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未验证 提交 36363292 编写于 作者: T tensor-tang 提交者： GitHub
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Merge pull request #12904 from tensor-tang/refine/jit 

optimize cpu vec activations
上级 2b64a19f 7bdaf096
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Showing with 530 addition and 48 deletion
paddle/fluid/operators/attention_lstm_op.cc
浏览文件 @ 36363292
......@@ -232,40 +232,28 @@ use lstm_x_t as input and compute as standard LSTM.
template <typename T>
inline void bias_relu(const int n, const T* x, const T* bias, T* y) {
if (bias) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] + bias[0];
}
math::vec_relu<T>(n, y, y);
math::vec_add_bias<T, platform::jit::avx>(n, *bias, x, y);
math::vec_relu<T, platform::jit::avx>(n, y, y);
} else {
math::vec_relu<T>(n, x, y);
math::vec_relu<T, platform::jit::avx>(n, x, y);
}
}
template <typename DeviceContext, typename T>
inline void vec_softmax(const math::BlasT<DeviceContext, T>& blas, const int n,
const T* x, T* y) {
template <typename T>
inline void vec_softmax(const int n, const T* x, T* y) {
T scalar = x[0];
// max
for (int i = 1; i < n; ++i) {
scalar = scalar < x[i] ? x[i] : scalar;
}
// sub
for (int i = 0; i < n; ++i) {
y[i] = x[i] - scalar;
}
// exp
blas.VEXP(n, y, y);
math::vec_add_bias<T, platform::jit::avx>(n, -scalar, x, y); // sub
math::vec_exp<T>(n, y, y); // exp
// sum
scalar = T(0);
for (int i = 0; i < n; ++i) {
scalar += y[i];
}
// scale
blas.SCAL(n, static_cast<T>(1) / scalar, y);
math::vec_scal<T>(n, static_cast<T>(1) / scalar, y); // scale
}
template <typename T>
......@@ -311,11 +299,21 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(c0->dims()[0], N, "C0 dims should be %d x %d.", N, D);
fc_out->Resize({max_seq_len, 1});
math::VecActivations<T> act_functor;
std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
act_gate = act_functor(ctx.Attr<std::string>("gate_activation"));
act_cell = act_functor(ctx.Attr<std::string>("cell_activation"));
act_cand = act_functor(ctx.Attr<std::string>("candidate_activation"));
auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<T, platform::jit::avx> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
} else {
math::VecActivations<T, platform::jit::isa_any> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
}
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : NULL;
......@@ -363,7 +361,7 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
fc_out_data);
}
// 1d. softmax
vec_softmax<DeviceContext, T>(blas, seq_len, fc_out_data, fc_out_data);
vec_softmax<T>(seq_len, fc_out_data, fc_out_data);
// mul x(seq_len*M) and sum pool
math::FCCompute<DeviceContext, T>(blas, 1, M, seq_len, fc_out_data,
cur_x_data, lstm_x_data);
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 36363292
......@@ -65,3 +65,4 @@ if(WITH_GPU)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
paddle/fluid/operators/math/cpu_vec.h
浏览文件 @ 36363292
......@@ -13,8 +13,16 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <cmath>
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
namespace paddle {
namespace operators {
......@@ -22,16 +30,161 @@ namespace math {
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
#define AVX_FLOAT_BLOCK 8
#define AVX_DOUBLE_BLOCK 4
#define AVX2_FLOAT_BLOCK 8
#define AVX2_DOUBLE_BLOCK 4
#define AVX512_FLOAT_BLOCK 16
#define AVX512_DOUBLE_BLOCK 8
template <typename T>
inline T sigmoid(T x) {
return 1. / (1. + exp(-x));
inline void vec_exp(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = std::exp(x[i]);
}
}
template <typename T>
inline T tanh(T x) {
return 2. * sigmoid(2. * x) - 1.;
inline void vec_scal(const int n, const T a, T* x) {
for (int i = 0; i < n; ++i) {
x[i] = a * x[i];
}
}
#ifdef PADDLE_WITH_MKLML
template <>
inline void vec_exp<float>(const int n, const float* x, float* y) {
platform::dynload::vsExp(n, x, y);
}
template <>
inline void vec_exp<double>(const int n, const double* x, double* y) {
platform::dynload::vdExp(n, x, y);
}
template <>
inline void vec_scal<float>(const int n, const float a, float* x) {
platform::dynload::cblas_sscal(n, a, x, 1);
}
template <>
inline void vec_scal<double>(const int n, const double a, double* x) {
platform::dynload::cblas_dscal(n, a, x, 1);
}
#endif
// MKL scal only support inplace, choose this if src and dst are not equal
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_scal(const int n, const T a, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = a * x[i];
}
}
template <>
inline void vec_scal<float, platform::jit::avx>(const int n, const float a,
const float* x, float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_scal<float, platform::jit::isa_any>(n, a, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 scalar = _mm256_set1_ps(a);
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step if src and dst are inplace
for (i = n - rest; i < n; ++i) {
y[i] = a * x[i];
}
#else
vec_scal<float, platform::jit::isa_any>(n, a, x, y);
#endif
}
template <>
