Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 56e21116 编写于 作者: H Hong Ming
浏览文件
操作

add arm kernel and unit test for relu op

上级 505e450d 0fbcd4ea
隐藏空白更改
内联 并排
Showing with 708 addition and 22 deletion
paddle/fluid/lite/arm/math/CMakeLists.txt
浏览文件 @ 56e21116
......@@ -32,6 +32,7 @@ cc_library(math_arm SRCS
conv_winograd_3x3.cc
conv_winograd.cc
split.cc
activation.cc
DEPS ${lite_kernel_deps} eigen3 framework_proto_lite)
# TODO(TJ): fix me do not deps proto
......
paddle/fluid/lite/arm/math/activation.cc 0 → 100644
浏览文件 @ 56e21116
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include "paddle/fluid/lite/arm/math/activation.h"
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <>
void act_relu<float>(const float* din, float* dout, int size, int threads) {
int nums_per_thread = size / threads;
int remain = size - threads * nums_per_thread;
int neon_loop_cnt = nums_per_thread >> 4;
int neon_loop_remain = nums_per_thread - (neon_loop_cnt << 4);
float32x4_t vzero = vdupq_n_f32(0.f);
#pragma omp parallel for
for (int i = 0; i < threads; ++i) {
const float* ptr_in_thread = din + i * nums_per_thread;
float* ptr_out_thread = dout + i * nums_per_thread;
int cnt = neon_loop_cnt;
#ifdef __aarch64__
for (int num = 0; num < neon_loop_cnt; ++num) {
float32x4_t vr0 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr1 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr2 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr3 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
vr0 = vmaxq_f32(vr0, vzero);
vr1 = vmaxq_f32(vr1, vzero);
vr2 = vmaxq_f32(vr2, vzero);
vr3 = vmaxq_f32(vr3, vzero);
vst1q_f32(ptr_out_thread, vr0);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vr1);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vr2);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vr3);
ptr_out_thread += 4;
}
#else
if (cnt > 0) {
asm volatile(
"1: @ loop header\n"
"vld1.32 {d0-d3}, [%[din]]! @ load din 0\n"
"vld1.32 {d4-d7}, [%[din]]! @ load din 0\n"
"vmax.f32 q8, q0, %q[vzero] @ relu\n"
"vmax.f32 q9, q1, %q[vzero] @ relu\n"
"vmax.f32 q10, q2, %q[vzero] @ relu\n"
"vmax.f32 q11, q3, %q[vzero] @ relu\n"
"vst1.32 {d16-d19}, [%[dout]]! @ store result, add pointer\n"
"vst1.32 {d20-d23}, [%[dout]]! @ store result, add pointer\n"
"subs %[cnt], #1 @ loop count minus 1\n"
"bne 1b @ jump to main loop start "
"point\n"
: [dout] "+r"(ptr_out_thread), [din] "+r"(ptr_in_thread),
[cnt] "+r"(cnt)
: [vzero] "w"(vzero)
: "cc", "memory", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11");
}
#endif
for (int j = 0; j < neon_loop_remain; ++j) {
ptr_out_thread[0] = ptr_in_thread[0] > 0.f ? ptr_in_thread[0] : 0.f;
ptr_in_thread++;
ptr_out_thread++;
}
}
float* out_ptr_remain = dout + threads * nums_per_thread;
const float* in_ptr_remain = din + threads * nums_per_thread;
for (int j = 0; j < remain; ++j) {
out_ptr_remain[0] = in_ptr_remain[0] > 0.f ? in_ptr_remain[0] : 0.f;
in_ptr_remain++;
out_ptr_remain++;
}
}
template <>
void act_relu_neg<float>(const float* din, float* dout, int size,
const float negative_slope, int threads) {
int nums_per_thread = size / threads;
int remain = size - threads * nums_per_thread;
int neon_loop_cnt = nums_per_thread >> 4;
