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提交 b45d020f 编写于 作者: H hedaoyuan 提交者： GitHub
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Merge pull request #3718 from hedaoyuan/convolution 

Depthwise Convolution Optimization
上级 47eb8691 168707ca
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Showing with 750 addition and 6 deletion
paddle/function/CMakeLists.txt
浏览文件 @ b45d020f
...@@ -21,6 +21,8 @@ if(USE_NNPACK) ...@@ -21,6 +21,8 @@ if(USE_NNPACK)
endif() endif()
endif() endif()
list(APPEND cpp_files neon/NeonDepthwiseConv.cpp)
add_library(paddle_function STATIC ${cpp_files} ${cu_objs}) add_library(paddle_function STATIC ${cpp_files} ${cu_objs})
add_dependencies(paddle_function ${external_project_dependencies}) add_dependencies(paddle_function ${external_project_dependencies})
add_dependencies(paddle_function paddle_proto) add_dependencies(paddle_function paddle_proto)
...@@ -42,11 +44,11 @@ if(WITH_GPU) ...@@ -42,11 +44,11 @@ if(WITH_GPU)
add_simple_unittest(RowConvOpTest) add_simple_unittest(RowConvOpTest)
add_simple_unittest(BlockExpandOpTest) add_simple_unittest(BlockExpandOpTest)
add_simple_unittest(CropOpTest) add_simple_unittest(CropOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif() endif()
add_simple_unittest(Im2ColTest) add_simple_unittest(Im2ColTest)
add_simple_unittest(GemmConvOpTest) add_simple_unittest(GemmConvOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif() endif()
add_style_check_target(paddle_function ${h_files}) add_style_check_target(paddle_function ${h_files})
......
paddle/function/DepthwiseConvOpTest.cpp
浏览文件 @ b45d020f
...@@ -34,4 +34,13 @@ TEST(DepthwiseConv, BackwardFilter) { ...@@ -34,4 +34,13 @@ TEST(DepthwiseConv, BackwardFilter) {
} }
#endif #endif
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
TEST(DepthwiseConv, Forward) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"GemmConv-CPU", "NeonDepthwiseConv-CPU", forward);
}
#endif
} // namespace paddle } // namespace paddle
paddle/function/Im2Col.h
浏览文件 @ b45d020f
...@@ -16,6 +16,7 @@ limitations under the License. */ ...@@ -16,6 +16,7 @@ limitations under the License. */
#include "TensorShape.h" #include "TensorShape.h"
#include "TensorType.h" #include "TensorType.h"
#include "neon/neon_util.h"
namespace paddle { namespace paddle {
...@@ -93,4 +94,95 @@ public: ...@@ -93,4 +94,95 @@ public:
int paddingWidth); int paddingWidth);
}; };
template <class T>
struct Padding {
static void run(const T* src,
T* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
memcpy(dest, src, inputWidth * sizeof(T));
dest += inputWidth;
src += inputWidth;
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
}
}
};
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <>
struct Padding<float> {
static void run(const float* src,
float* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
int step = inputWidth >> 2;
int remain = inputWidth & 3;
for (int s = 0; s < step; s++) {
float32x4_t s0 = vld1q_f32(src);
vst1q_f32(dest, s0);
src += 4;
dest += 4;
}
for (int r = 0; r < remain; r++) {
*dest++ = *src++;
}
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
}
}
};
#endif
} // namespace paddle } // namespace paddle
paddle/function/neon/NeonDepthwiseConv.cpp 0 → 100644
浏览文件 @ b45d020f
/* 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. */
#include "neon_util.h"
#include "paddle/function/ConvOp.h"
#include "paddle/function/Im2Col.h"
namespace paddle {
namespace neon {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <int filterSize, int stride>
struct DepthwiseConvKernel {};
inline float32_t conv3x3(float32x4_t r0,
float32x4_t r1,
float32x4_t r2,
float32x4_t k0,
float32x4_t k1,
float32x4_t k2) {
float32x4_t tmp;
tmp = vmulq_f32(r0, k0);
tmp = vmlaq_f32(tmp, r1, k1);
tmp = vmlaq_f32(tmp, r2, k2);
return vaddvq_f32(tmp);
}
inline float32_t conv4x4(float32x4_t r0,
float32x4_t r1,
float32x4_t r2,
float32x4_t r3,
float32x4_t k0,
float32x4_t k1,
float32x4_t k2,
float32x4_t k3) {
float32x4_t tmp;
tmp = vmulq_f32(r0, k0);
tmp = vmlaq_f32(tmp, r1, k1);
tmp = vmlaq_f32(tmp, r2, k2);
tmp = vmlaq_f32(tmp, r3, k3);
return vaddvq_f32(tmp);
}
/**
* Each step calculates four elements of the output.