inline void vec_scal<float, platform::jit::avx2>(const int n, const float a,
const float* x, float* y) {
vec_scal<float, platform::jit::avx>(n, a, x, y);
}
template <>
inline void vec_scal<float, platform::jit::avx512_common>(const int n,
const float a,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_scal<float, platform::jit::avx2>(n, a, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_add_bias(const int n, const T a, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] + a;
}
}
template <>
inline void vec_add_bias<float, platform::jit::avx>(const int n, const float a,
const float* x, float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 bias = _mm256_set1_ps(a);
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_add_ps(tmp, bias); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step if src and dst are inplace
for (i = n - rest; i < n; ++i) {
y[i] = x[i] + a;
}
#else
vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
#endif
}
template <>
inline void vec_add_bias<float, platform::jit::avx2>(const int n, const float a,
const float* x, float* y) {
vec_add_bias<float, platform::jit::avx>(n, a, x, y);
}
template <>
inline void vec_add_bias<float, platform::jit::avx512_common>(const int n,
const float a,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_add_bias<float, platform::jit::avx2>(n, a, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
......@@ -45,18 +198,97 @@ inline void vec_sigmoid(const int n, const T* x, T* y) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
for (int i = 0; i < n; ++i) {
T tmp = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
y[i] = 1.0 / (1.0 + std::exp(-tmp));
y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
y[i] = static_cast<T>(0) - y[i];
}
vec_exp<T>(n, y, y);
for (int i = 0; i < n; ++i) {
y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
}
}
template <>
inline void vec_sigmoid<float, platform::jit::avx>(const int n, const float* x,
float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);
__m256 zeros = _mm256_setzero_ps();
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, min); \
tmp = _mm256_min_ps(tmp, max); \
tmp = _mm256_sub_ps(zeros, tmp); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest != 0) {
// can not continue move step since the src and dst address could be equal
const float xmin = SIGMOID_THRESHOLD_MIN;
const float xmax = SIGMOID_THRESHOLD_MAX;
for (i = n - rest; i < n; ++i) {
y[i] = 0.f - ((x[i] < xmin) ? xmin : ((x[i] > xmax) ? xmax : x[i]));
}
}
vec_exp<float>(n, y, y);
__m256 ones = _mm256_set1_ps(1.0f);
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(y + i); \
tmp = _mm256_add_ps(ones, tmp); \
tmp = _mm256_div_ps(ones, tmp); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step
for (i = n - rest; i < n; ++i) {
y[i] = 1.f / (1.f + y[i]);
}
#else
vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
#endif
}
template <>
inline void vec_sigmoid<float, platform::jit::avx2>(const int n, const float* x,
float* y) {
vec_sigmoid<float, platform::jit::avx>(n, x, y);
}
template <>
inline void vec_sigmoid<float, platform::jit::avx512_common>(const int n,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_sigmoid<float, platform::jit::avx2>(n, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_tanh(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = tanh<T>(x[i]);
}
vec_scal<T, isa>(n, static_cast<T>(2), x, y);
vec_sigmoid<T, isa>(n, y, y);
vec_scal<T>(n, static_cast<T>(2), y);
vec_add_bias<T, isa>(n, static_cast<T>(-1), y, y);
}
// TODO(TJ): make relu clip
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_relu(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
......@@ -65,23 +297,55 @@ inline void vec_relu(const int n, const T* x, T* y) {
}
template <>
inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
float* y) {
// TODO(TJ): complete me
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block * 4) {
vec_relu<float, platform::jit::isa_any>(n, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 zeros = _mm256_setzero_ps();
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
if (rest == 0) {
return;
}
i = n - block;
MOVE_ONE_STEP;
#undef MOVE_ONE_STEP
#else
vec_relu<float, platform::jit::isa_any>(n, x, y);
#endif
}
template <>
inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
float* y) {
// TODO(TJ): complete me
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
vec_relu<float, platform::jit::avx>(n, x, y);
}
template <>
inline void vec_relu<float, platform::jit::avx512_common>(const int n,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_relu<float, platform::jit::avx2>(n, x, y);
}
// TODO(TJ): optimize double of sigmoid, tanh and relu if necessary
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
class VecActivations {
public:
......@@ -96,7 +360,7 @@ class VecActivations {
} else if (type == "identity" || type == "") {
return vec_identity<T, isa>;
}
PADDLE_THROW("Not support type %s.", type);
LOG(FATAL) << "Not support type: " << type;
}
};
......