int neon_loop_remain = nums_per_thread - (neon_loop_cnt << 4);
float32x4_t vzero = vdupq_n_f32(0.f);
float32x4_t valpha = vdupq_n_f32(negative_slope);
#pragma omp parallel for
for (int i = 0; i < threads; ++i) {
const float* ptr_in_thread = din + i * nums_per_thread;
float* ptr_out_thread = dout + i * nums_per_thread;
int cnt = neon_loop_cnt;
#ifdef __aarch64__
for (int num = 0; num < neon_loop_cnt; ++num) {
float32x4_t vr0 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr1 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr2 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr3 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
uint32x4_t vm0 = vcgeq_f32(vr0, vzero);
uint32x4_t vm1 = vcgeq_f32(vr1, vzero);
uint32x4_t vm2 = vcgeq_f32(vr2, vzero);
uint32x4_t vm3 = vcgeq_f32(vr3, vzero);
float32x4_t vn0 = vmulq_f32(vr0, valpha);
float32x4_t vn1 = vmulq_f32(vr1, valpha);
float32x4_t vn2 = vmulq_f32(vr2, valpha);
float32x4_t vn3 = vmulq_f32(vr3, valpha);
float32x4_t vo0 = vbslq_f32(vm0, vr0, vn0);
float32x4_t vo1 = vbslq_f32(vm1, vr1, vn1);
float32x4_t vo2 = vbslq_f32(vm2, vr2, vn2);
float32x4_t vo3 = vbslq_f32(vm3, vr3, vn3);
vst1q_f32(ptr_out_thread, vo0);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vo1);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vo2);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vo3);
ptr_out_thread += 4;
}
#else
if (cnt > 0) {
asm volatile(
"1: @ loop header\n"
"vld1.32 {d0-d3}, [%[din]]! @ load din 0\n"
"vld1.32 {d4-d7}, [%[din]]! @ load din 0\n"
"vcge.f32 q8, q0, %q[vzero] @ get mask\n"
"vcge.f32 q9, q1, %q[vzero] @ get mask\n"
"vcge.f32 q10, q2, %q[vzero] @ get mask\n"
"vcge.f32 q11, q3, %q[vzero] @ get mask\n"
"vmul.f32 q4, q0, %q[valpha] @ get neg data\n"
"vmul.f32 q5, q1, %q[valpha] @ get neg data\n"
"vmul.f32 q6, q2, %q[valpha] @ get neg data\n"
"vmul.f32 q7, q3, %q[valpha] @ get neg data\n"
"vbit q4, q0, q8 @ bitsel, insert q0 to q4, "
"if q8 is 1\n"
"vbit q5, q1, q9 @ bitsel, insert q1 to q5, "
"if q9 is 1\n"
"vbit q6, q2, q10 @ bitsel, insert q2 to q6, "
"if q10 is 1\n"
"vbit q7, q3, q11 @ bitsel, insert q3 to q7, "
"if q11 is 1\n"
"vst1.32 {d8-d11}, [%[dout]]! @ store result, add pointer\n"
"vst1.32 {d12-d15}, [%[dout]]! @ store result, add pointer\n"
"subs %[cnt], #1 @ loop count minus 1\n"
"bne 1b @ jump to main loop start "
"point\n"
: [dout] "+r"(ptr_out_thread), [din] "+r"(ptr_in_thread),
[cnt] "+r"(cnt)
: [vzero] "w"(vzero), [valpha] "w"(valpha)
: "cc", "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q8", "q9", "q10", "q11");
}
#endif
for (int j = 0; j < neon_loop_remain; ++j) {
ptr_out_thread[0] = ptr_in_thread[0] > 0.f
? ptr_in_thread[0]
: ptr_in_thread[0] * negative_slope;
ptr_in_thread++;
ptr_out_thread++;
}
}
float* out_ptr_remain = dout + threads * nums_per_thread;
const float* in_ptr_remain = din + threads * nums_per_thread;
for (int j = 0; j < remain; ++j) {
out_ptr_remain[0] = in_ptr_remain[0] > 0.f
? in_ptr_remain[0]
: in_ptr_remain[0] * negative_slope;
in_ptr_remain++;
out_ptr_remain++;
}
}
template <>
void act_clipped_relu<float>(const float* din, float* dout, int size,
const float coef, int threads) {
int nums_per_thread = size / threads;
