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提交 1c8a0c4b 编写于 作者: D dangqingqing
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Refine activation function pointer for LSTM operator.

上级 2c5d4c6d
隐藏空白更改
内联 并排
Showing with 279 addition and 590 deletion
paddle/framework/CMakeLists.txt
浏览文件 @ 1c8a0c4b
......@@ -20,7 +20,8 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc)
cc_test(program_desc_test SRCS program_desc_test.cc DEPS proto_desc
device_context)
cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
......
paddle/operators/math/detail/CMakeLists.txt
浏览文件 @ 1c8a0c4b
if(WITH_AVX)
cc_library(activation_functions SRCS hl_cpu_functions.cc hl_avx_functions.cc)
else()
cc_library(activation_functions SRCS hl_cpu_functions.cc)
cc_library(activation_functions SRCS avx_functions.cc)
endif()
paddle/operators/math/detail/activation_functions.h 0 → 100644
浏览文件 @ 1c8a0c4b
/* 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
#include <math.h>
#include "paddle/platform/hostdevice.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace paddle {
namespace operators {
namespace math {
namespace detail {
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
namespace forward {
template <typename T>
DEVICE T linear(const T a) {
return a;
}
template <typename T>
DEVICE T relu(const T a) {
return a > static_cast<T>(0.0) ? a : static_cast<T>(0.0);
}
template <typename T>
DEVICE T sigmoid(const T a) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<T>(1.0) / (static_cast<T>(1.0) + exp(-tmp));
}
template <typename T>
DEVICE T tanh(const T a) {
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
} // namespace forward
namespace backward {
template <typename T>
DEVICE T linear(const T a, const T b) {
return a;
}
template <typename T>
DEVICE T relu(const T a, const T b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
template <typename T>
DEVICE T sigmoid(const T a, const T b) {
return a * b * (1.0 - b);
}
template <typename T>
DEVICE T tanh(const T a, const T b) {
return a * (1.0 - b * b);
}
} // namespace backward
template <typename T>
struct Active {
typedef T (*Act)(T);
typedef T (*ActGrad)(T, T);
};
static DEVICE Active<float>::Act kActFloat[] = {
&forward::sigmoid<float>, &forward::relu<float>, &forward::tanh<float>,
&forward::linear<float>};
static DEVICE Active<float>::ActGrad kActGradFloat[] = {
&backward::sigmoid<float>, &backward::relu<float>, &backward::tanh<float>,
&backward::linear<float>};
static DEVICE Active<double>::Act kActDouble[] = {
&forward::sigmoid<double>, &forward::relu<double>, &forward::tanh<double>,
&forward::linear<double>};
static DEVICE Active<double>::ActGrad kActGradDouble[] = {
&backward::sigmoid<double>, &backward::relu<double>,
&backward::tanh<double>, &backward::linear<double>};
namespace forward {
inline DEVICE float activation(float a, int index) {
return kActFloat[index](a);
}
inline DEVICE double activation(double a, int index) {
return kActDouble[index](a);
}
} // namespace forward
namespace backward {
inline DEVICE float activation(float a, float b, int index) {
return kActGradFloat[index](a, b);
}
inline DEVICE double activation(double a, double b, int index) {
return kActGradDouble[index](a, b);
}
} // namespace backward
#ifdef __AVX__
namespace forward {
namespace avx {
__m256 relu(const __m256 a);
__m256 sigmoid(const __m256 a);
__m256 tanh(const __m256 a);
__m256 linear(const __m256 a);
} // namespace avx
} // namespace forward
namespace backward {
namespace avx {
__m256 relu(const __m256 a, const __m256 b);
__m256 sigmoid(const __m256 a, const __m256 b);
__m256 tanh(const __m256 a, const __m256 b);
__m256 linear(const __m256 a, const __m256 b);
} // namespace avx
} // namespace backward
static Active<__m256>::Act kActAvx[] = {
&forward::avx::sigmoid, &forward::avx::relu, &forward::avx::tanh,
&forward::avx::linear};
static Active<__m256>::ActGrad kActGradAvx[] = {
&backward::avx::sigmoid, &backward::avx::relu, &backward::avx::tanh,
&backward::avx::linear};
namespace forward {
inline __m256 activation(__m256 a, int index) { return kActAvx[index](a); }
