenStridedArrayMap<T>(
dst, output_w, output_h, EigenStride(stride_h * W, stride_w)) +=
ConstEigenArrayMap<T>(col_data, output_w, output_h);
}
col_data += output_size;
}
}
img_data += H * W;
}
}
template <typename T>
C10_EXPORT void Im2ColZeroPaddingAndNoDilationNHWC(
const int C,
const int H,
const int W,
const int kernel_h,
const int kernel_w,
const int stride_h,
const int stride_w,
const T* img_data,
T* col_data,
CPUContext* context) {
const int output_h = (H - kernel_h) / stride_h + 1;
const int output_w = (W - kernel_w) / stride_w + 1;
const int kernel_size = kernel_h * kernel_w;
for (int yh = 0; yh < output_h; ++yh) {
for (int yw = 0; yw < output_w; ++yw) {
const T* src = img_data + (yh * stride_h * W + yw * stride_w) * C;
CopyMatrix<T, CPUContext>(
kernel_h, kernel_w * C, src, W * C, col_data, kernel_w * C, context);
col_data += kernel_size * C;
}
}
}
template <typename T>
C10_EXPORT void Col2ImZeroPaddingAndNoDilationNHWC(
const int C,
const int H,
const int W,
const int kernel_h,
const int kernel_w,
const int stride_h,
const int stride_w,
const T* col_data,
T* img_data,
CPUContext* context) {
Set<T, CPUContext>(H * W * C, T(0), img_data, context);
const int output_h = (H - kernel_h) / stride_h + 1;
const int output_w = (W - kernel_w) / stride_w + 1;
const int kernel_size = kernel_h * kernel_w;
for (int yh = 0; yh < output_h; ++yh) {
for (int yw = 0; yw < output_w; ++yw) {
T* dst = img_data + (yh * stride_h * W + yw * stride_w) * C;
EigenOuterStridedArrayMap<T>(
dst, kernel_w * C, kernel_h, EigenOuterStride(W * C)) +=
ConstEigenArrayMap<T>(col_data, kernel_w * C, kernel_h);
col_data += kernel_size * C;
}
}
}
template <typename T, bool kCol2Im>
C10_EXPORT void Im2ColNdNCHWImpl(
const int N,
const int img_size,
const int col_size,
const int* img_shape,
const int* col_shape,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const float* X_data,
float* Y_data) {
if (kCol2Im) {
std::memset(Y_data, 0, img_size * sizeof(float));
}
const int outer_size = col_shape[0];
const int inner_size = col_size / outer_size;
const int kernel_size = std::accumulate(
kernel_shape, kernel_shape + N, 1, std::multiplies<int>());
std::vector<FixedDivisor<int>> kernel_shape_div(N);
for (int i = 0; i < N; ++i) {
kernel_shape_div[i] = FixedDivisor<int>(kernel_shape[i]);
}
std::vector<int> d_offset(N, 0);
std::vector<int> d_iter(N, 0);
for (int i = 0; i < outer_size; ++i) {
// Loop over spatial axes in reverse order to compute a per-axis offset.
int offset = i;
for (int d_i = N - 1; d_i >= 0; --d_i) {
kernel_shape_div[d_i].DivMod(offset, &offset, &d_offset[d_i]);
}
for (int j = 0; j < inner_size; ++j) {
// Loop over spatial axes in forward order to compute the indices in the
// image and column, and whether the index lies in the padding.