int remain = size - threads * nums_per_thread;
int neon_loop_cnt = nums_per_thread >> 4;
int neon_loop_remain = nums_per_thread - (neon_loop_cnt << 4);
float32x4_t vzero = vdupq_n_f32(0.f);
float32x4_t vclip = vdupq_n_f32(coef);
#pragma omp parallel for
for (int i = 0; i < threads; ++i) {
const float* ptr_in_thread = din + i * nums_per_thread;
float* ptr_out_thread = dout + i * nums_per_thread;
int cnt = neon_loop_cnt;
#ifdef __aarch64__
for (int num = 0; num < neon_loop_cnt; ++num) {
float32x4_t vr0 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr1 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr2 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vr3 = vld1q_f32(ptr_in_thread);
ptr_in_thread += 4;
float32x4_t vt0 = vmaxq_f32(vr0, vzero);
float32x4_t vt1 = vmaxq_f32(vr1, vzero);
float32x4_t vt2 = vmaxq_f32(vr2, vzero);
float32x4_t vt3 = vmaxq_f32(vr3, vzero);
float32x4_t vo0 = vminq_f32(vt0, vclip);
float32x4_t vo1 = vminq_f32(vt1, vclip);
float32x4_t vo2 = vminq_f32(vt2, vclip);
float32x4_t vo3 = vminq_f32(vt3, vclip);
vst1q_f32(ptr_out_thread, vo0);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vo1);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vo2);
ptr_out_thread += 4;
vst1q_f32(ptr_out_thread, vo3);
ptr_out_thread += 4;
}
#else
if (cnt > 0) {
asm volatile(
"1: @ loop header\n"
"vld1.32 {d0-d3}, [%[din]]! @ load din 0\n"
"vld1.32 {d4-d7}, [%[din]]! @ load din 0\n"
"vmax.f32 q8, q0, %q[vzero] @ relu\n"
"vmax.f32 q9, q1, %q[vzero] @ relu\n"
"vmax.f32 q10, q2, %q[vzero] @ relu\n"
"vmax.f32 q11, q3, %q[vzero] @ relu\n"
"vmin.f32 q4, q8, %q[vclip] @ clip relu\n"
"vmin.f32 q5, q9, %q[vclip] @ clip relu\n"
"vmin.f32 q6, q10, %q[vclip] @ clip relu\n"
"vmin.f32 q7, q11, %q[vclip] @ clip relu\n"
"vst1.32 {d8-d11}, [%[dout]]! @ store result, add pointer\n"
"vst1.32 {d12-d15}, [%[dout]]! @ store result, add pointer\n"
"subs %[cnt], #1 @ loop count minus 1\n"
"bne 1b @ jump to main loop start "
"point\n"
: [dout] "+r"(ptr_out_thread), [din] "+r"(ptr_in_thread),
[cnt] "+r"(cnt)
: [vzero] "w"(vzero), [vclip] "w"(vclip)
: "cc", "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q8", "q9", "q10", "q11");
}
#endif
for (int j = 0; j < neon_loop_remain; ++j) {
ptr_out_thread[0] = ptr_in_thread[0] > 0.f ? ptr_in_thread[0] : 0.f;
ptr_out_thread[0] = ptr_out_thread[0] < coef ? ptr_out_thread[0] : coef;
ptr_in_thread++;
ptr_out_thread++;
}
}
float* out_ptr_remain = dout + threads * nums_per_thread;
const float* in_ptr_remain = din + threads * nums_per_thread;
for (int j = 0; j < remain; ++j) {
out_ptr_remain[0] = in_ptr_remain[0] > 0.f ? in_ptr_remain[0] : 0.f;
out_ptr_remain[0] = out_ptr_remain[0] < coef ? out_ptr_remain[0] : coef;
in_ptr_remain++;
out_ptr_remain++;
}
}
template <>
void act_prelu<float>(const float* din, float* dout, int outer_size,
int channel_size, int inner_size, bool channel_shared,
float* channel_slope, int threads) {
int stride_size = inner_size * channel_size;
int cnt = inner_size >> 4;
int remain = inner_size & 15;
float32x4_t vzero = vdupq_n_f32(0.f);
for (int n = 0; n < outer_size; n++) {
const float* data_in_batch = din + n * stride_size;
float* data_out_batch = dout + n * stride_size;
#pragma omp parallel for