} // namespace forward
namespace backward {
inline __m256 activation(__m256 a, __m256 b, int index) {
return kActGradAvx[index](a, b);
}
} // namespace backward
#endif
} // namespace detail
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/detail/hl_avx_functions.cc → paddle/operators/math/detail/avx_functions.cc
浏览文件 @ 1c8a0c4b
......@@ -13,14 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <immintrin.h>
#include "hl_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
// TODO(qingqing) refine this dependence
#include "paddle/cuda/src/avx_mathfun.h"
namespace hppl {
namespace paddle {
namespace operators {
namespace math {
namespace detail {
__m256 exp(__m256 a) { return exp256_ps(a); }
namespace forward {
namespace avx {
__m256 relu(const __m256 a) {
__m256 tmp = _mm256_set1_ps(0.0f);
return _mm256_max_ps(a, tmp);
......@@ -50,6 +55,11 @@ __m256 tanh(const __m256 a) {
__m256 linear(const __m256 a) { return a; }
} // namespace avx
} // namespace forward
namespace backward {
namespace avx {
__m256 relu(const __m256 a, const __m256 b) {
return _mm256_mul_ps(
a, _mm256_and_ps(_mm256_cmp_ps(b, _mm256_set1_ps(0.0f), _CMP_GT_OS),
......@@ -67,4 +77,10 @@ __m256 tanh(const __m256 a, const __m256 b) {
}
__m256 linear(const __m256 a, const __m256 b) { return a; }
} // namespace hppl
} // namespace avx
} // namespace backward
} // namespace detail
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/detail/hl_activation_functions.h 已删除 100644 → 0
浏览文件 @ 2c5d4c6d
/* 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_ACTIVATION_FUNCTIONS_H_
#define HL_ACTIVATION_FUNCTIONS_H_
#include "hl_functions.h"
#include "paddle/operators/math/lstm_compute.h"
/**
* Active functions: sigmoid, relu, tanh and linear.
*/
#define FLOAT_ACTIVE_FUNCTION \
{ \
hppl::typef::sigmoid, hppl::typef::relu, hppl::typef::tanh, \
hppl::typef::linear \
}
#define DOUBLE_ACTIVE_FUNCTION \
{ \
hppl::typed::sigmoid, hppl::typed::relu, hppl::typed::tanh, \
hppl::typed::linear \
}
#define AVX_ACTIVE_FUNCTION \
{ hppl::sigmoid, hppl::relu, hppl::tanh, hppl::linear }
namespace hppl {
using activation_mode_t = paddle::operators::math::activation_mode_t;
/**
* Hppl supports sigmoid, relu, tanh, linear active functions
* for neural networks' forward and backward activation.
*/
template <class T>
class Active {
public:
typedef T (*forward)(T);
typedef T (*backward)(T, T);
};
template <typename T>
struct ForwardActType;
template <>
struct ForwardActType<float> {
using type = Active<float>::forward;
};
template <>
struct ForwardActType<double> {
using type = Active<double>::forward;
};
template <typename T>
struct BackwardActType;
template <>
struct BackwardActType<float> {
using type = Active<float>::backward;
};
template <>
struct BackwardActType<double> {
using type = Active<double>::backward;
};
#ifdef __NVCC__
namespace gpu {
static __device__ Active<float>::forward forward[] = FLOAT_ACTIVE_FUNCTION;
static __device__ Active<float>::backward backward[] = FLOAT_ACTIVE_FUNCTION;
static __device__ Active<double>::forward forward_d[] = DOUBLE_ACTIVE_FUNCTION;
static __device__ Active<double>::backward backward_d[] =
DOUBLE_ACTIVE_FUNCTION;
template <typename T>
struct ForwardAct {
__device__ typename ForwardActType<T>::type operator()(
activation_mode_t type);
};
template <>
struct ForwardAct<float> {
__device__ ForwardActType<float>::type operator()(activation_mode_t type) {
return forward[type];
}
};
template <>
struct ForwardAct<double> {
__device__ ForwardActType<double>::type operator()(activation_mode_t type) {
return forward_d[type];
}
};
template <typename T>
struct BackwardAct {
__device__ typename BackwardActType<T>::type operator()(
activation_mode_t type);
};
template <>
struct BackwardAct<float> {
__device__ BackwardActType<float>::type operator()(activation_mode_t type) {
return backward[type];
}
};
template <>
struct BackwardAct<double> {
__device__ BackwardActType<double>::type operator()(activation_mode_t type) {
return backward_d[type];
}
};
} // namespace gpu
#else
namespace cpu {
static Active<float>::forward forward[] = FLOAT_ACTIVE_FUNCTION;
