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PaddleDetection
提交 519ef7f7 编写于 作者: T Tensor Tang
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Merge branch 'mul' into 'develop' 

enable Mul

See merge request paddlepaddle/paddlelitearmbackend!16
上级 02029900 85487210
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Showing with 347 addition and 101 deletion
paddle/fluid/lite/core/cpu_info.cc
浏览文件 @ 519ef7f7
......@@ -54,15 +54,15 @@ void DeviceInfo::InitInternal(DeviceInfo* dev) {
<< ", cluster ID: " << dev->cluster_ids_[dev->core_ids_[i]]
<< ", CPU ARCH: A" << dev->archs_[i];
}
LOG(INFO) << "L1 DataCache size is: ";
VLOG(1) << "L1 DataCache size is: ";
for (int i = 0; i < dev->compute_core_num_; ++i) {
LOG(INFO) << dev->L1_cache_[i] / 1024 << " KB";
VLOG(1) << dev->L1_cache_[i] / 1024 << " KB";
}
LOG(INFO) << "L2 Cache size is: ";
VLOG(1) << "L2 Cache size is: ";
for (int i = 0; i < dev->compute_core_num_; ++i) {
LOG(INFO) << dev->L2_cache_[i] / 1024 << " KB";
VLOG(1) << dev->L2_cache_[i] / 1024 << " KB";
}
LOG(INFO) << "Total memory: " << dev->max_memory_ << "KB";
VLOG(1) << "Total memory: " << dev->max_memory_ << "KB";
dev->max_freq_ = max_freq[0];
for (int j = 1; j < dev->compute_core_num_; ++j) {
......
paddle/fluid/lite/kernels/arm/CMakeLists.txt
浏览文件 @ 519ef7f7
......@@ -6,7 +6,7 @@ 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(mul_compute_arm SRCS mul_compute.cc DEPS ${lite_kernel_deps} eigen3)
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)
cc_library(conv_compute_arm SRCS conv_compute.cc DEPS ${lite_kernel_deps} math_arm)
......@@ -20,6 +20,7 @@ lite_cc_test(test_softmax_compute_arm SRCS softmax_compute_test.cc DEPS softmax_
lite_cc_test(test_conv_compute_arm SRCS conv_compute_test.cc DEPS conv_compute_arm)
lite_cc_test(test_elementwise_add_compute_arm SRCS elementwise_add_compute_test.cc DEPS elementwise_add_compute_arm)
lite_cc_test(test_pool_compute_arm SRCS pool_compute_test.cc DEPS pool_compute_arm)
lite_cc_test(test_mul_compute_arm SRCS mul_compute_test.cc DEPS mul_compute_arm)
lite_cc_test(test_split_compute_arm SRCS split_compute_test.cc DEPS split_compute_arm)
set(arm_kernels
......
paddle/fluid/lite/kernels/arm/conv_compute_test.cc
浏览文件 @ 519ef7f7
......@@ -124,6 +124,20 @@ TEST(conv_arm, init) {
TEST(conv_arm, compute) {
DeviceInfo::Init();
#if 1
for (auto n : {2}) {
for (auto ic : {6}) {
for (auto oc : {6}) {
for (auto ih : {9}) {
for (auto iw : {9}) {
for (auto flag_bias : {false, true}) {
for (auto flag_relu : {false, true}) {
for (auto depthwise : {false, true}) {
for (auto dilation : {1}) {
for (auto stride : {1, 2}) {
for (auto padding : {0, 1, 2}) {
for (auto ks : {1, 3, 5}) {
#else
for (auto n : {1, 2}) {
for (auto ic : {6, 32 /*, 128*/}) {
for (auto oc : {6, 32 /*, 128*/}) {
......@@ -136,6 +150,7 @@ TEST(conv_arm, compute) {
for (auto stride : {1, 2}) {
for (auto padding : {0, 1, 2}) {
for (auto ks : {1, 3, 5}) {
#endif
int group = 1;
if (depthwise) { // depthwise convolution ?
group = oc = ic;
......