* First step:
* R0[0, 1, 2, 3...] * K[0][0]
* R0[1, 2, 3, 4...] * K[0][1]
* R0[2, 3, 4, 5...] * K[0][2]
* R1[0, 1, 2, 3...] * K[1][0]
* R1[1, 2, 3, 4...] * K[1][1]
* R1[2, 3, 4, 5...] * K[1][2]
* R2[0, 1, 2, 3...] * K[2][0]
* R2[1, 2, 3, 4...] * K[2][1]
* + R2[2, 3, 4, 5...] * K[2][2]
* ------------------------------
* Output[0, 1, 2, 3]
*/
template <>
struct DepthwiseConvKernel<3, 1> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 9) {
// Load the filters
float32x4_t k[3];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 3);
k[2] = vld1q_f32(filterData + 6);
k[0] = vsetq_lane_f32(0.f, k[0], 3);
k[1] = vsetq_lane_f32(0.f, k[1], 3);
k[2] = vsetq_lane_f32(0.f, k[2], 3);
const float* r0 =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
const float* r1 = r0 + inputWidth;
const float* r2 = r0 + inputWidth * 2;
float32x4_t input[3][3];
for (int h = 0; h < outputHeight; h++) {
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t tmp;
input[0][0] = vld1q_f32(r0);
tmp = vld1q_f32(r0 + 4);
input[0][1] = vextq_f32(input[0][0], tmp, 1);
input[0][2] = vextq_f32(input[0][0], tmp, 2);
input[1][0] = vld1q_f32(r1);
tmp = vld1q_f32(r1 + 4);
input[1][1] = vextq_f32(input[1][0], tmp, 1);
input[1][2] = vextq_f32(input[1][0], tmp, 2);
input[2][0] = vld1q_f32(r2);
tmp = vld1q_f32(r2 + 4);
input[2][1] = vextq_f32(input[2][0], tmp, 1);
input[2][2] = vextq_f32(input[2][0], tmp, 2);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 4;
r1 += 4;
r2 += 4;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
r0++;
r1++;
r2++;
outputData++;
}
r0 += 2;
r1 += 2;
r2 += 2;
}
}
}
};
/**
* Each step calculates four elements of the output.
* First step:
* R0[0, 2, 4, 6...] * K[0][0]
* R0[1, 3, 5, 7...] * K[0][1]
* R0[2, 4, 6, 8...] * K[0][2]
* R1[0, 2, 4, 6...] * K[1][0]
* R1[1, 3, 5, 7...] * K[1][1]
* R1[2, 4, 6, 8...] * K[1][2]
* R2[0, 2, 4, 6...] * K[2][0]
* R2[1, 3, 5, 7...] * K[2][1]
* R2[2, 4, 6, 8...] * K[2][2]
* ------------------------------
* Output[0, 1, 2, 3]
*/
template <>
struct DepthwiseConvKernel<3, 2> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 9) {
// Load the filters
float32x4_t k[3];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 3);
k[2] = vld1q_f32(filterData + 6);
k[0] = vsetq_lane_f32(0.f, k[0], 3);
k[1] = vsetq_lane_f32(0.f, k[1], 3);
k[2] = vsetq_lane_f32(0.f, k[2], 3);
const float* start =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
float32x4_t input[3][3];
for (int h = 0; h < outputHeight; h++) {
const float* r0 = start + 2 * h * inputWidth;
const float* r1 = start + (2 * h + 1) * inputWidth;
const float* r2 = start + (2 * h + 2) * inputWidth;
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t data1;
float32x4x2_t data2;
data2 = vld2q_f32(r0);
input[0][0] = data2.val[0];
input[0][1] = data2.val[1];
data1 = vld1q_f32(r0 + 8);
input[0][2] = vextq_f32(data2.val[0], data1, 1);
data2 = vld2q_f32(r1);
input[1][0] = data2.val[0];
input[1][1] = data2.val[1];
data1 = vld1q_f32(r1 + 8);
input[1][2] = vextq_f32(data2.val[0], data1, 1);
data2 = vld2q_f32(r2);
input[2][0] = data2.val[0];
input[2][1] = data2.val[1];
data1 = vld1q_f32(r2 + 8);
input[2][2] = vextq_f32(data2.val[0], data1, 1);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 8;
r1 += 8;
r2 += 8;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
r0 += 2;
r1 += 2;
r2 += 2;
outputData++;
}
}
}
}
};
/**
* Each step calculates four elements of the output.
