提交 987f065a 编写于 作者: L Liu Yiqun

1. Support scalar computing.

2. Centralize the use of sse and neon instrinsic.
3. Disable neon intrinsic when enable gpu.
上级 21be601b
......@@ -17,10 +17,9 @@ limitations under the License. */
#include <stdio.h>
#include "hl_base.h"
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include "hl_neon_matrix_kernel.cuh"
#else
#include "hl_sse_matrix_kernel.cuh"
#ifndef __CUDA_ARCH__
#include "hl_cpu_matrix_kernel_detail.cuh"
#endif
/**
......@@ -114,35 +113,6 @@ void hl_cpu_apply_quaternary_op(Op op,
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda) {
for (int i = 0; i < dimM; i++) {
real tmp = agg.init();
for (int j = 0; j < dimN; j++) {
tmp = agg(tmp, op(A[i * lda + j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda,
real *B, int ldb) {
for (int i = 0; i < dimM; i++) {
real tmp = agg.init();
for (int j = 0; j < dimN; j++) {
tmp = agg(tmp, op(A[i * lda + j], B[i * ldb + j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
}
}
template <class Agg, class Op, class Saver>
void hl_cpu_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
......
......@@ -13,26 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_SSE_MATRIX_KERNEL_CUH_
#define HL_SSE_MATRIX_KERNEL_CUH_
#ifndef HL_MATRIX_KERNEL_DETAIL_CUH_
#define HL_MATRIX_KERNEL_DETAIL_CUH_
#include "hl_matrix_type.cuh"
#define VECTOR_SIZE 16
#ifndef PADDLE_TYPE_DOUBLE
/* number of float in vector */
#define VECTOR_LEN 4
#define VECTOR_SET _mm_set_ps1
#else
#if defined(__APPLE__) || defined(__OSX__)
#define _mm_set_pd1 _mm_set1_pd
#endif
/* number of double in vector */
#define VECTOR_LEN 2
#define VECTOR_SET _mm_set_pd1
#endif
inline bool hl_check_align(size_t size) {
return !(size & (VECTOR_SIZE - 1));
}
......@@ -41,27 +26,63 @@ inline bool hl_check_align(void *ptr) {
return hl_check_align(reinterpret_cast<size_t>(ptr));
}
#ifndef PADDLE_TYPE_DOUBLE
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
__m128 lo = _mm_unpacklo_ps(mm, mm);
__m128 hi = _mm_unpackhi_ps(mm, mm);
__m128 tmp1 = agg.vecOp(lo, hi);
__m128 tmp2 = _mm_movehl_ps(tmp1, tmp1);
__m128 ret = agg.vecOp(tmp1, tmp2);
template <class Agg, class Op, class Saver>
void hl_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda) {
for (int i = 0; i < dimM; i++) {
real tmp = agg.init();
for (int j = 0; j < dimN; j++) {
tmp = agg(tmp, op(A[i * lda + j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
}
}
return _mm_cvtss_f32(ret);
template <class Agg, class Op, class Saver>
void hl_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda,
real *B, int ldb) {
for (int i = 0; i < dimM; i++) {
real tmp = agg.init();
for (int j = 0; j < dimN; j++) {
tmp = agg(tmp, op(A[i * lda + j], B[i * ldb + j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
#else
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
__m128d lo = _mm_unpacklo_pd(mm, mm);
__m128d hi = _mm_unpackhi_pd(mm, mm);
__m128d ret = agg.vecOp(lo, hi);
return _mm_cvtsd_f64(ret);
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j], B[i * ldb + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
#endif
template <class Agg, class Op, class Saver>
void hl_sse_matrix_row_op(Agg agg, Op op, Saver sv,
......@@ -118,35 +139,6 @@ void hl_sse_matrix_row_op(Agg agg, Op op, Saver sv,
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j], B[i * ldb + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