paddle/fluid/lite/kernels/arm/fc_compute.cc
浏览文件 @ 519ef7f7
......@@ -22,6 +22,10 @@ namespace lite {
namespace kernels {
namespace arm {
void FcCompute::PrepareForRun() {
// TODO(TJ): transpose weight
}
void FcCompute::Run() {
auto& param = this->Param<operators::FcParam>();
auto x_dims = param.input->dims();
......@@ -48,22 +52,16 @@ void FcCompute::Run() {
&ctx);
lite::arm::math::sgemm_prepack(packed_in, w_data, b_data, o_data, x_h, n,
x_w, false, false, false, &ctx);
if (param.bias) {
CHECK_EQ(param.bias->numel(), n);
lite::arm::math::fill_bias_fc(o_data, b_data, x_h, n);
}
} else {
// use sgemmv
// sgemv((const float*)weights, (const float*)din, (float*)dout,
// false, n, x_w, _param->_flag_bias, (float*)bias, false);
lite::arm::math::sgemv(w_data, i_data, o_data, false, n, x_w,
b_data != nullptr, b_data, false);
}
}
TargetType FcCompute::target() const { return TARGET(kARM); }
PrecisionType FcCompute::precision() const { return PRECISION(kFloat); }
} // namespace arm
} // namespace kernels
} // namespace lite
......
paddle/fluid/lite/kernels/arm/fc_compute.h
浏览文件 @ 519ef7f7
......@@ -25,10 +25,9 @@ class FcCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using param_t = operators::FcParam;
void Run() override;
void PrepareForRun() override;
TargetType target() const override;
PrecisionType precision() const override;
void Run() override;
virtual ~FcCompute() = default;
};
......
paddle/fluid/lite/kernels/arm/mul_compute.cc
浏览文件 @ 519ef7f7
......@@ -12,57 +12,57 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <Eigen/Core>
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/kernels/arm/mul_compute.h"
#include "paddle/fluid/lite/arm/math/funcs.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/core/types.h"
#include "paddle/fluid/lite/core/type_system.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace arm {
template <typename T>
void mul_compute_eigen(const T* x, int x_h, int x_w, const T* y, int y_h,
int y_w, T* out) {
using matrix_t =
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
Eigen::Map<const matrix_t> X(x, x_h, x_w);
Eigen::Map<const matrix_t> Y(y, y_h, y_w);
Eigen::Map<matrix_t> Out(out, x_h, y_w);
Out = X * Y;
void MulCompute::PrepareForRun() {
// TODO(TJ): transpose x or y if necessary
}
class MulCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using param_t = operators::MulParam;
void MulCompute::Run() {
auto& param = Param<param_t>();
void Run() override {
auto& param = Param<operators::MulParam>();
core::dim2 x_shape(
{static_cast<int>(
param.x->dims().Slice(0, param.x_num_col_dims).production()),
static_cast<int>(
param.x->dims()
const auto* x_data = param.x->data<float>();
const auto* y_data = param.y->data<float>();
auto* o_data = param.output->mutable_data<float>();
int m = static_cast<int>(
param.x->dims().Slice(0, param.x_num_col_dims).production());
int x_w =
static_cast<int>(param.x->dims()
.Slice(param.x_num_col_dims, param.x->dims().size())
.production())});
core::dim2 y_shape(
{static_cast<int>(
param.y->dims().Slice(0, param.y_num_col_dims).production()),
static_cast<int>(
param.y->dims()
.production());
int y_h = static_cast<int>(
param.y->dims().Slice(0, param.y_num_col_dims).production());
int n =
static_cast<int>(param.y->dims()
.Slice(param.y_num_col_dims, param.y->dims().size())
.production())});
.production());
mul_compute_eigen(param.x->data<float>(), x_shape.x, x_shape.y, //
param.y->data<float>(), y_shape.x, y_shape.y, //
param.output->mutable_data<float>());
}
CHECK_EQ(x_w, y_h) << "x_w must be equal with y_h";
auto k = x_w;
if (n == 1) {
lite::arm::math::sgemv(x_data, y_data, o_data, false, m, k, false, nullptr,
false);
virtual ~MulCompute() = default;
};
} else {
constexpr bool is_tranposed_y = false;
auto& ctx = this->ctx_->template As<ARMContext>();
float* packed_x = static_cast<float*>(ctx.workspace_data<float>()) +
ctx.l2_cache_size() / sizeof(float);
lite::arm::math::prepackA(packed_x, x_data, k, 0, m, 0, k, false, &ctx);
lite::arm::math::sgemm_prepack(packed_x, y_data, nullptr, o_data, m, n, k,
false, false, is_tranposed_y, &ctx);
}
}
} // namespace arm
} // namespace kernels
......