const int col_index = i * inner_size + j;
int img_index = i / kernel_size;
bool is_padding = false;
for (int d_i = 0; d_i < N; ++d_i) {
const int d_img = d_iter[d_i] * stride[d_i] - pad[d_i] +
d_offset[d_i] * dilation[d_i];
is_padding |= !utils::IsAGeZeroAndALtB(d_img, img_shape[d_i + 1]);
img_index = img_index * img_shape[d_i + 1] + d_img;
}
if (!kCol2Im) {
Y_data[col_index] = is_padding ? 0 : X_data[img_index];
} else if (!is_padding) {
Y_data[img_index] += X_data[col_index];
}
utils::IncreaseIndexInDims(N, col_shape + 1, d_iter.data());
}
}
}
template <typename T>
void Im2Col3dNCHWImpl(
const int channels,
const int clip_len,
const int height,
const int width,
const int kernel_t,
const int kernel_h,
const int kernel_w,
const int dilation_t,
const int dilation_h,
const int dilation_w,
const int pad_p,
const int pad_t,
const int pad_l,
const int pad_a,
const int pad_b,
const int pad_r,
const int stride_t,
const int stride_h,
const int stride_w,
const T* img_data,
T* col_data) {
const int output_t =
(clip_len + pad_p + pad_a - (dilation_t * (kernel_t - 1) + 1)) /
stride_t +
1;
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;
const int kernel_size = kernel_t * kernel_h * kernel_w;
const int kernel_hw_size = kernel_h * kernel_w;
const int output_size = output_t * output_h * output_w;
const int channel_size = clip_len * height * width;
const int output_hw_size = output_h * output_w;
const int channel_hw_size = height * width;
// Fast path for zero padding and no dilation
// From Torch, THNN_(unfolded_copy)
if (dilation_t == 1 && dilation_h == 1 && dilation_w == 1 && pad_a == 0 &&
pad_p == 0 && pad_l == 0 && pad_r == 0 && pad_t == 0 && pad_b == 0) {
for (auto k = 0; k < channels * kernel_size; k++) {
const auto nip = k / kernel_size;
const auto rest = k % kernel_size;
const auto kt = rest / kernel_hw_size;
const auto rest_hw = rest % kernel_hw_size;
const auto kh = rest_hw / kernel_w;
const auto kw = rest_hw % kernel_w;
auto* dst = col_data + nip * (kernel_size * output_size) +
kt * (kernel_hw_size * output_size) + kh * (kernel_w * output_size) +
kw * output_size;
const auto* src = img_data + nip * channel_size;
for (auto t = 0; t < output_t; t++) {
const auto it = t * stride_t + kt;
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 + (t * output_hw_size + y * output_w),
src + (it * channel_hw_size + iy * width + ix),
sizeof(T) * output_w);
} else {
for (auto x = 0; x < output_w; x++) {
memcpy(
dst + (t * output_hw_size + y * output_w + x),
src + (it * channel_hw_size + iy * width + ix + x * stride_w),
sizeof(T));
}
}
}
}
}
return;
}
// Fast path for equal padding
if (pad_a == pad_p && pad_l == pad_r && pad_t == pad_b) {
const int pad_f = pad_a;
const int pad_h = pad_t;
const int pad_w = pad_l;
for (int channel = channels; channel--; img_data += channel_size) {
for (int kernel_frame = 0; kernel_frame < kernel_t; kernel_frame++) {
for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
int input_frame = -pad_f + kernel_frame * dilation_t;
for (int output_frames = output_t; output_frames; output_frames--) {
if (!utils::IsAGeZeroAndALtB(input_frame, clip_len)) {
for (int output_rows = output_h; output_rows; output_rows--) {
for (int output_cols = output_w; output_cols; output_cols--) {
*(col_data++) = 0;
}
}
} else {
int input_row = -pad_h + kernel_row * dilation_h;
for (int output_rows = output_h; output_rows; output_rows--) {
if (!utils::IsAGeZeroAndALtB(input_row, height)) {
for (int output_cols = output_w; output_cols;
output_cols--) {
*(col_data++) = 0;
}
} else {
int input_col = -pad_w + kernel_col * dilation_w;
for (int output_col = output_w; output_col; output_col--) {
if (utils::IsAGeZeroAndALtB(input_col, width)) {
*(col_data++) = img_data
[(input_frame * height + input_row) * width +
input_col];
} else {
*(col_data++) = 0;
}
input_col += stride_w;
}
}
input_row += stride_h;
}
}
input_frame += stride_t;
}
}
}
}
}
return;
}
// Baseline
const int dkernel_t = dilation_t * (kernel_t - 1) + 1;
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
int clip_col = (clip_len + pad_p + pad_a - dkernel_t) / stride_t + 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_t * 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 t_offset = (c / kernel_w / kernel_h) % kernel_t;
int c_im = c / kernel_h / kernel_w / kernel_t;
for (int t = 0; t < clip_col; ++t) {
for (int h = 0; h < height_col; ++h) {
for (int w = 0; w < width_col; ++w) {
int t_pad = t * stride_t - pad_p + t_offset * dilation_t;
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 (t_pad >= 0 && t_pad < clip_len && h_pad >= 0 && h_pad < height &&
w_pad >= 0 && w_pad < width) {
col_data[((c * clip_col + t) * height_col + h) * width_col + w] =
img_data
[((c_im * clip_len + t_pad) * height + h_pad) * width +
w_pad];
} else {
col_data[((c * clip_col + t) * height_col + h) * width_col + w] = 0;
}
}
}
}
}
}
} // namespace
template <>
C10_EXPORT void Im2ColNd<float, CPUContext, StorageOrder::NCHW>(
const int N,
const int img_size,
const int col_size,
const int* img_shape,
const int* col_shape,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const float* img_data,
float* col_data,
CPUContext* /* context */,
const int /* groups */) {
// In NCHW, the number of groups doesn't affect Im2Col.