/*
* MaxRow greater than or equal dimN
* dimN is multiples of VECTOR_LEN
......@@ -315,4 +307,4 @@ void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
}
}
#endif /* HL_SSE_MATRIX_KERNEL_CUH_ */
#endif /* HL_MATRIX_KERNEL_DETAIL_CUH_ */
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_CPU_SCALAR_CUH_
#define HL_CPU_SCALAR_CUH_
#define VECTOR_SIMD false
#define VECTOR_SET hl_vec_set
#ifndef PADDLE_TYPE_DOUBLE
/* size of float */
#define VECTOR_SIZE 4
#else
/* size of double */
#define VECTOR_SIZE 8
#endif
typedef real vecType;
/* Consider a real as a vector */
#define VECTOR_LEN 1
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
return mm;
}
INLINE real hl_vec_set(const real r) {
return r;
}
INLINE real hl_vec_max(const real a, const real b) {
return a > b ? a : b;
}
INLINE real hl_vec_min(const real a, const real b) {
return a > b ? b : a;
}
INLINE real hl_vec_add(const real a, const real b) {
return a + b;
}
INLINE real hl_vec_sub(const real a, const real b) {
return a - b;
}
INLINE real hl_vec_mul(const real a, const real b) {
return a * b;
}
INLINE real hl_vec_div(const real a, const real b) {
return a / b;
}
INLINE real hl_vec_classification_error(const real a,
const real b,
const real p,
const real r) {
return ((a > p) == (b > p)) ? 0.0f : 1.0f;
}
#endif // HL_CPU_SCALAR_CUH_
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_CPU_SIMD_NEON_CUH_
#define HL_CPU_SIMD_NEON_CUH_
#include <arm_neon.h>
#define VECTOR_SIMD true
#define VECTOR_SIZE 16
#define VECTOR_SET hl_vec_set
#ifndef PADDLE_TYPE_DOUBLE
typedef float32x4_t vecType;
/* number of float in vector */
#define VECTOR_LEN 4
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
float32x4_t rev = vrev64q_f32(mm);
float32x4_t tmp1 = agg.vecOp(rev, rev);
float32x2_t lo = vget_high_f32(rev);
float32x2_t hi = vget_low_f32(rev);
float32x4_t tmp2 = vcombine_f32(hi, lo);
float32x4_t ret = agg.vecOp(tmp1, tmp2);
return vgetq_lane_f32(ret, 0);
}
inline float32x4_t hl_vec_set(const real f) {
return vdupq_n_f32(f);
}
inline float32x4_t hl_vec_max(const float32x4_t a, const float32x4_t b) {
return vmaxq_f32(a, b);
}
inline float32x4_t hl_vec_min(const float32x4_t a, const float32x4_t b) {
return vminq_f32(a, b);
}
inline float32x4_t hl_vec_add(const float32x4_t a, const float32x4_t b) {
return vaddq_f32(a, b);
}
inline float32x4_t hl_vec_sub(const float32x4_t a, const float32x4_t b) {
return vsubq_f32(a, b);
}
inline float32x4_t hl_vec_mul(const float32x4_t a, const float32x4_t b) {
return vmulq_f32(a, b);
}
inline float32x4_t hl_vec_div(const float32x4_t a, const float32x4_t b) {
float32x4_t tmp = vrecpeq_f32(b);
return vmulq_f32(a, tmp);
}
inline float32x4_t hl_vec_classification_error(const float32x4_t a,
const float32x4_t b,
const float32x4_t p,
const float32x4_t r) {
uint32x4_t tmp1 = vcgtq_f32(a, p);
uint32x4_t tmp2 = vcgtq_f32(b, p);
uint32x4_t tmp3 = veorq_u32(tmp1, tmp2);
return vcvtq_f32_u32(vandq_u32(tmp3, vcvtq_u32_f32(r)));
}
#else
#ifdef __aarch64__
typedef float64x2_t vecType;
/* number of float in vector */
#define VECTOR_LEN 2
#define VECTOR_SET vdupq_n_f64
#error To be implemented
#else
#error NEON instructions does not support double precision
#endif // __aarch64__
#endif
#endif // HL_CPU_SIMD_NEON_CUH_
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_CPU_SIMD_SSE_CUH_
#define HL_CPU_SIMD_SSE_CUH_
