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#ifndef CAFFE2_UTILS_MATH_H_
#define CAFFE2_UTILS_MATH_H_
// This is a simple translation from the old Caffe math interfaces. We aim to
// still keep it simple, so all platforms would be able to support it fairly
// easily.
// We include the cblas header here so that we can obtain the macros from cblas.
extern "C" {
#include "caffe2/utils/cblas.h"
}
#ifdef CAFFE2_USE_ACCELERATE
#include <Accelerate/Accelerate.h>
#endif // CAFFE2_USE_ACCELERATE
#include "caffe2/core/common.h"
#include "caffe2/core/types.h"
#include "caffe2/utils/math/broadcast.h"
#include "caffe2/utils/math/elementwise.h"
#include "caffe2/utils/math/reduce.h"
#include "caffe2/utils/math/transpose.h"
#include "caffe2/utils/math/utils.h"
namespace caffe2 {
// TODO: Change dims related arguments to int64_t?
class Tensor;
// An empty class as a placeholder for a math function that has no specific
// engine specified.
class TORCH_API DefaultEngine {};
namespace math {
#define C10_DECLARE_COMPARE_OP(Comp) \
template <typename T, class Context, bool kBroadcast1st = false> \
void Rowwise##Comp( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
bool* C, \
Context* context); \
\
template <typename T, class Context, bool kBroadcast1st = false> \
void Colwise##Comp( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
bool* C, \
Context* context); \
\
template <typename T, class Context> \
void Comp( \
const int A_ndim, \
const int* A_dims, \
const int B_ndim, \
const int* B_dims, \
const T* A, \
const T* B, \
bool* C, \
Context* context);
C10_DECLARE_COMPARE_OP(EQ)
C10_DECLARE_COMPARE_OP(NE)
C10_DECLARE_COMPARE_OP(LT)
C10_DECLARE_COMPARE_OP(LE)
C10_DECLARE_COMPARE_OP(GT)
C10_DECLARE_COMPARE_OP(GE)
#undef C10_DECLARE_COMPARE_OP
#define C10_DECLARE_BINARY_OP(Func) \
template <typename T, class Context, bool kBroadcast1st = false> \
void Rowwise##Func( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
Context* context); \
\
template <typename T, class Context, bool kBroadcast1st = false> \
void Colwise##Func( \
const int rows, \
const int cols, \
const T* A, \
const T* B, \
T* C, \
Context* context); \
\
template <typename T, class Context> \
void Func( \
const int A_ndim, \
const int* A_dims, \
const int B_ndim, \
const int* B_dims, \
const T* A, \
const T* B, \
T* C, \
Context* context);
C10_DECLARE_BINARY_OP(Add)
C10_DECLARE_BINARY_OP(Sub)
C10_DECLARE_BINARY_OP(Mul)
C10_DECLARE_BINARY_OP(Div)
C10_DECLARE_BINARY_OP(And)
C10_DECLARE_BINARY_OP(Or)
C10_DECLARE_BINARY_OP(Xor)
C10_DECLARE_BINARY_OP(BitwiseAnd)
C10_DECLARE_BINARY_OP(BitwiseOr)
C10_DECLARE_BINARY_OP(BitwiseXor)
#undef C10_DECLARE_BINARY_OP
// Broadcasts X with X_dims to Y with Y_dims.
template <typename T, class Context>
TORCH_API void Broadcast(
const int X_ndim,
const int* X_dims,
const int Y_ndim,
const int* Y_dims,
const T alpha,
const T* X,
T* Y,
Context* context,
bool allow_broadcast_fastpath=false);
// Computes inv_std from variance.
template <typename T, class Context>
TORCH_API void InvStd(
const int N,
const T epsilon,
const T* var,
T* inv_std,
Context* context);
// Adds batch sub-tensors elementwise to output. Stripe is the stripe length
// and N is the number of elements to add (size of Y).
template <typename T, class Context>
TORCH_API void AddStripedBatch(
const int N,
const T* first,
T* y,
const int stripe,
const int batch,
Context* context);
// Compute the row-wise max of a N*D matrix X, and write it to a N
// dimensional vector y.
template <typename T, class Context>
TORCH_API void
RowwiseMax(const int N, const int D, const T* x, T* y, Context* context);
// Compute the column-wise max of a N*D matrix X, and write it to a D
// dimensional vector y.
template <typename T, class Context>
TORCH_API void
ColwiseMax(const int N, const int D, const T* x, T* y, Context* context);
// Elemwise maximum of vector x and scalar alpha. y[i] = max(x[i], alpha)
template <typename T, class Context>
TORCH_API void
Maximum(const int N, const float alpha, const T* x, T* y, Context* context);
// Decaf gemm provides a simpler interface to the gemm functions, with the
// limitation that the data has to be contiguous in memory.
template <typename T, class Context, class Engine = DefaultEngine>
TORCH_API void Gemm(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
const int M,
const int N,
const int K,
const float alpha,
const T* A,
const T* B,
const float beta,
T* C,
Context* context,
TensorProto::DataType math_type = TensorProto_DataType_FLOAT);
// We also provide a gemm that has explicit lda, ldb and ldc specified.