for (int c = 0; c < channel_size; c++) {
const float* data_in_c = data_in_batch + c * inner_size;
float* data_out_c = data_out_batch + c * inner_size;
float slope = channel_shared ? channel_slope[0] : channel_slope[c];
float32x4_t vslope = vdupq_n_f32(slope);
#ifdef __aarch64__
for (int i = 0; i < cnt; ++i) {
float32x4_t vr0 = vld1q_f32(data_in_c);
float32x4_t vr1 = vld1q_f32(data_in_c + 4);
float32x4_t vr2 = vld1q_f32(data_in_c + 8);
float32x4_t vr3 = vld1q_f32(data_in_c + 12);
uint32x4_t vm0 = vcltq_f32(vr0, vzero); // vr0 <= vzero
uint32x4_t vm1 = vcltq_f32(vr1, vzero); // vr0 <= vzero
uint32x4_t vm2 = vcltq_f32(vr2, vzero); // vr0 <= vzero
uint32x4_t vm3 = vcltq_f32(vr3, vzero); // vr0 <= vzero
float32x4_t vo0 = vmulq_f32(vr0, vslope); // vr0 * vslope
float32x4_t vo1 = vmulq_f32(vr1, vslope); // vr0 * vslope
float32x4_t vo2 = vmulq_f32(vr2, vslope); // vr0 * vslope
float32x4_t vo3 = vmulq_f32(vr3, vslope); // vr0 * vslope
float32x4_t vos0 = vbslq_f32(vm0, vo0, vr0);
float32x4_t vos1 = vbslq_f32(vm1, vo1, vr1);
float32x4_t vos2 = vbslq_f32(vm2, vo2, vr2);
float32x4_t vos3 = vbslq_f32(vm3, vo3, vr3);
vst1q_f32(data_out_c, vos0);
vst1q_f32(data_out_c + 4, vos1);
vst1q_f32(data_out_c + 8, vos2);
vst1q_f32(data_out_c + 12, vos3);
data_in_c += 16;
data_out_c += 16;
}
#else
int cnt_loop = cnt;
if (cnt_loop > 0) {
asm volatile(
"vld1.32 {d0-d3}, [%[ptr_in]]! @ load "
"input to q0, q1\n"
"pld [%[ptr_in]] @ preload\n"
"pld [%[ptr_in], #64] @ preload\n"
"pld [%[ptr_in], #128] @ preload\n"
"pld [%[ptr_in], #192] @ preload\n"
"1: @main loop\n"
"vld1.32 {d4-d7}, [%[ptr_in]]! @ load input to "
"q2, q3\n"
"vclt.f32 q8, q0, %q[vzero] @vcle q0 <= vzero\n"
"vclt.f32 q9, q1, %q[vzero] @vcle q1 <= vzero\n"
"vmul.f32 q10, q0, %q[vslope] @vmul q0 * vslope\n"
"vmul.f32 q11, q1, %q[vslope] @vmul q1 * vslope\n"
"vclt.f32 q12, q2, %q[vzero] @vcle q2 <= vzero\n"
"vclt.f32 q13, q3, %q[vzero] @vcle q3 <= vzero\n"
"vmul.f32 q14, q2, %q[vslope] @vmul q2 * vslope\n"
"vmul.f32 q15, q3, %q[vslope] @vmul q3 * vslope\n"
"vbif.32 q10, q0, q8 @vbit q10, q0, q8\n"
"vbif.32 q11, q1, q9 @vbit q11, q1, q9\n"
"vbif.32 q14, q2, q12 @vbit q14, q2, "
"q12\n"
"vbif.32 q15, q3, q13 @vbit q15, q3, "
"q13\n"
"subs %[cnt], #1 @subs nn, 1\n"
"vld1.32 {d0-d3}, [%[ptr_in]]! @ load input to "
"q0, q1\n"
"vst1.f32 {d20-d23}, [%[dout]]! @store data\n"
"vst1.f32 {d28-d31}, [%[dout]]! @store data\n"
"bne 1b @bne nn\n"
"sub %[ptr_in], #32 @ ptr-32\n"
: [ptr_in] "+r"(data_in_c), [cnt] "+r"(cnt_loop),
[dout] "+r"(data_out_c)
: [vzero] "w"(vzero), [vslope] "w"(vslope)
: "cc", "memory", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11",
"q12", "q13", "q14", "q15");
}
#endif // __aarch64__
for (int i = remain; i > 0; i--) {
*(data_out_c++) =
data_in_c[0] > 0.f ? data_in_c[0] : data_in_c[0] * slope;
data_in_c++;
}
}
}
}
template <>
void act_sigmoid(const float* din, float* dout, int size, int threads) {
int nums_per_thread = size / threads;
int remain = size - threads * nums_per_thread;
int neon_loop_cnt_dim4 = nums_per_thread >> 2;
int neon_loop_remain_dim4 = nums_per_thread - (neon_loop_cnt_dim4 << 2);
float32x4_t vzero = vdupq_n_f32(0.f);
#pragma omp parallel for
for (int i = 0; i < threads; ++i) {