paddle/fluid/lite/kernels/arm/mul_compute.h 0 → 100644
浏览文件 @ 519ef7f7
// 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
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/core/types.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace arm {
class MulCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using param_t = operators::MulParam;
void PrepareForRun() override;
void Run() override;
virtual ~MulCompute() = default;
};
} // namespace arm
} // namespace kernels
} // namespace lite
} // namespace paddle
paddle/fluid/lite/kernels/arm/mul_compute_test.cc 0 → 100644
浏览文件 @ 519ef7f7
// 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/mul_compute.h"
#include <gtest/gtest.h>
#include <algorithm>
#include <iostream>
#include <memory>
#include <random>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/arm/math/funcs.h"
#include "paddle/fluid/lite/core/op_registry.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace arm {
template <typename T>
void FillData(T* a, const int n, const T lower = static_cast<T>(-2.f),
const T upper = static_cast<T>(2.f)) {
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
for (int i = 0; i < n; ++i) {
a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
}
TEST(mul_arm, retrive_op) {
auto mul =
KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>("mul");
ASSERT_FALSE(mul.empty());
ASSERT_TRUE(mul.front());
}
TEST(mul_arm, init) {
MulCompute mul;
ASSERT_EQ(mul.precision(), PRECISION(kFloat));
ASSERT_EQ(mul.target(), TARGET(kARM));
}
TEST(mul_arm, compare_test) {
using T = float;
for (int m : {1, 2, 3, 4}) {
for (int n : {1, 2, 3, 4}) {
for (int k : {1, 2, 3, 4}) {
VLOG(3) << "m: " << m << ", n: " << n << ", k: " << k;
lite::Tensor x, y, out, ref;
x.Resize({m, k});
y.Resize({k, n});
out.Resize({m, n});
ref.Resize({m, n});
auto* x_data = x.mutable_data<T>();
auto* y_data = y.mutable_data<T>();
auto* out_data = out.mutable_data<T>();
auto* ref_data = ref.mutable_data<T>();
FillData<T>(x_data, x.dims().production());
FillData<T>(y_data, y.dims().production());
FillData<T>(out_data, out.dims().production(), 0, 0);
FillData<T>(ref_data, out.dims().production(), 0, 0);
MulCompute mul;
operators::MulParam param;
param.x = &x;
param.y = &y;
param.output = &out;
DeviceInfo::Init();
std::unique_ptr<KernelContext> ctx(new KernelContext);
ctx->As<ARMContext>();
mul.SetParam(param);
mul.SetContext(std::move(ctx));
mul.PrepareForRun();
mul.Run();
lite::arm::math::mul_compute_eigen(x_data, m, k, y_data, k, n,
ref_data);
for (int i = 0; i < out.dims().production(); i++) {
EXPECT_NEAR(out_data[i], ref_data[i], 1e-3);
}
}
}
}
}
TEST(mul_arm, num_col_dims) {
using T = float;
lite::Tensor x, y, out, ref;
x.Resize({2, 3, 4});
y.Resize({3, 4, 5});
out.Resize({2, 5});
ref.Resize({2, 5});
auto* x_data = x.mutable_data<T>();
auto* y_data = y.mutable_data<T>();
auto* out_data = out.mutable_data<T>();
auto* ref_data = ref.mutable_data<T>();
FillData<T>(x_data, x.dims().production());
FillData<T>(y_data, y.dims().production());
FillData<T>(out_data, out.dims().production());
FillData<T>(ref_data, out.dims().production());
MulCompute mul;
operators::MulParam param;
param.x = &x;
param.y = &y;
param.output = &out;
param.x_num_col_dims = 1;
param.y_num_col_dims = 2;
DeviceInfo::Init();
std::unique_ptr<KernelContext> ctx(new KernelContext);
ctx->As<ARMContext>();
mul.SetParam(param);
mul.SetContext(std::move(ctx));
mul.PrepareForRun();
mul.Run();
lite::arm::math::mul_compute_eigen(x_data, 2, 12, y_data, 12, 5, ref_data);
for (int i = 0; i < out.dims().production(); i++) {
EXPECT_NEAR(out_data[i], ref_data[i], 1e-3);
}
}
} // namespace arm
} // namespace kernels
} // namespace lite
} // namespace paddle
USE_LITE_KERNEL(mul, kARM, kFloat, kNCHW, def);
paddle/fluid/lite/kernels/arm/pool_compute_test.cc
浏览文件 @ 519ef7f7
......@@ -182,7 +182,7 @@ TEST(pool_arm, compute) {
for (auto stride : {2}) {
for (auto pad : {0}) {
for (auto n : {1, 3, 4, 11}) {
for (auto c : {1, 3, 11, 4, 1024}) {
for (auto c : {1, 3, 11 /* ,1024 */}) { // speedup for ci
for (auto h : {3, 1, 11, 4, 1}) {
for (auto w : {1, 3, 4, 12, 1}) {
VLOG(3) << "n:" << n << " c:" << c << " h:" << h << " w:" << w
......