#include <mmintrin.h>
#include <xmmintrin.h>
#include <emmintrin.h>
#define VECTOR_SIMD true
#define VECTOR_SIZE 16
#define VECTOR_SET hl_vec_set
#ifndef PADDLE_TYPE_DOUBLE
typedef __m128 vecType;
/* number of float in vector */
#define VECTOR_LEN 4
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
__m128 lo = _mm_unpacklo_ps(mm, mm);
__m128 hi = _mm_unpackhi_ps(mm, mm);
__m128 tmp1 = agg.vecOp(lo, hi);
__m128 tmp2 = _mm_movehl_ps(tmp1, tmp1);
__m128 ret = agg.vecOp(tmp1, tmp2);
return _mm_cvtss_f32(ret);
}
inline __m128 hl_vec_set(const real f) {
return _mm_set_ps1(f);
}
inline __m128 hl_vec_max(const __m128 a, const __m128 b) {
return _mm_max_ps(a, b);
}
inline __m128 hl_vec_min(const __m128 a, const __m128 b) {
return _mm_min_ps(a, b);
}
inline __m128 hl_vec_add(const __m128 a, const __m128 b) {
return _mm_add_ps(a, b);
}
inline __m128 hl_vec_sub(const __m128 a, const __m128 b) {
return _mm_sub_ps(a, b);
}
inline __m128 hl_vec_mul(const __m128 a, const __m128 b) {
return _mm_mul_ps(a, b);
}
inline __m128 hl_vec_div(const __m128 a, const __m128 b) {
return _mm_div_ps(a, b);
}
inline __m128 hl_vec_classification_error(const __m128 a,
const __m128 b,
const __m128 p,
const __m128 r) {
__m128 tmp1 = _mm_cmpgt_ps(a, p);
__m128 tmp2 = _mm_cmpgt_ps(b, p);
__m128 tmp3 = _mm_xor_ps(tmp1, tmp2);
return _mm_and_ps(tmp3, r);
}
#else
typedef __m128d vecType;
/* number of double in vector */
#define VECTOR_LEN 2
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
__m128d lo = _mm_unpacklo_pd(mm, mm);
__m128d hi = _mm_unpackhi_pd(mm, mm);
__m128d ret = agg.vecOp(lo, hi);
return _mm_cvtsd_f64(ret);
}
inline __m128d hl_vec_set(const real d) {
#if defined(__APPLE__) || defined(__OSX__)
return _mm_set1_pd(d);
#else
return _mm_set_pd1(d);
#endif
}
inline __m128d hl_vec_max(const __m128d a, const __m128d b) {
return _mm_max_pd(a, b);
}
inline __m128d hl_vec_min(const __m128d a, const __m128d b) {
return _mm_min_pd(a, b);
}
inline __m128d hl_vec_add(const __m128d a, const __m128d b) {
return _mm_add_pd(a, b);
}
inline __m128d hl_vec_sub(const __m128d a, const __m128d b) {
return _mm_sub_pd(a, b);
}
inline __m128d hl_vec_mul(const __m128d a, const __m128d b) {
return _mm_mul_pd(a, b);
}
inline __m128d hl_vec_div(const __m128d a, const __m128d b) {
return _mm_div_pd(a, b);
}
inline __m128d hl_vec_classification_error(const __m128d a,
const __m128d b,
const __m128d p,
const __m128d r) {
__m128d tmp1 = _mm_cmpgt_pd(a, p);
__m128d tmp2 = _mm_cmpgt_pd(b, p);
__m128d tmp3 = _mm_xor_pd(tmp1, tmp2);
return _mm_and_pd(tmp3, r);
}
#endif
#endif // HL_CPU_SIMD_SSE_CUH_
......@@ -18,26 +18,6 @@ limitations under the License. */
#include "hl_matrix_type.cuh"
#ifdef __CUDA_ARCH__
/**
* CUDA kernel inline function
*/
#define INLINE __device__ inline
#else
/**
* CPP inline function
*/
#define INLINE inline
#endif
#ifndef PADDLE_TYPE_DOUBLE
#define DEVICE_FMAX fmaxf
#define DEVICE_FMIN fminf
#else
#define DEVICE_FMAX fmax
#define DEVICE_FMIN fmin
#endif
class BaseOp {
public:
static const bool sse = false;
......@@ -66,10 +46,8 @@ typedef BaseOp SSESquaredDiff;
typedef BaseOp SSEFirst;
typedef BaseOp SSESecond;
typedef BaseOp SSEClassificationError;
#elif defined(__ARM__NEON__) || defined(__ARM_NEON)
#include "hl_matrix_base_neon.cuh"
#else
#include "hl_matrix_base_sse.cuh"
#include "hl_matrix_base_detail.cuh"
#endif
namespace aggregate {
......