float32x4_t exp_vec = vdupq_n_f32(0.0f);
float32x4_t recip = vdupq_n_f32(0.0f);
const float* ptr_in_thread = din + i * nums_per_thread;
float* ptr_out_thread = dout + i * nums_per_thread;
for (int k = 0; k < neon_loop_cnt_dim4; ++k) {
exp_vec = exp_ps(vnegq_f32(vld1q_f32(ptr_in_thread)));
exp_vec = vaddq_f32(exp_vec, vdupq_n_f32(1.0f));
recip = vrecpeq_f32(exp_vec);
recip = vmulq_f32(vrecpsq_f32(exp_vec, recip), recip);
recip = vmulq_f32(vrecpsq_f32(exp_vec, recip), recip);
vst1q_f32(ptr_out_thread, recip);
ptr_out_thread += 4;
ptr_in_thread += 4;
}
for (int j = 0; j < neon_loop_remain_dim4; ++j) {
ptr_out_thread[0] = 1.f / (1 + expf(-ptr_in_thread[0]));
ptr_in_thread++;
ptr_out_thread++;
}
}
float* ptr_out = dout + threads * nums_per_thread;
const float* ptr_in = din + threads * nums_per_thread;
for (int j = 0; j < remain; ++j) {
ptr_out[0] = 1.f / (1 + expf(-ptr_in[0]));
ptr_in++;
ptr_out++;
}
}
// tanh : (exp(x) - exp(-x)) / (exp(x) + exp(-x))
template <>
void act_tanh<float>(const float* din, float* dout, int size, int threads) {
int nums_per_thread = size / threads;
int remain = size - threads * nums_per_thread;
int neon_loop_cnt_dim4 = nums_per_thread >> 2;
int neon_loop_remain_dim4 = nums_per_thread - (neon_loop_cnt_dim4 << 2);
#pragma omp parallel for
for (int i = 0; i < threads; ++i) {
float32x4_t exp_plus_vec = vdupq_n_f32(0.0f);
float32x4_t exp_minus_vec = vdupq_n_f32(0.0f);
float32x4_t exp_sum_vec = vdupq_n_f32(0.0f);
float32x4_t exp_diff_vec = vdupq_n_f32(0.0f);
float32x4_t recip = vdupq_n_f32(0.0f);
const float* ptr_in_thread = din + i * nums_per_thread;
float* ptr_out_thread = dout + i * nums_per_thread;
for (int k = 0; k < neon_loop_cnt_dim4; ++k) {
exp_plus_vec = exp_ps(vld1q_f32(ptr_in_thread));
exp_minus_vec = exp_ps(vnegq_f32(vld1q_f32(ptr_in_thread)));
exp_sum_vec = vaddq_f32(exp_plus_vec, exp_minus_vec);
exp_diff_vec = vsubq_f32(exp_plus_vec, exp_minus_vec);
recip = div_ps(exp_diff_vec, exp_sum_vec);
vst1q_f32(ptr_out_thread, recip);
ptr_out_thread += 4;
ptr_in_thread += 4;
}
for (int j = 0; j < neon_loop_remain_dim4; ++j) {
ptr_out_thread[0] = (expf(ptr_in_thread[0]) - expf(-ptr_in_thread[0])) /
(expf(ptr_in_thread[0]) + expf(-ptr_in_thread[0]));
ptr_in_thread++;
ptr_out_thread++;
}
}
float* ptr_out = dout + threads * nums_per_thread;
const float* ptr_in = din + threads * nums_per_thread;
for (int j = 0; j < remain; ++j) {
ptr_out[0] = (expf(ptr_in[0]) - expf(-ptr_in[0])) /
(expf(ptr_in[0]) + expf(-ptr_in[0]));
ptr_in++;
ptr_out++;
}
}
// swish: x /(1 + exp(-(b * x)))
template <>
void act_swish<float>(const float* din, float* dout, int size, const float coef,
int threads) {
int nums_per_thread = size / threads;
int remain = size - threads * nums_per_thread;
int neon_loop_cnt_dim4 = nums_per_thread >> 2;
int neon_loop_remain_dim4 = nums_per_thread - (neon_loop_cnt_dim4 << 2);
const float beta = coef;
float32x4_t vbeta = vdupq_n_f32(beta);
float32x4_t vone = vdupq_n_f32(1.f);
#pragma omp parallel for
for (int i = 0; i < threads; ++i) {
const float* ptr_in_thread = din + i * nums_per_thread;
float* ptr_out_thread = dout + i * nums_per_thread;
for (int k = 0; k < neon_loop_cnt_dim4; ++k) {