if (N == 3) {
const int channels =
col_shape[0] / kernel_shape[0] / kernel_shape[1] / kernel_shape[2];
Im2Col3dNCHWImpl<float>(
channels,
img_shape[1],
img_shape[2],
img_shape[3],
kernel_shape[0],
kernel_shape[1],
kernel_shape[2],
dilation[0],
dilation[1],
dilation[2],
pad[0],
pad[1],
pad[2],
pad[3],
pad[4],
pad[5],
stride[0],
stride[1],
stride[2],
img_data,
col_data);
} else {
Im2ColNdNCHWImpl<float, false>(
N,
img_size,
col_size,
img_shape,
col_shape,
kernel_shape,
stride,
dilation,
pad,
img_data,
col_data);
}
}
template <>
C10_EXPORT void Col2ImNd<float, CPUContext, StorageOrder::NCHW>(
const int N,
const int img_size,
const int col_size,
const int* img_shape,
const int* col_shape,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const float* col_data,
float* img_data,
CPUContext* /* context */,
const int /* groups */) {
// In NCHW, the number of groups doesn't affect Col2Im.
Im2ColNdNCHWImpl<float, true>(
N,
img_size,
col_size,
img_shape,
col_shape,
kernel_shape,
stride,
dilation,
pad,
col_data,
img_data);
}
template <>
C10_EXPORT void Im2Col<float, CPUContext, StorageOrder::NCHW>(
const int C,
const int H,
const int W,
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,
const float* img_data,
float* col_data,
CPUContext* context,
const int /* groups */) {
// In NCHW, the number of groups doesn't affect Im2Col.
// Fast path for zero padding and no dilation
if (pad_t == 0 && pad_l == 0 && pad_b == 0 && pad_r == 0 && dilation_h == 1 &&
dilation_w == 1) {
Im2ColZeroPaddingAndNoDilationNCHW<float>(
C,
H,
W,
kernel_h,
kernel_w,
stride_h,
stride_w,
img_data,
col_data,
context);
return;
}
// Baseline
const int output_h =
(H + pad_t + pad_b - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
const int output_w =
(W + pad_l + pad_r - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
const int output_size = output_h * output_w;
for (int c = 0; c < C; ++c) {
for (int kh = 0; kh < kernel_h; ++kh) {
for (int kw = 0; kw < kernel_w; ++kw) {
for (int h = 0; h < output_h; ++h) {
const int h_pad = h * stride_h - pad_t + kh * dilation_h;
if (!utils::IsAGeZeroAndALtB(h_pad, H)) {
std::memset(col_data + h * output_w, 0, output_w * sizeof(float));
continue;
}
for (int w = 0; w < output_w; ++w) {
const int w_pad = w * stride_w - pad_l + kw * dilation_w;
col_data[h * output_w + w] = utils::IsAGeZeroAndALtB(w_pad, W)
? img_data[(c * H + h_pad) * W + w_pad]
: 0;
}
}
col_data += output_size;
}
}
}
}
template <>
C10_EXPORT void Im2Col<float, CPUContext, StorageOrder::NHWC>(
const int C,
const int H,
const int W,
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,
const float* img_data,
float* col_data,
CPUContext* context,
const int groups) {
// Fast path for zero padding and no dilation
if (pad_t == 0 && pad_l == 0 && pad_b == 0 && pad_r == 0 && dilation_h == 1 &&
dilation_w == 1 && groups == 1) {
Im2ColZeroPaddingAndNoDilationNHWC<float>(
C,
H,
W,
kernel_h,
kernel_w,
stride_h,
stride_w,
img_data,
col_data,
context);
return;
}
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
const int output_h = (H + pad_t + pad_b - dkernel_h) / stride_h + 1;
const int output_w = (W + pad_l + pad_r - dkernel_w) / stride_w + 1;
int h_pad = -pad_t;
if (groups == 1) {
for (int h = 0; h < output_h; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < output_w; ++w) {
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h) {
if (!utils::IsAGeZeroAndALtB(ih, H)) {
std::memset(col_data, 0, sizeof(float) * kernel_w * C);
col_data += kernel_w * C;
continue;
}
for (int iw = w_pad; iw < w_pad + dkernel_w; iw += dilation_w) {
if (utils::IsAGeZeroAndALtB(iw, W)) {
std::memcpy(
col_data, img_data + (ih * W + iw) * C, sizeof(float) * C);
} else {
std::memset(col_data, 0, sizeof(float) * C);
}
col_data += C;
} // iw
} // ih
w_pad += stride_w;
} // w
h_pad += stride_h;
} // h
} else {
/**
* img_data in N H W G C/G layout
* col_data in N G H W R S C/G layout
* Note that groups are pulled out to an outer dimension so that we can use
* GEMMs efficiently.