static Active<float>::backward backward[] = FLOAT_ACTIVE_FUNCTION;
static Active<double>::forward forward_d[] = DOUBLE_ACTIVE_FUNCTION;
static Active<double>::backward backward_d[] = DOUBLE_ACTIVE_FUNCTION;
template <typename T>
struct ForwardAct {
typename ForwardActType<T>::type operator()(activation_mode_t type);
};
template <>
struct ForwardAct<float> {
ForwardActType<float>::type operator()(activation_mode_t type) {
return forward[type];
}
};
template <>
struct ForwardAct<double> {
ForwardActType<double>::type operator()(activation_mode_t type) {
return forward_d[type];
}
};
template <typename T>
struct BackwardAct {
typename BackwardActType<T>::type operator()(activation_mode_t type);
};
template <>
struct BackwardAct<float> {
BackwardActType<float>::type operator()(activation_mode_t type) {
return backward[type];
}
};
template <>
struct BackwardAct<double> {
BackwardActType<double>::type operator()(activation_mode_t type) {
return backward_d[type];
}
};
} // namespace cpu
#ifdef __AVX__
namespace avx {
static Active<__m256>::forward forward[] = AVX_ACTIVE_FUNCTION;
static Active<__m256>::backward backward[] = AVX_ACTIVE_FUNCTION;
} // namespace avx
#endif
#endif
} // namespace hppl
#endif // HL_ACTIVATION_FUNCTIONS_H_
paddle/operators/math/detail/hl_avx_functions.h 已删除 100644 → 0
浏览文件 @ 2c5d4c6d
/* 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_AVX_FUNCTIONS_H_
#define HL_AVX_FUNCTIONS_H_
#include <immintrin.h>
namespace hppl {
__m256 relu(const __m256 a);
__m256 sigmoid(const __m256 a);
__m256 tanh(const __m256 a);
__m256 linear(const __m256 a);
__m256 relu(const __m256 a, const __m256 b);
__m256 sigmoid(const __m256 a, const __m256 b);
__m256 tanh(const __m256 a, const __m256 b);
__m256 linear(const __m256 a, const __m256 b);
} // namespace hppl
#endif // HL_AVX_FUNCTIONS_H_
paddle/operators/math/detail/hl_cpu_functions.cc 已删除 100644 → 0
浏览文件 @ 2c5d4c6d
/* 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 <math.h>
#include "hl_functions.h"
namespace hppl {
namespace typef {
float relu(const float a) {
return a > static_cast<float>(0.0) ? a : static_cast<float>(0.0);
}
float sigmoid(const float a) {
const float min = SIGMOID_THRESHOLD_MIN;
const float max = SIGMOID_THRESHOLD_MAX;
float tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<float>(1.0) / (static_cast<float>(1.0) + exp(-tmp));
}
float tanh(const float a) {
float tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
float linear(const float a) { return a; }
float relu(const float a, const float b) { return a * (b > 0.0 ? 1.0 : 0.0); }
float sigmoid(const float a, const float b) {
return a * b * (static_cast<float>(1) - b);
}
float tanh(const float a, const float b) {
return a * (static_cast<float>(1) - b * b);
}
float linear(const float a, const float b) { return a; }
} // namespace typef
namespace typed {
double relu(const double a) {
return a > static_cast<double>(0.0) ? a : static_cast<double>(0.0);
}
double sigmoid(const double a) {
const double min = SIGMOID_THRESHOLD_MIN;
const double max = SIGMOID_THRESHOLD_MAX;
double tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<double>(1.0) / (static_cast<double>(1.0) + exp(-tmp));
}
double tanh(const double a) {
double tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
double linear(const double a) { return a; }
double relu(const double a, const double b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
double sigmoid(const double a, const double b) {
return a * b * (static_cast<double>(1) - b);
}
double tanh(const double a, const double b) {
return a * (static_cast<double>(1) - b * b);
}
double linear(const double a, const double b) { return a; }
} // namespace typed
} // namespace hppl
paddle/operators/math/detail/hl_functions.h 已删除 100644 → 0
浏览文件 @ 2c5d4c6d
/* 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_FUNCTIONS_H_
#define HL_FUNCTIONS_H_
/**
* sigmoid threshold maximum
*/
#define SIGMOID_THRESHOLD_MIN -40.0
/**
* sigmoid threshold minimum
*/
#define SIGMOID_THRESHOLD_MAX 13.0
/**
* The maximum input value for exp, used to avoid overflow problem.