paddle/fluid/lite/kernels/arm/scale_compute_test.cc
浏览文件 @ 519ef7f7
......@@ -54,6 +54,15 @@ TEST(scale_arm, compute) {
lite::Tensor output;
lite::Tensor output_ref;
#if 1 // for ci speedup
for (auto n : {1, 3}) {
for (auto c : {1, 3}) {
for (auto h : {3, 4}) {
for (auto w : {4, 3}) {
for (auto bias_after_scale : {true, false}) {
for (auto s : {-1.0f, 0.13f}) {
for (auto b : {-15.f, 0.11234f}) {
#else
for (auto n : {1, 3, 4, 11}) {
for (auto c : {1, 3, 11, 4}) {
for (auto h : {3, 1, 11, 4}) {
......@@ -61,6 +70,8 @@ TEST(scale_arm, compute) {
for (auto bias_after_scale : {true, false}) {
for (auto s : {-100.25f, -1.0f, 0.13f, 3840.975f}) {
for (auto b : {-3075.495f, -15.f, 0.11234f, 128.15f}) {
#endif
x.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
output.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
output_ref.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
......
paddle/fluid/lite/operators/op_params.h
浏览文件 @ 519ef7f7
......@@ -57,6 +57,7 @@ struct FcParam {
lite::Tensor* output{};
lite::DDim in_mat_dims;
int in_num_col_dims{1};
bool weight_transposed{false};
};
struct ReluParam {
......
paddle/fluid/lite/tools/build.sh
浏览文件 @ 519ef7f7
......@@ -43,10 +43,14 @@ function cmake_arm {
-DARM_TARGET_OS=$1 -DARM_TARGET_ARCH_ABI=$2
}
function build_single {
make $1 -j$(expr $(nproc) - 2)
}
function build {
file=$1
for _test in $(cat $file); do
make $_test -j$(expr $(nproc) - 2)
build_single $_test
done
}
......@@ -63,39 +67,6 @@ function test_lite {
done
}
port_armv8=5554
port_armv7=5556
# Run test on android
function test_lite_android {
local file=$1
local adb_abi=$2
local port=
if [[ ${adb_abi} == "armeabi-v7a" ]]; then
port=${port_armv7}
fi
if [[ ${adb_abi} == "arm64-v8a" ]]; then
port=${port_armv8}
fi
if [[ "${port}x" == "x" ]]; then
echo "Port can not be empty"
exit 1
fi
echo "file: ${file}"
# push all to adb and test
adb_work_dir="/data/local/tmp"
skip_list="test_model_parser_lite"
for _test in $(cat $file); do
[[ $skip_list =~ (^|[[:space:]])$_test($|[[:space:]]) ]] && continue || echo 'skip $_test'
testpath=$(find ./paddle/fluid -name ${_test})
adb -s emulator-${port} push ${testpath} ${adb_work_dir}
adb -s emulator-${port} shell chmod +x "${adb_work_dir}/${_test}"
adb -s emulator-${port} shell "./${adb_work_dir}/${_test}"