float32x4_t va = vld1q_f32(ptr_in_thread); // x
float32x4_t vb = vnegq_f32(vld1q_f32(ptr_in_thread)); // -x
float32x4_t vsum = vmulq_f32(vb, vbeta);
vsum = exp_ps(vsum);
float32x4_t vc = vaddq_f32(vone, vsum);
float32x4_t vrst = div_ps(va, vc);
vst1q_f32(ptr_out_thread, vrst);
ptr_out_thread += 4;
ptr_in_thread += 4;
}
for (int j = 0; j < neon_loop_remain_dim4; ++j) {
ptr_out_thread[0] =
ptr_in_thread[0] / (1.0 + expf(-ptr_in_thread[0] * beta));
ptr_in_thread++;
ptr_out_thread++;
}
}
float* ptr_out = dout + threads * nums_per_thread;
const float* ptr_in = din + threads * nums_per_thread;
for (int j = 0; j < remain; ++j) {
ptr_out[0] = ptr_in[0] / (1.0 + expf(-ptr_in[0] * beta));
ptr_in++;
ptr_out++;
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/activation.h 0 → 100644
浏览文件 @ 56e21116
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#pragma once
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void act_relu(const T* din, T* dout, int size, int threads);
template <typename T>
void act_relu_neg(const T* din, T* dout, int size, const float negative_slope,
int threads);
template <typename T>
void act_clipped_relu(const T* din, T* dout, int size, const float coef,
int threads);
template <typename T>
void act_prelu(const T* din, T* dout, int outer_size, int channel_size,
int inner_size, bool channel_shared, float* channel_slope,
int threads);
template <typename T>
void act_sigmoid(const T* din, T* dout, int size, int threads);
template <typename T>
void act_tanh(const T* din, T* dout, int size, int threads);
template <typename T>
void act_swish(const T* din, T* dout, int size, const float coef, int threads);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/kernels/arm/CMakeLists.txt
浏览文件 @ 56e21116
......@@ -5,7 +5,7 @@ endif()
message(STATUS "compile with lite ARM kernels")
cc_library(fc_compute_arm SRCS fc_compute.cc DEPS ${lite_kernel_deps} math_arm)
cc_library(relu_compute_arm SRCS relu_compute.cc DEPS ${lite_kernel_deps})
cc_library(activation_compute_arm SRCS activation_compute.cc DEPS ${lite_kernel_deps} math_arm)
cc_library(mul_compute_arm SRCS mul_compute.cc DEPS ${lite_kernel_deps} math_arm)
cc_library(scale_compute_arm SRCS scale_compute.cc DEPS ${lite_kernel_deps} math_arm)
cc_library(softmax_compute_arm SRCS softmax_compute.cc DEPS ${lite_kernel_deps} math_arm)
......@@ -16,6 +16,7 @@ cc_library(pool_compute_arm SRCS pool_compute.cc DEPS ${lite_kernel_deps} math_a
cc_library(split_compute_arm SRCS split_compute.cc DEPS ${lite_kernel_deps} math_arm)
lite_cc_test(test_fc_compute_arm SRCS fc_compute_test.cc DEPS fc_compute_arm math_arm)
lite_cc_test(test_activation_compute_arm SRCS activation_compute_test.cc DEPS activation_compute_arm)
lite_cc_test(test_scale_compute_arm SRCS scale_compute_test.cc DEPS scale_compute_arm)
lite_cc_test(test_softmax_compute_arm SRCS softmax_compute_test.cc DEPS softmax_compute_arm)
lite_cc_test(test_conv_compute_arm SRCS conv_compute_test.cc DEPS conv_compute_arm)
......@@ -27,7 +28,7 @@ lite_cc_test(test_split_compute_arm SRCS split_compute_test.cc DEPS split_comput
set(arm_kernels
fc_compute_arm
relu_compute_arm
activation_compute_arm
mul_compute_arm
scale_compute_arm
softmax_compute_arm
......