* currently only used for tanh function.
*/
#define EXP_MAX_INPUT 40.0
#ifndef __NVCC__
namespace hppl {
namespace typef {
float relu(const float a);
float sigmoid(const float a);
float tanh(const float a);
float linear(const float a);
float relu(const float a, const float b);
float sigmoid(const float a, const float b);
float tanh(const float a, const float b);
float linear(const float a, const float b);
} // namespace typef
namespace typed {
double relu(const double a);
double sigmoid(const double a);
double tanh(const double a);
double linear(const double a);
double relu(const double a, const double b);
double sigmoid(const double a, const double b);
double tanh(const double a, const double b);
double linear(const double a, const double b);
} // namespace typed
} // namespace hppl
#ifdef __AVX__
#include "hl_avx_functions.h"
#endif
#else
#include "hl_gpu_functions.h"
#endif
#endif // HL_FUNCTIONS_H_
paddle/operators/math/detail/hl_gpu_functions.h 已删除 100644 → 0
浏览文件 @ 2c5d4c6d
/* 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_GPU_FUNCTIONS_CUH_
#define HL_GPU_FUNCTIONS_CUH_
#include "hl_base.h"
namespace hppl {
namespace typef {
__device__ static float relu(const float a) { return a > 0.0f ? a : 0.0f; }
__device__ static float sigmoid(const float a) {
const float min = SIGMOID_THRESHOLD_MIN;
const float max = SIGMOID_THRESHOLD_MAX;
float tmp = (a < min) ? min : ((a > max) ? max : a);
return __fdividef(1.0f, 1.0f + __expf(-tmp));
}
__device__ static float tanh(const float a) {
float tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return __fdividef(2.0f, (1.0f + __expf(-2.0f * tmp))) - 1.0f;
}
__device__ static float linear(const float a) { return a; }
__device__ static float relu(const float a, const float b) {
return a * (b > 0.0f ? 1.0f : 0.0f);
}
__device__ static float sigmoid(const float a, const float b) {
return a * b * (1.0f - b);
}
__device__ static float tanh(const float a, const float b) {
return a * (1.0f - b * b);
}
__device__ static float linear(const float a, const float b) { return a; }
} // namespace typef
namespace typed {
__device__ static double relu(const double a) { return a > 0.0 ? a : 0.0; }
__device__ static double sigmoid(const double a) {
const double min = SIGMOID_THRESHOLD_MIN;
const double max = SIGMOID_THRESHOLD_MAX;
double tmp = (a < min) ? min : ((a > max) ? max : a);
return 1.0 / (1.0 + exp(-tmp));
}
__device__ static double tanh(const double a) {
double tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(-2.0 * a))) - 1.0;
}
__device__ static double linear(const double a) { return a; }
__device__ static double relu(const double a, const double b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
__device__ static double sigmoid(const double a, const double b) {
return a * b * (1 - b);
}
__device__ static double tanh(const double a, const double b) {
return a * (1.0 - b * b);
}
__device__ static double linear(const double a, const double b) { return a; }
} // namespace typef
} // namespace hppl
#endif // HL_GPU_FUNCTIONS_CUH_
paddle/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ 1c8a0c4b
......@@ -14,7 +14,7 @@ limitations under the License. */
#pragma once
#include <type_traits>
#include "paddle/operators/math/detail/hl_activation_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
#include "paddle/operators/math/lstm_compute.h"
namespace paddle {
......@@ -26,7 +26,10 @@ namespace detail {
template <class T, class Op>
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frameSize) {
int frameSize,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
T rValueIn;
T rValueIg;
T rValueFg;
......@@ -58,7 +61,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
}
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO);
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
valueIn[i] = rValueIn;
valueIg[i] = rValueIg;
......@@ -72,7 +75,10 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize) {
LstmMetaGrad<T> grad, int frameSize,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
T rValueIn;
T rValueIg;
T rValueFg;
......@@ -122,7 +128,7 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg,