......@@ -12,32 +12,33 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_MATRIX_BASE_DETAIL_CUH_
#define HL_MATRIX_BASE_DETAIL_CUH_
#ifndef HL_MATRIX_BASE_NEON_CUH_
#define HL_MATRIX_BASE_NEON_CUH_
#include "hl_matrix_type.cuh"
namespace aggregate {
class SSESum {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vaddq_f32(a, b);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_add(a, b);
}
};
class SSEMax {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vmaxq_f32(a, b);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_max(a, b);
}
};
class SSEMin {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vminq_f32(a, b);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_min(a, b);
}
};
} // namespace aggregate
......@@ -46,8 +47,8 @@ namespace base {
namespace unary {
class SSEIdentity {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a) const {
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a) const {
return a;
}
};
......@@ -56,106 +57,95 @@ public:
namespace binary {
class SSEAdd {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vaddq_f32(a, b);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_add(a, b);
}
};
class SSEAdd2 {
public:
static const bool sse = true;
static const bool sse = VECTOR_SIMD;
const real p1;
const real p2;
float32x4_t mp1;
float32x4_t mp2;
vecType mp1;
vecType mp2;
public:
SSEAdd2(const real s1, const real s2) : p1(s1), p2(s2) {
mp1 = vdupq_n_f32(p1);
mp2 = vdupq_n_f32(p2);
mp1 = hl_vec_set(p1);
mp2 = hl_vec_set(p2);
}
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
float32x4_t tmp1, tmp2;
tmp1 = vmulq_f32(mp1, a);
tmp2 = vmulq_f32(mp2, b);
return vaddq_f32(tmp1, tmp2);
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_add(hl_vec_mul(mp1, a), hl_vec_mul(mp2, b));
}
};
class SSESub {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vsubq_f32(a, b);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_sub(a, b);
}
};
class SSEMul {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
return vmulq_f32(a, b);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_mul(a, b);
}
};
class SSEDiv {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
float32x4_t tmp;
tmp = vrecpeq_f32(b);
return vmulq_f32(a, tmp);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_div(a, b);
}
};
class SSESquaredDiff {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
float32x4_t tmp;
tmp = vsubq_f32(a, b);
return vmulq_f32(tmp, tmp);
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_mul(hl_vec_sub(a, b), hl_vec_sub(a, b));
}
};
class SSEFirst {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return a;
}
};
class SSESecond {
public:
static const bool sse = true;
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
static const bool sse = VECTOR_SIMD;
INLINE vecType vecOp(const vecType a, const vecType b) const {
return b;
}
};
class SSEClassificationError {
public:
static const bool sse = true;
static const bool sse = VECTOR_SIMD;
const real p;
float32x4_t mp;
uint32x4_t result;
vecType mp;
vecType result;
public:
explicit SSEClassificationError(const real s) : p(s) {
mp = vdupq_n_f32(p);
result = vdupq_n_u32(1);
mp = hl_vec_set(p);
result = hl_vec_set(1.0f);
}
// TODO: to be check
INLINE float32x4_t vecOp(const float32x4_t a, const float32x4_t b) const {
uint32x4_t tmp1 = vcgtq_f32(a, mp);
uint32x4_t tmp2 = vcgtq_f32(b, mp);
uint32x4_t tmp3 = veorq_u32(tmp1, tmp2);
return vcvtq_f32_u32(vandq_u32(tmp3, result));
INLINE vecType vecOp(const vecType a, const vecType b) const {
return hl_vec_classification_error(a, b, mp, result);