paddle/fluid/operators/math/cpu_vec_test.cc 0 → 100644
浏览文件 @ 36363292
/* Copyright (c) 2018 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 <sys/time.h>
#include <cmath>
#include <cstring>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
inline double GetCurrentUS() {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+6 * time.tv_sec + time.tv_usec;
}
constexpr int repeat = 1000;
template <typename T>
inline T _sigmoid(T x) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (x < min) ? min : ((x > max) ? max : x);
return static_cast<T>(1) / (static_cast<T>(1) + std::exp(-tmp));
}
template <typename T>
inline T _tanh(T x) {
return static_cast<T>(2) * _sigmoid<T>(static_cast<T>(2) * x) -
static_cast<T>(1);
}
template <typename T>
void ref_sigmoid(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = _sigmoid(x[i]);
}
}
template <typename T>
void ref_tanh(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = _tanh(x[i]);
}
}
template <typename T>
void ref_relu(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
}
template <typename T>
void RandomVec(const int n, T* a) {
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
const T lower = static_cast<T>(-20.f);
const T upper = static_cast<T>(20.f);
for (int i = 0; i < n; ++i) {
a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
}
template <typename T>
void TestAndBench(const int n, std::function<void(const int, const T*, T*)> tgt,
std::function<void(const int, const T*, T*)> ref) {
std::vector<T> x(n);
std::vector<T> ytgt(n), yref(n);
RandomVec<T>(n, x.data());
const T* x_data = x.data();
T* ytgt_data = ytgt.data();
T* yref_data = yref.data();
auto st = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
tgt(n, x_data, ytgt_data);
}
auto mt = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
ref(n, x_data, yref_data);
}
auto et = GetCurrentUS();
VLOG(3) << "Vec size " << n << ": refer takes: " << (et - mt) / repeat
<< " us, tgt takes: " << (mt - st) / repeat;
for (int i = 0; i < n; ++i) {
EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
}
}
TEST(CpuVecTest, sigmoid) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx512_common>,
ref_sigmoid<float>);
}
TestAndBench<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}
TEST(CpuVecTest, tanh) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_tanh<float>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx512_common>,
ref_tanh<float>);
}
TestAndBench<double>(30, vec_tanh<double>, ref_tanh<double>);
}
TEST(CpuVecTest, relu) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_relu<float>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx512_common>,
ref_relu<float>);
}
TestAndBench<double>(30, vec_relu<double>, ref_relu<double>);
}
template <typename T>
void TestInplace(const int n, std::function<void(const int, const T*, T*)> tgt,
std::function<void(const int, const T*, T*)> ref) {
std::vector<T> x(n);
std::vector<T> ytgt(n), yref(n);
RandomVec<T>(n, x.data());
const T* x_data = x.data();
T* yref_data = yref.data();
T* ytgt_data = ytgt.data();
std::memcpy(yref_data, x_data, sizeof(T) * n);
std::memcpy(ytgt_data, x_data, sizeof(T) * n);
ref(n, yref_data, yref_data);
tgt(n, ytgt_data, ytgt_data);
for (int i = 0; i < n; ++i) {
EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
}
}
TEST(CpuVecTest, inplace_sigmoid) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx512_common>,
ref_sigmoid<float>);
}
TestInplace<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}
TEST(CpuVecTest, inplace_tanh) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_tanh<float>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx512_common>,
ref_tanh<float>);
}
TestInplace<double>(30, vec_tanh<double>, ref_tanh<double>);
}
TEST(CpuVecTest, inplace_relu) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_relu<float>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx512_common>,
ref_relu<float>);
}
TestInplace<double>(30, vec_relu<double>, ref_relu<double>);
}
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 36363292
......@@ -50,7 +50,7 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library(device_context SRCS device_context.cc init.cc DEPS malloc
place eigen3 stringpiece cpu_helper framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
place eigen3 stringpiece cpu_helper cpu_info framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_info)
cc_test(init_test SRCS init_test.cc DEPS device_context)
......
paddle/fluid/platform/cpu_info.h
浏览文件 @ 36363292
......@@ -51,7 +51,7 @@ typedef enum {
} cpu_isa_t; // Instruction set architecture
// May I use some instruction
inline bool MayIUse(const cpu_isa_t cpu_isa);
bool MayIUse(const cpu_isa_t cpu_isa);
} // namespace jit
......
paddle/fluid/platform/init.cc
浏览文件 @ 36363292
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
......@@ -120,6 +121,22 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
if (platform::jit::MayIUse(platform::jit::avx512_common)) {
#ifndef __AVX512F__
LOG(WARNING) << "AVX512F is available, Please re-compile on local machine";
#endif
}
if (platform::jit::MayIUse(platform::jit::avx2)) {
#ifndef __AVX2__
LOG(WARNING) << "AVX2 is available, Please re-compile on local machine";
#endif
}
if (platform::jit::MayIUse(platform::jit::avx)) {
#ifndef __AVX__
LOG(WARNING) << "AVX is available, Please re-compile on local machine";
#endif
}
}
void InitGLOG(const std::string &prog_name) {
......
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