rGradOg, rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv,
rOutputGrad, rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad,
rCheckOGrad);
rCheckOGrad, active_node, active_gate, active_state);
gradIn[i] = rGradIn;
gradIg[i] = rGradIg;
......@@ -176,8 +182,7 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
}
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO, hppl::avx::forward[active_node],
hppl::avx::forward[active_gate], hppl::avx::forward[active_state]);
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
valueIn[i] = rValueIn;
valueIg[i] = rValueIg;
......@@ -246,8 +251,7 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg,
rGradOg, rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv,
rOutputGrad, rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad,
rCheckOGrad, hppl::avx::backward[active_node],
hppl::avx::backward[active_gate], hppl::avx::backward[active_state]);
rCheckOGrad, active_node, active_gate, active_state);
gradIn[i] = rGradIn;
gradIg[i] = rGradIg;
......@@ -274,7 +278,8 @@ void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frameSize,
avx_lstm_forward_one_sequence<T>(op, value, frameSize, active_node,
active_gate, active_state);
} else {
naive_lstm_forward_one_sequence<T>(op, value, frameSize);
naive_lstm_forward_one_sequence<T>(op, value, frameSize, active_node,
active_gate, active_state);
}
}
......@@ -287,7 +292,8 @@ void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
avx_lstm_backward_one_sequence<T>(op, value, grad, frameSize, active_node,
active_gate, active_state);
} else {
naive_lstm_backward_one_sequence<T>(op, value, grad, frameSize);
naive_lstm_backward_one_sequence<T>(op, value, grad, frameSize, active_node,
active_gate, active_state);
}
}
......
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ 1c8a0c4b
......@@ -13,13 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <type_traits>
#include "paddle/operators/math/detail/hl_activation_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
#include "paddle/operators/math/lstm_compute.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/device_context.h"
#include <glog/logging.h>
#include <type_traits>
namespace paddle {
namespace operators {
......@@ -32,7 +31,9 @@ namespace detail {
*/
template <class T, class Op, bool isBatch>
__global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
int batchSize) {
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -69,7 +70,7 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
}
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO);
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
value.gateValue[frameIdx] = rValueIn;
value.gateValue[frameIdx + frameSize] = rValueIg;
......@@ -88,7 +89,9 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
template <class T, class Op, bool isBatch>
__global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize,
int batchSize) {
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -141,7 +144,8 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg, rGradOg,
rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv, rOutputGrad,
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad);
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad,
active_node, active_gate, active_state);
grad.gateGrad[frameIdx] = rGradIn;
grad.gateGrad[frameIdx + frameSize] = rGradIg;
......@@ -197,11 +201,13 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
if (batchSize == 1) {
KeLstmForward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize);
op, value, frameSize, batchSize, active_node, active_gate,
active_state);
} else {
KeLstmForward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize);
op, value, frameSize, batchSize, active_node, active_gate,
active_state);
}
}
......@@ -230,11 +236,13 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
if (batchSize == 1) {
KeLstmBackward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize);
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
} else {
KeLstmBackward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize);
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
}
}
......