*/
const int C_per_G = C / groups;
for (int h = 0; h < output_h; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < output_w; ++w) {
int r = 0;
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h, ++r) {
int s = 0;
for (int iw = w_pad; iw < w_pad + dkernel_w; iw += dilation_w, ++s) {
if (utils::IsAGeZeroAndALtB(ih, H) &&
utils::IsAGeZeroAndALtB(iw, W)) {
for (int g = 0; g < groups; ++g) {
std::memcpy(
col_data + ((g * kernel_h + r) * kernel_w + s) * C_per_G,
img_data + (ih * W + iw) * C + g * C_per_G,
sizeof(float) * C_per_G);
}
} else {
for (int g = 0; g < groups; ++g) {
std::memset(
col_data + ((g * kernel_h + r) * kernel_w + s) * C_per_G,
0,
sizeof(float) * C_per_G);
}
}
} // iw
} // ih
col_data += kernel_h * kernel_w * C;
w_pad += stride_w;
} // w
h_pad += stride_h;
} // h
}
}
/**
* The layout of the result is N H W G R S C/G.
* Note that groups are pulled out to an outer dimension so that we can use
* GEMMs efficiently.
*/
template <typename TData>
C10_EXPORT void Im2Col3dNHWCImpl(
const int C,
const int T,
const int H,
const int W,
const int kernel_t,
const int kernel_h,
const int kernel_w,
const int dilation_t,
const int dilation_h,
const int dilation_w,
const int pad_p, // previous frame
const int pad_t, // top
const int pad_l, // left
const int pad_n, // next frame
const int pad_b, // bottom
const int pad_r, // right
const int stride_t,
const int stride_h,
const int stride_w,
const TData* img_data,
TData* col_data,
const int groups) {
const int dkernel_t = dilation_t * (kernel_t - 1) + 1;
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
const int output_t = (T + pad_p + pad_n - dkernel_t) / stride_t + 1;
const int output_h = (H + pad_t + pad_b - dkernel_h) / stride_h + 1;
const int output_w = (W + pad_l + pad_r - dkernel_w) / stride_w + 1;
const int C_per_G = C / groups;
int t_pad = -pad_p;
for (int t = 0; t < output_t; ++t) {
int h_pad = -pad_t;
for (int h = 0; h < output_h; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < output_w; ++w) {
int q = 0;
for (int it = t_pad; it < t_pad + dkernel_t; it += dilation_t, ++q) {
int r = 0;
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h, ++r) {
int s = 0;
for (int iw = w_pad; iw < w_pad + dkernel_w;
iw += dilation_w, ++s) {
if (utils::IsAGeZeroAndALtB(it, T) &&
utils::IsAGeZeroAndALtB(ih, H) &&
utils::IsAGeZeroAndALtB(iw, W)) {
for (int g = 0; g < groups; ++g) {
std::memcpy(
col_data +
(((g * kernel_t + q) * kernel_h + r) * kernel_w + s) *
C_per_G,
img_data + ((it * H + ih) * W + iw) * C + g * C_per_G,
sizeof(TData) * C_per_G);
}
} else {
for (int g = 0; g < groups; ++g) {
std::memset(
col_data +
(((g * kernel_t + q) * kernel_h + r) * kernel_w + s) *
C_per_G,
0,
sizeof(TData) * C_per_G);
}
}
} // iw
} // ih
} // it
col_data += kernel_t * kernel_h * kernel_w * C;
w_pad += stride_w;
} // w
h_pad += stride_h;
} // h
t_pad += stride_t;
} // t
}
template <>
C10_EXPORT void Im2ColNd<float, CPUContext, StorageOrder::NHWC>(
const int N,
const int /*img_size*/,
const int /*col_size*/,
const int* img_shape,
const int* col_shape,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const float* img_data,
float* col_data,
CPUContext* /* context */,
const int groups) {
if (N == 3) {
const int channels =
col_shape[3] / kernel_shape[0] / kernel_shape[1] / kernel_shape[2];
Im2Col3dNHWCImpl<float>(
channels,
img_shape[0],
img_shape[1],
img_shape[2],
kernel_shape[0],
kernel_shape[1],
kernel_shape[2],
dilation[0],
dilation[1],
dilation[2],
pad[0],
pad[1],
pad[2],
pad[3],
pad[4],
pad[5],
stride[0],
stride[1],
stride[2],
img_data,
col_data,
groups);
} else {
CAFFE_NOT_IMPLEMENTED;
}
}
template <>
C10_EXPORT void Col2Im<float, CPUContext, StorageOrder::NCHW>(
const int C,
const int H,
const int W,
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,
const float* col_data,
float* img_data,
CPUContext* context,
const int /* groups */) {
// In NCHW, the number of groups doesn't affect Col2Im.