*/
template <>
struct DepthwiseConvKernel<4, 1> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 16) {
// Load the filters
float32x4_t k[4];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 4);
k[2] = vld1q_f32(filterData + 8);
k[3] = vld1q_f32(filterData + 12);
const float* r0 =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
const float* r1 = r0 + inputWidth;
const float* r2 = r0 + inputWidth * 2;
const float* r3 = r0 + inputWidth * 3;
float32x4_t input[4][4];
for (int h = 0; h < outputHeight; h++) {
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t tmp;
input[0][0] = vld1q_f32(r0);
tmp = vld1q_f32(r0 + 4);
input[0][1] = vextq_f32(input[0][0], tmp, 1);
input[0][2] = vextq_f32(input[0][0], tmp, 2);
input[0][3] = vextq_f32(input[0][0], tmp, 3);
input[1][0] = vld1q_f32(r1);
tmp = vld1q_f32(r1 + 4);
input[1][1] = vextq_f32(input[1][0], tmp, 1);
input[1][2] = vextq_f32(input[1][0], tmp, 2);
input[1][3] = vextq_f32(input[1][0], tmp, 3);
input[2][0] = vld1q_f32(r2);
tmp = vld1q_f32(r2 + 4);
input[2][1] = vextq_f32(input[2][0], tmp, 1);
input[2][2] = vextq_f32(input[2][0], tmp, 2);
input[2][3] = vextq_f32(input[2][0], tmp, 3);
input[3][0] = vld1q_f32(r3);
tmp = vld1q_f32(r3 + 4);
input[3][1] = vextq_f32(input[3][0], tmp, 1);
input[3][2] = vextq_f32(input[3][0], tmp, 2);
input[3][3] = vextq_f32(input[3][0], tmp, 3);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 4;
r1 += 4;
r2 += 4;
r3 += 4;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
float32x4_t i3 = vld1q_f32(r3);
*outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]);
r0++;
r1++;
r2++;
r3++;
outputData++;
}
r0 += 3;
r1 += 3;
r2 += 3;
r3 += 3;
}
}
}
};
/**
* Each step calculates four elements of the output.
*/
template <>
struct DepthwiseConvKernel<4, 2> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 16) {
// Load the filters
float32x4_t k[4];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 4);
k[2] = vld1q_f32(filterData + 8);
k[3] = vld1q_f32(filterData + 12);
const float* start =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
float32x4_t input[4][4];
for (int h = 0; h < outputHeight; h++) {
const float* r0 = start + 2 * h * inputWidth;
const float* r1 = start + (2 * h + 1) * inputWidth;
const float* r2 = start + (2 * h + 2) * inputWidth;
const float* r3 = start + (2 * h + 3) * inputWidth;
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4x2_t data1;
float32x4x2_t data2;
data1 = vld2q_f32(r0);
data2 = vld2q_f32(r0 + 8);
input[0][0] = data1.val[0];
input[0][1] = data1.val[1];
input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r1);
data2 = vld2q_f32(r1 + 8);
input[1][0] = data1.val[0];
input[1][1] = data1.val[1];
input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r2);
data2 = vld2q_f32(r2 + 8);
input[2][0] = data1.val[0];
input[2][1] = data1.val[1];
input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r3);
data2 = vld2q_f32(r3 + 8);
input[3][0] = data1.val[0];
input[3][1] = data1.val[1];
input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 8;
r1 += 8;
r2 += 8;
r3 += 8;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
float32x4_t i3 = vld1q_f32(r3);
*outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]);
r0 += 2;
r1 += 2;
r2 += 2;
r3 += 2;
outputData++;
}
}
}
}
};
template <DeviceType Device>
class NeonDepthwiseConvFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
// only support strideH() == strideW() and filterHeight == filterWidth.