paddle/operators/math/detail/lstm_kernel.h
浏览文件 @ 1c8a0c4b
......@@ -12,7 +12,7 @@ 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/operators/math/detail/hl_activation_functions.h"
#include "paddle/operators/math/detail/activation_functions.h"
#include "paddle/platform/hostdevice.h"
#include <type_traits>
......@@ -24,45 +24,22 @@ namespace detail {
namespace forward {
template <typename T>
DEVICE inline T sigmoid(const T a) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<T>(1.0) / (static_cast<T>(1.0) + exp(-tmp));
}
template <typename T>
DEVICE inline T tanh(const T a) {
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
template <class T>
class lstm {
public:
HOSTDEVICE void operator()(T &valueIn, T &valueIg, T &valueFg, T &valueOg,
T &prevState, T &state, T &stateAtv, T &output,
T &checkI, T &checkF, T &checkO) {
#if 0
// TODO(qingqing) support to activation speficed by users
valueIn = actInput(valueIn);
valueIg = actGate(valueIg + prevState * checkI);
valueFg = actGate(valueFg + prevState * checkF);
state = valueIn * valueIg + prevState * valueFg;
valueOg = actGate(valueOg + state * checkO);
stateAtv = actState(state);
output = valueOg * stateAtv;
#else
valueIn = tanh<T>(valueIn);
valueIg = sigmoid<T>(valueIg + prevState * checkI);
valueFg = sigmoid<T>(valueFg + prevState * checkF);
T &checkI, T &checkF, T &checkO,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
valueIn = activation(valueIn, active_node);
valueIg = activation(valueIg + prevState * checkI, active_gate);
valueFg = activation(valueFg + prevState * checkF, active_gate);
state = valueIn * valueIg + prevState * valueFg;
valueOg = sigmoid<T>(valueOg + state * checkO);
stateAtv = tanh<T>(state);
valueOg = activation(valueOg + state * checkO, active_gate);
stateAtv = activation(state, active_state);
output = valueOg * stateAtv;
#endif
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -75,16 +52,19 @@ class lstm {
__m256 &valueOg, __m256 &prevState, __m256 &state,
__m256 &stateAtv, __m256 &output, __m256 &checkI,
__m256 &checkF, __m256 &checkO,
hppl::Active<__m256>::forward actInput,
hppl::Active<__m256>::forward actGate,
hppl::Active<__m256>::forward actState) {
valueIn = actInput(valueIn);
valueIg = actGate(_mm256_add_ps(valueIg, _mm256_mul_ps(prevState, checkI)));
valueFg = actGate(_mm256_add_ps(valueFg, _mm256_mul_ps(prevState, checkF)));
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
valueIn = activation(valueIn, active_node);
valueIg = activation(
_mm256_add_ps(valueIg, _mm256_mul_ps(prevState, checkI)), active_gate);
valueFg = activation(
_mm256_add_ps(valueFg, _mm256_mul_ps(prevState, checkF)), active_gate);
state = _mm256_add_ps(_mm256_mul_ps(valueIn, valueIg),
_mm256_mul_ps(prevState, valueFg));
valueOg = actGate(_mm256_add_ps(valueOg, _mm256_mul_ps(state, checkO)));
stateAtv = actState(state);
valueOg = activation(_mm256_add_ps(valueOg, _mm256_mul_ps(state, checkO)),
active_gate);
stateAtv = activation(state, active_state);
output = _mm256_mul_ps(valueOg, stateAtv);
}
#endif
......@@ -95,16 +75,6 @@ class lstm {
namespace backward {
template <typename T>
DEVICE inline T sigmoid(const T a, const T b) {
return a * b * (1.0 - b);
}
template <typename T>
DEVICE inline T tanh(const T a, const T b) {
return a * (1.0 - b * b);
}
template <class T>
class lstm {
public:
......@@ -113,29 +83,20 @@ class lstm {
T &prevState, T &prevStateGrad, T &state,
T &stateGrad, T &stateAtv, T &outputGrad,
T &checkI, T &checkF, T &checkO, T &checkIGrad,
T &checkFGrad, T &checkOGrad) {
#if 0
// TODO(qingqing) support to activation speficed by users
gradOg = actGate(outputGrad * stateAtv, valueOg);
stateGrad += actState(outputGrad * valueOg, stateAtv) + gradOg * checkO;
gradIn = actInput(stateGrad * valueIg, valueIn);