// In most cases you probably want to use the function above, though.
template <typename T, class Context, class Engine = DefaultEngine>
TORCH_API void GemmEx(
const CBLAS_TRANSPOSE trans_A,
const CBLAS_TRANSPOSE trans_B,
const int M,
const int N,
const int K,
const T alpha,
const T* A,
const int lda,
const T* B,
const int ldb,
const T beta,
T* C,
const int ldc,
Context* context);
// GemmBatched provides a simple abstraction into library routines
template <typename T, class Context, class Engine = DefaultEngine>
TORCH_API void GemmBatched(
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 T** A,
const T** B,
const float beta,
T** C,
Context* context,
TensorProto::DataType math_type = TensorProto_DataType_FLOAT);
template <typename T, class Context, class Engine = DefaultEngine>
TORCH_API void GemmStridedBatched(
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 T* A,
const int A_stride,
const T* B,
const int B_stride,
const float beta,
T* C,
const int C_stride,
Context* context,
TensorProto::DataType math_type = TensorProto_DataType_FLOAT);
// Gemv always takes in a M*N matrix A, and depending on whether we set TransA
// to Trans, the output is:
// CblasNoTrans: x is an N dim vector and y is an M dim vector.
// CblasTrans: x is an M dim vector and y is an N dim vector.
template <typename T, class Context, class Engine = DefaultEngine>
TORCH_API void Gemv(
const CBLAS_TRANSPOSE trans_A,
const int M,
const int N,
const float alpha,
const T* A,
const T* x,
const float beta,
T* y,
Context* context,
TensorProto::DataType math_type = TensorProto_DataType_FLOAT);
template <typename T, class Context>
TORCH_API void
RandUniform(const size_t n, const T a, const T b, T* r, Context* context);
// Generate n values that sum up to a fixed sum
// and subject to a restriction a <= x <= b for each x generated
template <typename T, class Context>
TORCH_API void RandFixedSum(
const size_t n,
const T a,
const T b,
const T sum,
T* r,
Context* context);
template <typename T, class Context>
TORCH_API void RandUniformUnique(
const size_t n,
const T a,
const T b,
T* r,
const size_t m,
const T* avoid,
Context* context);
// Generate n values from synthetic data distribution,
// define by unique accesses and stack distances
template <typename T, class Context>
TORCH_API void
RandSyntheticData(const size_t n, const T a, const T b, T* r, Context* context);
template <typename T, class Context>
TORCH_API void
RandGaussian(const size_t n, const T mean, const T std, T* r, Context* context);
// Dot matrix of vector a and b, and writes the result to a single value y.
template <typename T, class Context>
TORCH_API void
Dot(const int N, const T* a, const T* b, T* y, Context* context);
// Sum of vector x, and writes the result to a single value y.
template <typename T, class Context>
TORCH_API void Sum(
const int N,
const T* x,
T* y,
Context* context,
Tensor* scratch_ptr = nullptr);
// Sum of squares of vector x, and writes the result to a single value y.
template <typename T, class Context>
TORCH_API void SumSqr(
const int N,
const T* x,
T* y,
Context* context,
Tensor* scratch_ptr = nullptr);
// Select does index selection of the rows a N*D matrix x, and gives the N
// dimensional vector y that contains the selected data.
template <typename T, class Context>
TORCH_API void Select(
const int N,
const int D,
const T* x,
const int* idx,
T* y,
Context* context);
// groups must be 1 for GPU
// For NHWC order with groups > 1, the result will be layout in
// NHW G RS C/G order to make data within the same group to be contiguous.
// For NCHW order, groups doesn't make any difference because we're doing Im2Col
// for each N and C is the slowest moving dimension among CHW.
template <typename T, class Context, StorageOrder kOrder>
TORCH_API void Im2Col(
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,
const T* img_data,
T* col_data,
Context* context,
const int groups = 1);
// groups must be 1 for GPU
template <typename T, class Context, StorageOrder kOrder>
TORCH_API void Im2ColNd(
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 T* img_data,
T* col_data,
Context* context,
const int groups = 1);
// groups must be 1 for GPU
// For NHWC order with groups > 1, the result will be layout in
// NHW G RS C/G order to make data within the same group to be contiguous.
// For NCHW order, groups doesn't make any difference because we're doing Im2Col
// for each N and C is the slowest moving dimension among CHW.
template <typename T, class Context, StorageOrder kOrder>
TORCH_API void Col2Im(
const int channels,
const int height,
const int width,
const int patch_h,
const int patch_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 T* col_data,
T* img_data,
Context* context,
const int groups = 1);
// groups must be 1 for GPU
// For NHWC order with groups > 1, the result will be layout in
// NHW G RS C/G order to make data within the same group to be contiguous.
// For NCHW order, groups doesn't make any difference because we're doing Im2Col
// for each N and C is the slowest moving dimension among CHW.
template <typename T, class Context, StorageOrder kOrder>
TORCH_API void Col2ImNd(
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 T* col_data,
T* img_data,
Context* context,
const int groups = 1);
// Applies a per-channel bias value to each channel of the input
// image. image_size is H * W
template <typename T, class Context>
TORCH_API void BiasCHW(
const T* bias,
const T* bias_multiplier,
const int bias_channels,
const int image_size,
T* image,
Context* context);
template <class Context>
TORCH_API void CopyMatrix(
const size_t item_size,
const int M,
const int N,
const void* A,
const int lda,
void* B,
const int ldb,
Context* context,
TypeMeta::Copy copy = nullptr);
template <typename T, class Context>
TORCH_API void CopyMatrix(
const int M,
const int N,
const T* A,
const int lda,
T* B,
const int ldb,
Context* context);
template <typename T, class Context>
TORCH_API void CopyMatrix(
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,
Context* context);
template <typename T, class Context>
TORCH_API void CopyVector(const int N, const T* A, T* B, Context* context);
} // namespace math
} // namespace caffe2
#include "caffe2/utils/math-detail.h"
#endif // CAFFE2_UTILS_MATH_H_
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