paddle/fluid/lite/kernels/arm/relu_compute.h → paddle/fluid/lite/kernels/arm/activation_compute.cc
浏览文件 @ 56e21116
......@@ -12,31 +12,23 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/kernels/arm/activation_compute.h"
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace arm {
class ReluCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
void Run() override {
auto& param = Param<operators::ReluParam>();
auto n = param.input->dims().production();
const float* input = param.input->data<float>();
float* output = param.output->mutable_data<float>();
for (int i = 0; i < n; i++) {
output[i] = std::max(0.f, input[i]);
}
}
TargetType target() const override { return TARGET(kARM); }
PrecisionType precision() const override { return PRECISION(kFloat); }
};
void ReluCompute::Run() {
auto& param = this->Param<param_t>();
auto& ctx = this->ctx_->template As<ARMContext>();
auto x_dims = param.X->dims();
auto x_data = param.X->data<float>();
auto output_data = param.Out->mutable_data<float>();
lite::arm::math::act_relu<float>(x_data, output_data, x_dims.production(),
ctx.threads());
}
} // namespace arm
} // namespace kernels
......
paddle/fluid/lite/kernels/arm/relu_compute.cc → paddle/fluid/lite/kernels/arm/activation_compute.h
浏览文件 @ 56e21116
......@@ -12,4 +12,26 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/lite/kernels/arm/relu_compute.h"
#pragma once
#include <algorithm>
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/op_registry.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace arm {
class ReluCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using param_t = operators::ActivationParam;
void Run() override;
virtual ~ReluCompute() = default;
};
} // namespace arm
} // namespace kernels
} // namespace lite
} // namespace paddle
paddle/fluid/lite/kernels/arm/activation_compute_test.cc 0 → 100644
浏览文件 @ 56e21116
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
#include "paddle/fluid/lite/kernels/arm/activation_compute.h"
#include <gtest/gtest.h>
#include <memory>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/core/op_registry.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace arm {
template <typename dtype>
void activation_compute_ref(const operators::ActivationParam& param) {
auto x_data = param.X->data<dtype>();
auto output_data = param.Out->mutable_data<dtype>();
DDim x_dims = param.X->dims();
DDim output_dims = param.Out->dims();
ASSERT_EQ(x_dims.data(), output_dims.data());
for (int i = 0; i < output_dims.production(); i++) {
output_data[i] = std::max(0.f, x_data[i]);
}
}
TEST(activation_arm, retrive_op) {
auto activation =
KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>("relu");
ASSERT_FALSE(activation.empty());
ASSERT_TRUE(activation.front());
}
TEST(activation_arm, init) {
ReluCompute activation;
ASSERT_EQ(activation.precision(), PRECISION(kFloat));
ASSERT_EQ(activation.target(), TARGET(kARM));
}
TEST(activation_arm, compute) {
DeviceInfo::Init();
for (auto n : {1, 2}) {
for (auto c : {6, 32 /*, 128*/}) {
for (auto h : {9, 18 /*, 56 , 112, 224, 512*/}) {
for (auto w : {9, 18 /*, 56, 112, 224, 512*/}) {
Tensor x;
Tensor output;
Tensor output_ref;
// set the dims of input, output, ref output tensors
x.Resize({n, c, h, w});
output.Resize({n, c, h, w});
output_ref.Resize({n, c, h, w});
// initialize the data of input tensors
auto* x_data = x.mutable_data<float>();
auto* output_data = output.mutable_data<float>();
for (int i = 0; i < x.dims().production(); i++) {
float sign = i % 3 == 0 ? -1.0f : 1.0f;
x_data[i] = sign * static_cast<float>(i % 128) * 0.013f;
}
// prepare kernel params and run
ReluCompute activation;
std::unique_ptr<KernelContext> ctx(new KernelContext);
ctx->As<ARMContext>();
activation.SetContext(std::move(ctx));
operators::ActivationParam param;
param.X = &x;
param.Out = &output;
activation.SetParam(param);
activation.Launch();
// invoking ref implementation and compare results
param.Out = &output_ref;
activation_compute_ref<float>(param);
auto* output_ref_data = output_ref.mutable_data<float>();
for (int i = 0; i < output.dims().production(); i++) {
EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
}
}
}
}
}
}
} // namespace arm
} // namespace kernels
} // namespace lite
} // namespace paddle
USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或