}
};
} // namespace binary
} // namespace base
#endif /* HL_MATRIX_BASE_NEON_CUH_ */
#endif /* HL_MATRIX_BASE_DETAIL_CUH_ */
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_MATRIX_BASE_SSE_CUH_
#define HL_MATRIX_BASE_SSE_CUH_
namespace aggregate {
class SSESum {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_add_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_add_pd(a, b);
}
};
class SSEMax {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_max_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_max_pd(a, b);
}
};
class SSEMin {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_min_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_min_pd(a, b);
}
};
} // namespace aggregate
namespace base {
namespace unary {
class SSEIdentity {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a) const {
return a;
}
INLINE __m128d vecOp(const __m128d a) const {
return a;
}
};
} // namespace unary
namespace binary {
class SSEAdd {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_add_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_add_pd(a, b);
}
};
class SSEAdd2 {
public:
static const bool sse = true;
const real p1;
const real p2;
union {__m128 f; __m128d d;} mp1;
union {__m128 f; __m128d d;} mp2;
public:
SSEAdd2(const real s1, const real s2) : p1(s1), p2(s2) {
if (sizeof(real) == sizeof(float)) {
mp1.f = _mm_set1_ps(p1);
mp2.f = _mm_set1_ps(p2);
} else {
mp1.d = _mm_set1_pd(p1);
mp2.d = _mm_set1_pd(p2);
}
}
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
__m128 tmp1, tmp2;
tmp1 = _mm_mul_ps(mp1.f, a);
tmp2 = _mm_mul_ps(mp2.f, b);
return _mm_add_ps(tmp1, tmp2);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
__m128d tmp1, tmp2;
tmp1 = _mm_mul_pd(mp1.d, a);
tmp2 = _mm_mul_pd(mp2.d, b);
return _mm_add_pd(tmp1, tmp2);
}
};
class SSESub {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_sub_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_sub_pd(a, b);
}
};
class SSEMul {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_mul_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_mul_pd(a, b);
}
};
class SSEDiv {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_div_ps(a, b);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_div_pd(a, b);
}
};
class SSESquaredDiff {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return _mm_mul_ps(_mm_sub_ps(a, b), _mm_sub_ps(a, b));
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return _mm_mul_pd(_mm_sub_pd(a, b), _mm_sub_pd(a, b));
}
};
class SSEFirst {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return a;
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return a;
}
};
class SSESecond {
public:
static const bool sse = true;
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
return b;
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
return b;
}
};
class SSEClassificationError {
public:
static const bool sse = true;
const real p;
union {__m128 f; __m128d d;} mp;
union {__m128 f; __m128d d;} result;
public:
explicit SSEClassificationError(const real s) : p(s) {
if (sizeof(real) == sizeof(float)) {
mp.f = _mm_set1_ps(p);
result.f = _mm_set1_ps(1.0f);
} else {
mp.d = _mm_set1_pd(p);
result.d = _mm_set1_pd(1.0);
}
}
INLINE __m128 vecOp(const __m128 a, const __m128 b) const {
__m128 tmp1 = _mm_cmpgt_ps(a, mp.f);
__m128 tmp2 = _mm_cmpgt_ps(b, mp.f);
__m128 tmp3 = _mm_xor_ps(tmp1, tmp2);
return _mm_and_ps(tmp3, result.f);
}
INLINE __m128d vecOp(const __m128d a, const __m128d b) const {
__m128d tmp1 = _mm_cmpgt_pd(a, mp.d);
__m128d tmp2 = _mm_cmpgt_pd(b, mp.d);
__m128d tmp3 = _mm_xor_pd(tmp1, tmp2);
return _mm_and_pd(tmp3, result.d);