// Fast path for zero padding and no dilation
if (pad_t == 0 && pad_l == 0 && pad_b == 0 && pad_r == 0 && dilation_h == 1 &&
dilation_w == 1) {
Col2ImZeroPaddingAndNoDilationNCHW<float>(
C,
H,
W,
kernel_h,
kernel_w,
stride_h,
stride_w,
col_data,
img_data,
context);
return;
}
// Fallback
Set<float, CPUContext>(C * H * W, 0.0f, img_data, context);
const int output_h =
(H + pad_t + pad_b - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
const int output_w =
(W + pad_l + pad_r - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
const int output_size = output_h * output_w;
for (int c = 0; c < C; ++c) {
for (int kh = 0; kh < kernel_h; ++kh) {
for (int kw = 0; kw < kernel_w; ++kw) {
for (int h = 0; h < output_h; ++h) {
const int h_pad = h * stride_h - pad_t + kh * dilation_h;
if (!utils::IsAGeZeroAndALtB(h_pad, H)) {
continue;
}
for (int w = 0; w < output_w; ++w) {
const int w_pad = w * stride_w - pad_l + kw * dilation_w;
if (utils::IsAGeZeroAndALtB(w_pad, W)) {
img_data[(c * H + h_pad) * W + w_pad] +=
col_data[h * output_w + w];
}
}
}
col_data += output_size;
}
}
}
}
template <>
C10_EXPORT void Col2Im<float, CPUContext, StorageOrder::NHWC>(
const int C,
const int H,
const int W,
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,
const float* col_data,
float* img_data,
CPUContext* context,
const int groups) {
// Fast path for zero padding and no dilation
if (pad_t == 0 && pad_l == 0 && pad_b == 0 && pad_r == 0 && dilation_h == 1 &&
dilation_w == 1 && groups == 1) {
Col2ImZeroPaddingAndNoDilationNHWC<float>(
C,
H,
W,
kernel_h,
kernel_w,
stride_h,
stride_w,
col_data,
img_data,
context);
return;
}
Set<float, CPUContext>(H * W * C, 0, img_data, context);
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
const int output_h = (H + pad_t + pad_b - dkernel_h) / stride_h + 1;
const int output_w = (W + pad_l + pad_r - dkernel_w) / stride_w + 1;
int h_pad = -pad_t;
if (groups == 1) {
for (int h = 0; h < output_h; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < output_w; ++w) {
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h) {
if (!utils::IsAGeZeroAndALtB(ih, H)) {
col_data += kernel_w * C;
continue;
}
for (int iw = w_pad; iw < w_pad + dkernel_w; iw += dilation_w) {
if (utils::IsAGeZeroAndALtB(iw, W)) {
float* img_data_patch = img_data + (ih * W + iw) * C;
Add<float, CPUContext>(
C, img_data_patch, col_data, img_data_patch, context);
}
col_data += C;
} // iw
} // ih
w_pad += stride_w;
} // w
h_pad += stride_h;
} // h
} else {
const int C_per_G = C / groups;
for (int h = 0; h < output_h; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < output_w; ++w) {
int r = 0;
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h, ++r) {
int s = 0;
for (int iw = w_pad; iw < w_pad + dkernel_w; iw += dilation_w, ++s) {
if (utils::IsAGeZeroAndALtB(ih, H) &&
utils::IsAGeZeroAndALtB(iw, W)) {
float* img_data_patch = img_data + (ih * W + iw) * C;
for (int g = 0; g < groups; ++g) {
Add<float, CPUContext>(
C_per_G,
img_data_patch + g * C_per_G,
col_data + ((g * kernel_h + r) * kernel_w + s) * C_per_G,
img_data_patch + g * C_per_G,
context);
}
}
} // iw
} // ih
col_data += kernel_h * kernel_w * C;
w_pad += stride_w;
} // w
h_pad += stride_h;
} // h
}
}
/**
* The layout of the result is N H W G R S C/G.