CHECK_EQ(strideH(), strideW());
CHECK_EQ(filterHeight, filterWidth);
float* inputData = inputs[0].data<float>();
float* filterData = inputs[1].data<float>();
float* outputData = outputs[0].data<float>();
// padding the input
float* inputPadding = inputData;
if (paddingH() > 0 || paddingW() > 0) {
int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) *
(inputWidth + 2 * paddingW());
resizeBuffer<Device>(newSize);
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
Padding<float>::run(inputData,
inputPadding,
batchSize * inputChannels,
inputHeight,
inputWidth,
paddingH(),
paddingW());
// height and width of padding data
inputHeight += 2 * paddingH();
inputWidth += 2 * paddingW();
}
std::function<void(
const float*, const float*, int, int, int, int, int, int, float*)>
DepthWiseConv;
if (filterWidth == 3 && strideW() == 1) {
DepthWiseConv = DepthwiseConvKernel<3, 1>::run;
} else if (filterWidth == 3 && strideW() == 2) {
DepthWiseConv = DepthwiseConvKernel<3, 2>::run;
} else if (filterWidth == 4 && strideW() == 1) {
DepthWiseConv = DepthwiseConvKernel<4, 1>::run;
} else if (filterWidth == 4 && strideW() == 2) {
DepthWiseConv = DepthwiseConvKernel<4, 2>::run;
} else {
LOG(FATAL) << "Not supported";
}
for (size_t i = 0; i < batchSize; i++) {
DepthWiseConv(inputPadding,
filterData,
inputHeight,
inputWidth,
outputChannels,
outputHeight,
outputWidth,
filterMultiplier,
outputData);
inputPadding += inputChannels * inputHeight * inputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}
};
REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction);
#endif
} // namespace neon
} // namespace paddle
paddle/function/neon/neon_util.h 0 → 100644
浏览文件 @ b45d020f
/* 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. */
#pragma once
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
namespace paddle {
namespace neon {
inline float32x4_t vld1q_f32_aligned(const float* p) {
return vld1q_f32(
(const float*)__builtin_assume_aligned(p, sizeof(float32x4_t)));
}
#ifndef __aarch64__
inline float32_t vaddvq_f32(float32x4_t a) {
float32x2_t v = vadd_f32(vget_high_f32(a), vget_low_f32(a));
return vget_lane_f32(vpadd_f32(v, v), 0);
}
inline float32x4_t vmlaq_laneq_f32(float32x4_t a,
float32x4_t b,
float32x4_t v,
const int lane) {
return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane));
}
#endif
} // namespace neon
} // namespace paddle
#endif
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ b45d020f
...@@ -29,6 +29,10 @@ namespace paddle { ...@@ -29,6 +29,10 @@ namespace paddle {
REGISTER_LAYER(exconv, ExpandConvLayer); REGISTER_LAYER(exconv, ExpandConvLayer);
REGISTER_LAYER(exconvt, ExpandConvLayer); REGISTER_LAYER(exconvt, ExpandConvLayer);
inline bool isDepthwiseConv(int channels, int groups) {
return channels == groups;
}
bool ExpandConvLayer::init(const LayerMap &layerMap, bool ExpandConvLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) { const ParameterMap &parameterMap) {
/* Initialize the basic convolutional parent class */ /* Initialize the basic convolutional parent class */
...@@ -47,14 +51,27 @@ bool ExpandConvLayer::init(const LayerMap &layerMap, ...@@ -47,14 +51,27 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]}; std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]}; std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
if (useGpu_ && (size_t)groups_[i] == (size_t)channels_[i] && !isDeconv_) { // Convolution Layer uses the GemmConv function by default.
convType = "DepthwiseConv";
convGradInputType = "DepthwiseConvGradInput";
convGradFilterType = "DepthwiseConvGradFilter";
} else {
convType = "GemmConv"; convType = "GemmConv";
convGradInputType = "GemmConvGradInput"; convGradInputType = "GemmConvGradInput";
convGradFilterType = "GemmConvGradFilter"; convGradFilterType = "GemmConvGradFilter";
// If depth wise convolution and useGpu == true
if (useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) {
convType = "DepthwiseConv";
convGradInputType = "DepthwiseConvGradInput";
convGradFilterType = "DepthwiseConvGradFilter";
}
// If depth wise convolution and useGpu == false and ARM-NEON
if (!useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
if ((filterSize_[i] == filterSizeY_[i]) &&
(filterSize_[i] == 3 || filterSize_[i] == 4) &&
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2)) {
convType = "NeonDepthwiseConv";
}
#endif
} }
if (FLAGS_use_nnpack && !isDeconv_) { if (FLAGS_use_nnpack && !isDeconv_) {
......
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