}
};
} // namespace binary
} // namespace base
#endif /* HL_MATRIX_BASE_SSE_CUH_ */
......@@ -17,35 +17,31 @@ limitations under the License. */
#include "hl_base.h"
#if defined(__CUDA_ARCH__)
#ifdef __CUDA_ARCH__
/**
* CUDA kernel inline function
*/
#define INLINE __device__ inline
#else
/**
* CPP inline function
*/
#define INLINE inline
#endif
#ifdef __CUDA_ARCH__
#include <vector_types.h>
#ifndef PADDLE_TYPE_DOUBLE
typedef float4 vecType;
#else
typedef double2 vecType;
#endif
#elif (defined __ARM_NEON) || (defined __ARM_NEON__)
#include <arm_neon.h>
#ifndef PADDLE_TYPE_DOUBLE
typedef float32x4_t vecType;
#else
#error NEON instructions does not support double precision
#endif
#elif defined(__SSE3__)
#include "hl_cpu_simd_sse.cuh"
#elif (defined(__ARM_NEON) || defined(__ARM_NEON__)) && !defined(__NVCC__)
#include "hl_cpu_simd_neon.cuh"
#else
#include <mmintrin.h>
#include <xmmintrin.h>
#include <emmintrin.h>
#ifndef PADDLE_TYPE_DOUBLE
typedef __m128 vecType;
#else
typedef __m128d vecType;
#endif
#endif
#ifdef __CUDA_ARCH__
#define INLINE __device__ inline
#else
#define INLINE inline
#include "hl_cpu_scalar.cuh"
#endif
#endif // HL_MATRIX_TYPE_CUH_
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef HL_NEON_MATRIX_KERNEL_CUH_
#define HL_NEON_MATRIX_KERNEL_CUH_
#include "hl_matrix_type.cuh"
#define VECTOR_SIZE 16
/* number of float in vector */
#define VECTOR_LEN 4
#define VECTOR_SET vdupq_n_f32
inline bool hl_check_align(size_t size) {
return !(size & (VECTOR_SIZE - 1));
}
inline bool hl_check_align(void *ptr) {
return hl_check_align(reinterpret_cast<size_t>(ptr));
}
template <class Agg>
inline real hl_agg_op(Agg agg, vecType mm) {
float32x4_t rev = vrev64q_f32(mm);
float32x4_t tmp1 = agg.vecOp(rev, rev);
float32x2_t lo = vget_high_f32(rev);
float32x2_t hi = vget_low_f32(rev);
float32x4_t tmp2 = vcombine_f32(hi, lo);
float32x4_t ret = agg.vecOp(tmp1, tmp2);
return vgetq_lane_f32(ret, 0);
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda) {
for (int i = 0; i < dimM; i++, A += lda) {
vecType mm = VECTOR_SET(agg.init());
vecType *a = (vecType*)(A);
for (int j = 0; j < dimN / VECTOR_LEN; j++, a++) {
mm = agg.vecOp(mm, op.vecOp(*a));
}
int rem = dimN % VECTOR_LEN;
if (rem) {
real tmp = hl_agg_op(agg, mm);
real *a = A + (dimN / VECTOR_LEN) * VECTOR_LEN;
for (int j = 0; j < rem; j++) {
tmp = agg(tmp, op(a[j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
} else {
dst[i*ld] = sv(dst[i*ld], hl_agg_op(agg, mm));
}
}
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_row_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst, int ld,
real *A, int lda,
real *B, int ldb) {
for (int i = 0; i < dimM; i++, A += lda, B += ldb) {
vecType mm = VECTOR_SET(agg.init());
vecType *a = (vecType*)(A);
vecType *b = (vecType*)(B);
for (int j = 0; j < dimN / VECTOR_LEN; j++, a++, b++) {
mm = agg.vecOp(mm, op.vecOp(*a, *b));
}
int rem = dimN % VECTOR_LEN;
if (rem) {
real tmp = hl_agg_op(agg, mm);
real *a = A + (dimN / VECTOR_LEN) * VECTOR_LEN;
real *b = B + (dimN / VECTOR_LEN) * VECTOR_LEN;
for (int j = 0; j < rem; j++) {
tmp = agg(tmp, op(a[j], b[j]));
}
dst[i*ld] = sv(dst[i*ld], tmp);
} else {
dst[i*ld] = sv(dst[i*ld], hl_agg_op(agg, mm));
}
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
template <class Agg, class Op, class Saver>
void hl_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
for (int j = 0; j < dimN; j++) {
real tmp = agg.init();
for (int i = 0; i < dimM; i++) {
tmp = agg(tmp, op(A[i * lda + j], B[i * ldb + j]));