gradIg = actGate(stateGrad * valueIn, valueIg);
gradFg = actGate(stateGrad * prevState, valueFg);
T &checkFGrad, T &checkOGrad,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
gradOg = activation(outputGrad * stateAtv, valueOg, active_gate);
stateGrad += activation(outputGrad * valueOg, stateAtv, active_state) +
gradOg * checkO;
gradIn = activation(stateGrad * valueIg, valueIn, active_node);
gradIg = activation(stateGrad * valueIn, valueIg, active_gate);
gradFg = activation(stateGrad * prevState, valueFg, active_gate);
prevStateGrad = gradIg * checkI + gradFg * checkF + stateGrad * valueFg;
checkIGrad = gradIg * prevState;
checkFGrad = gradFg * prevState;
checkOGrad = gradOg * state;
#else
gradOg = sigmoid<T>(outputGrad * stateAtv, valueOg);
stateGrad += tanh<T>(outputGrad * valueOg, stateAtv) + gradOg * checkO;
gradIn = tanh<T>(stateGrad * valueIg, valueIn);
gradIg = sigmoid<T>(stateGrad * valueIn, valueIg);
gradFg = sigmoid<T>(stateGrad * prevState, valueFg);
prevStateGrad = gradIg * checkI + gradFg * checkF + stateGrad * valueFg;
checkIGrad = gradIg * prevState;
checkFGrad = gradFg * prevState;
checkOGrad = gradOg * state;
#endif
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -143,24 +104,26 @@ class lstm {
#else
// Only float support AVX optimization
static const bool avx = std::is_same<T, float>::value;
HOSTDEVICE void operator()(__m256 &valueIn, __m256 &valueIg, __m256 &valueFg,
__m256 &valueOg, __m256 &gradIn, __m256 &gradIg,
__m256 &gradFg, __m256 &gradOg, __m256 &prevState,
__m256 &prevStateGrad, __m256 &state,
__m256 &stateGrad, __m256 &stateAtv,
__m256 &outputGrad, __m256 &checkI, __m256 &checkF,
__m256 &checkO, __m256 &checkIGrad,
__m256 &checkFGrad, __m256 &checkOGrad,
hppl::Active<__m256>::backward actInput,
hppl::Active<__m256>::backward actGate,
hppl::Active<__m256>::backward actState) {
gradOg = actGate(_mm256_mul_ps(outputGrad, stateAtv), valueOg);
HOSTDEVICE void operator()(
__m256 &valueIn, __m256 &valueIg, __m256 &valueFg, __m256 &valueOg,
__m256 &gradIn, __m256 &gradIg, __m256 &gradFg, __m256 &gradOg,
__m256 &prevState, __m256 &prevStateGrad, __m256 &state,
__m256 &stateGrad, __m256 &stateAtv, __m256 &outputGrad, __m256 &checkI,
__m256 &checkF, __m256 &checkO, __m256 &checkIGrad, __m256 &checkFGrad,
__m256 &checkOGrad, activation_mode_t active_node,
activation_mode_t active_gate, activation_mode_t active_state) {
gradOg =
activation(_mm256_mul_ps(outputGrad, stateAtv), valueOg, active_gate);
stateGrad = _mm256_add_ps(
actState(_mm256_mul_ps(outputGrad, valueOg), stateAtv), stateGrad);
activation(_mm256_mul_ps(outputGrad, valueOg), stateAtv, active_state),
stateGrad);
stateGrad = _mm256_add_ps(_mm256_mul_ps(gradOg, checkO), stateGrad);
gradIn = actInput(_mm256_mul_ps(stateGrad, valueIg), valueIn);
gradIg = actGate(_mm256_mul_ps(stateGrad, valueIn), valueIg);
gradFg = actGate(_mm256_mul_ps(stateGrad, prevState), valueFg);
gradIn =
activation(_mm256_mul_ps(stateGrad, valueIg), valueIn, active_node);
gradIg =
activation(_mm256_mul_ps(stateGrad, valueIn), valueIg, active_gate);
gradFg =
activation(_mm256_mul_ps(stateGrad, prevState), valueFg, active_gate);
prevStateGrad = _mm256_add_ps(_mm256_mul_ps(gradIg, checkI),
_mm256_mul_ps(gradFg, checkF));
prevStateGrad =
......
python/paddle/v2/framework/tests/test_lstm_op.py
浏览文件 @ 1c8a0c4b
......@@ -157,7 +157,7 @@ class TestLstmOp(OpTest):
}
def test_check_output(self):
self.check_output()
self.check_output(atol=1e-8)
#TODO(qingqing) add more unit testing case
def test_check_grad(self):
......@@ -167,7 +167,7 @@ class TestLstmOp(OpTest):
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=0.02)
['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
class TestLstmOpHasNoInitial(TestLstmOp):
......
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