* Note that groups are pulled out to an outer dimension so that we can use
* GEMMs efficiently.
*/
template <typename TData>
C10_EXPORT void Col2Im3dNHWCImpl(
const int C,
const int T,
const int H,
const int W,
const int kernel_t,
const int kernel_h,
const int kernel_w,
const int dilation_t,
const int dilation_h,
const int dilation_w,
const int pad_p, // previous frame
const int pad_t, // top
const int pad_l, // left
const int pad_n, // next frame
const int pad_b, // bottom
const int pad_r, // right
const int stride_t,
const int stride_h,
const int stride_w,
const TData* col_data,
TData* img_data,
CPUContext* context,
const int groups) {
Set<float, CPUContext>(T * H * W * C, 0, img_data, context);
const int dkernel_t = dilation_t * (kernel_t - 1) + 1;
const int dkernel_h = dilation_h * (kernel_h - 1) + 1;
const int dkernel_w = dilation_w * (kernel_w - 1) + 1;
const int output_t = (T + pad_p + pad_n - dkernel_t) / stride_t + 1;
const int output_h = (H + pad_t + pad_b - dkernel_h) / stride_h + 1;
const int output_w = (W + pad_l + pad_r - dkernel_w) / stride_w + 1;
const int C_per_G = C / groups;
int t_pad = -pad_p;
for (int t = 0; t < output_t; ++t) {
int h_pad = -pad_t;
for (int h = 0; h < output_h; ++h) {
int w_pad = -pad_l;
for (int w = 0; w < output_w; ++w) {
int q = 0;
for (int it = t_pad; it < t_pad + dkernel_t; it += dilation_t, ++q) {
int r = 0;
for (int ih = h_pad; ih < h_pad + dkernel_h; ih += dilation_h, ++r) {
int s = 0;
for (int iw = w_pad; iw < w_pad + dkernel_w;
iw += dilation_w, ++s) {
if (utils::IsAGeZeroAndALtB(it, T) &&
utils::IsAGeZeroAndALtB(ih, H) &&
utils::IsAGeZeroAndALtB(iw, W)) {
float* img_data_patch = img_data + ((it * T + ih) * W + iw) * C;
for (int g = 0; g < groups; ++g) {
Add<float, CPUContext>(
C_per_G,
img_data_patch + g * C_per_G,
col_data +
(((g * kernel_t + q) * kernel_h + r) * kernel_w + s) *
C_per_G,
img_data_patch + g * C_per_G,
context);
}
}
} // iw
} // ih
} // it
col_data += kernel_t * kernel_h * kernel_w * C;
w_pad += stride_w;
} // w
h_pad += stride_h;
} // h
t_pad += stride_t;
} // t
}
template <>
C10_EXPORT void Col2ImNd<float, CPUContext, StorageOrder::NHWC>(
const int N,
const int /*img_size*/,
const int /*col_size*/,
const int* img_shape,
const int* col_shape,
const int* kernel_shape,
const int* stride,
const int* dilation,
const int* pad,
const float* col_data,
float* img_data,
CPUContext* context,
const int groups) {
if (N == 3) {
const int channels =
col_shape[3] / kernel_shape[0] / kernel_shape[1] / kernel_shape[2];
Col2Im3dNHWCImpl<float>(
channels,
img_shape[0],
img_shape[1],
img_shape[2],
kernel_shape[0],
kernel_shape[1],
kernel_shape[2],
dilation[0],
dilation[1],
dilation[2],
pad[0],
pad[1],
pad[2],
pad[3],
pad[4],
pad[5],
stride[0],
stride[1],
stride[2],
col_data,
img_data,
context,
groups);
} else {
CAFFE_NOT_IMPLEMENTED;
}
}
template <>
C10_EXPORT void BiasCHW<float, CPUContext>(
const float* bias,
const float* /*bias_multiplier*/,
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];
#if defined(__ARM_NEON__) || defined(__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 // defined(__ARM_NEON__) || defined(__ARM_NEON)
image += image_size;
}
}
#define CAFFE2_SPECIALIZED_COPYVECTOR(T) \
template <> \
C10_EXPORT 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
} // namespace math
} // namespace caffe2
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