}
dst[j] = sv(dst[j], tmp);
}
}
/*
* MaxRow greater than or equal dimN
* dimN is multiples of VECTOR_LEN
* so rem <= MaxRow / VECTOR_LEN
*/
template <int MaxRow, class Agg, class Op, class Saver>
void hl_sse_column_op_with_rem(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
vecType mm[MaxRow / VECTOR_LEN];
for (int n = 0; n < MaxRow / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
int rem = dimN % VECTOR_LEN;
if (rem) {
A += (dimN / VECTOR_LEN) * VECTOR_LEN;
dst += (dimN / VECTOR_LEN) * VECTOR_LEN;
hl_matrix_column_op(agg, op, sv, dimM, rem, dst, A, lda);
}
}
/*
* dimN is multiples of VECTOR_LEN
* dimN greater than Step
*/
template <int Step, class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
for (int j = 0; j < dimN / Step; j++, dst += Step, A += Step) {
vecType mm[Step / VECTOR_LEN];
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
}
int remRow = dimN % Step;
if (remRow) {
hl_sse_column_op_with_rem<Step>(agg, op, sv, dimM, remRow, dst, A, lda);
}
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda) {
if (dimN <= 16) {
hl_sse_matrix_column_op<16>(agg, op, sv, dimM, dimN, dst, A, lda);
} else if (dimN <= 32) {
hl_sse_matrix_column_op<32>(agg, op, sv, dimM, dimN, dst, A, lda);
} else if (dimN <= 1024 || dimM <= 512) {
hl_sse_matrix_column_op<64>(agg, op, sv, dimM, dimN, dst, A, lda);
} else {
hl_sse_matrix_column_op<1024>(agg, op, sv, dimM, dimN, dst, A, lda);
}
}
template <int MaxRow, class Agg, class Op, class Saver>
void hl_sse_column_op_with_rem(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
vecType mm[MaxRow / VECTOR_LEN];
for (int n = 0; n < MaxRow / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
vecType *b = (vecType*)(B + i * ldb);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n], b[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < dimN / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
int rem = dimN % VECTOR_LEN;
if (rem) {
A += (dimN / VECTOR_LEN) * VECTOR_LEN;
B += (dimN / VECTOR_LEN) * VECTOR_LEN;
dst += (dimN / VECTOR_LEN) * VECTOR_LEN;
hl_matrix_column_op(agg, op, sv, dimM, rem, dst, A, lda, B, ldb);
}
}
template <int Step, class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
for (int j = 0; j < dimN / Step; j++, dst += Step, A += Step, B += Step) {
vecType mm[Step / VECTOR_LEN];
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = VECTOR_SET(agg.init());
}
for (int i = 0; i < dimM; i++) {
vecType *a = (vecType*)(A + i * lda);
vecType *b = (vecType*)(B + i * ldb);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
mm[n] = agg.vecOp(mm[n], op.vecOp(a[n], b[n]));
}
}
vecType *result = (vecType*)(dst);
for (int n = 0; n < Step / VECTOR_LEN; n++) {
result[n] = sv.vecOp(result[n], mm[n]);
}
}
int remRow = dimN % Step;
if (remRow) {
hl_sse_column_op_with_rem<Step>(
agg, op, sv, dimM, remRow, dst, A, lda, B, ldb);
}
}
template <class Agg, class Op, class Saver>
void hl_sse_matrix_column_op(Agg agg, Op op, Saver sv,
int dimM, int dimN,
real *dst,
real *A, int lda,
real *B, int ldb) {
if (dimN <= 16) {
hl_sse_matrix_column_op<16>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
} else if (dimN <= 32) {
hl_sse_matrix_column_op<32>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
} else if (dimN <= 1024 || dimM <= 512) {
hl_sse_matrix_column_op<64>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
} else {
hl_sse_matrix_column_op<1024>(agg, op, sv, dimM, dimN, dst, A, lda, B, ldb);
}
}
#endif /* HL_NEON_MATRIX_KERNEL_CUH_ */
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