done
}
# Build the code and run lite server tests. This is executed in the CI system.
function build_test_server {
mkdir -p ./build
......@@ -108,8 +79,34 @@ function build_test_server {
build $LIBS_FILE
}
# Build the code and run lite server tests. This is executed in the CI system.
# test_arm_android <some_test_name> <adb_port_number>
function test_arm_android {
test_name=$1
port=$2
if [[ "${test_name}x" == "x" ]]; then
echo "test_name can not be empty"
exit 1
fi
if [[ "${port}x" == "x" ]]; then
echo "Port can not be empty"
exit 1
fi
echo "test name: ${test_name}"
adb_work_dir="/data/local/tmp"
skip_list="test_model_parser_lite" # add more with space
[[ $skip_list =~ (^|[[:space:]])$test_name($|[[:space:]]) ]] && continue || echo 'skip $test_name'
testpath=$(find ./paddle/fluid -name ${test_name})
adb -s emulator-${port} push ${testpath} ${adb_work_dir}
adb -s emulator-${port} shell chmod +x "${adb_work_dir}/${test_name}"
adb -s emulator-${port} shell "./${adb_work_dir}/${test_name}"
}
# Build the code and run lite arm tests. This is executed in the CI system.
function build_test_arm {
port_armv8=5554
port_armv7=5556
adb kill-server
adb devices | grep emulator | cut -f1 | while read line; do adb -s $line emu kill; done
# start android arm64-v8a armeabi-v7a emulators first
......@@ -122,6 +119,7 @@ function build_test_arm {
for os in "android" "armlinux" ; do
for abi in "arm64-v8a" "armeabi-v7a" "armeabi-v7a-hf" ; do
# TODO(TJ): enable compile on v7-hf on andorid and all v7 on armlinux
if [[ ${abi} == "armeabi-v7a-hf" ]]; then
echo "armeabi-v7a-hf is not supported on both android and armlinux"
continue
......@@ -138,17 +136,30 @@ function build_test_arm {
cmake_arm ${os} ${abi}
build $TESTS_FILE
# armlinux need in another docker
# TODO(TJ): enable test with armlinux
if [[ ${os} == "android" ]]; then
adb_abi=${abi}
if [[ ${adb_abi} == "armeabi-v7a-hf" ]]; then
adb_abi="armeabi-v7a"
fi
if [[ ${adb_abi} == "armeabi-v7a" ]]; then
# skip v7 tests
# skip all armv7 tests
# TODO(TJ): enable test with armv7
continue
fi
test_lite_android $TESTS_FILE ${adb_abi}
# armlinux need in another docker
local port=
if [[ ${adb_abi} == "armeabi-v7a" ]]; then
port=${port_armv7}
fi
if [[ ${adb_abi} == "arm64-v8a" ]]; then
port=${port_armv8}
fi
echo "test file: ${TESTS_FILE}"
for _test in $(cat $TESTS_FILE); do
test_arm_android $_test $port
done
fi
cd -
done
......@@ -164,12 +175,13 @@ function print_usage {
echo "----------------------------------------"
echo -e "cmake_x86: run cmake with X86 mode"
echo -e "cmake_cuda: run cmake with CUDA mode"
echo -e "cmake_arm: run cmake with ARM mode"
echo -e "--arm_os=<os> --arm_abi=<abi> cmake_arm: run cmake with ARM mode"
echo
echo -e "build: compile the tests"
echo -e "--test_name=<test_name> build_single: compile single test"
echo
echo -e "test_server: run server tests"
echo -e "test_mobile: run mobile tests"
echo -e "--test_name=<test_name> --adb_port_number=<adb_port_number> test_arm_android: run arm test"
echo "----------------------------------------"
echo
}
......@@ -182,11 +194,31 @@ function main {
TESTS_FILE="${i#*=}"
shift
;;
--test_name=*)
TEST_NAME="${i#*=}"
shift
;;
--arm_os=*)
ARM_OS="${i#*=}"
shift
;;
--arm_abi=*)
ARM_ABI="${i#*=}"
shift
;;
--arm_port=*)
ARM_PORT="${i#*=}"
shift
;;
build)
build $TESTS_FILE
build $LIBS_FILE
shift
;;
build_single)
build_single $TEST_NAME
shift
;;
cmake_x86)
cmake_x86
shift
......@@ -196,15 +228,15 @@ function main {
shift
;;
cmake_arm)
cmake_arm $2 $3
cmake_arm $ARM_OS $ARM_ABI
shift
;;
test_server)
test_lite $TESTS_FILE
shift
;;
test_mobile)
test_lite $TESTS_FILE
test_arm_android)
test_arm_android $TEST_NAME $ARM_PORT
shift
;;
build_test_server)
......@@ -224,7 +256,5 @@ function main {
done
}
print_usage
main $@
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