lstm_cpu_kernel.h 10.7 KB
Newer Older
D
dangqingqing 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
/* 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
16
#include <type_traits>
17
#include "paddle/operators/math/detail/activation_functions.h"
D
dangqingqing 已提交
18 19 20 21 22 23 24 25 26 27
#include "paddle/operators/math/lstm_compute.h"

namespace paddle {
namespace operators {
namespace math {
namespace detail {

#ifndef __NVCC__

template <class T, class Op>
28
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
29
                                     int frame_size,
30 31 32
                                     ActivationType active_node,
                                     ActivationType active_gate,
                                     ActivationType active_state) {
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
  T r_value_in;
  T r_value_ig;
  T r_value_fg;
  T r_value_og;
  T r_checkI;
  T r_checkF;
  T r_checkO;
  T r_state;
  T r_prev_state = 0;
  T r_state_atv;
  T r_out;

  T *value_in = value.gate_value;
  T *value_ig = value.gate_value + frame_size;
  T *value_fg = value.gate_value + frame_size * 2;
  T *value_og = value.gate_value + frame_size * 3;

  for (int i = 0; i < frame_size; i++) {
    r_value_in = value_in[i];
    r_value_ig = value_ig[i];
    r_value_fg = value_fg[i];
    r_value_og = value_og[i];
    r_checkI = value.check_ig ? value.check_ig[i] : 0;
    r_checkF = value.check_fg ? value.check_fg[i] : 0;
    r_checkO = value.check_og ? value.check_og[i] : 0;

    if (value.prev_state_value) {
      r_prev_state = value.prev_state_value[i];
D
dangqingqing 已提交
61 62
    }

63 64 65 66 67 68 69 70 71 72 73
    op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_prev_state, r_state,
       r_state_atv, r_out, r_checkI, r_checkF, r_checkO, active_node,
       active_gate, active_state);

    value_in[i] = r_value_in;
    value_ig[i] = r_value_ig;
    value_fg[i] = r_value_fg;
    value_og[i] = r_value_og;
    value.state_value[i] = r_state;
    value.state_active_value[i] = r_state_atv;
    value.output_value[i] = r_out;
D
dangqingqing 已提交
74 75 76 77
  }
}

template <class T, class Op>
78
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
79
                                      LstmMetaGrad<T> grad, int frame_size,
80 81 82
                                      ActivationType active_node,
                                      ActivationType active_gate,
                                      ActivationType active_state) {
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
  T r_value_in;
  T r_value_ig;
  T r_value_fg;
  T r_value_og;
  T r_grad_in;
  T r_grad_ig;
  T r_grad_fg;
  T r_grad_og;
  T r_prev_state = 0;
  T r_prev_state_grad;
  T r_state;
  T r_state_grad;
  T r_state_atv;
  T r_output_grad;
  T r_checkI;
  T r_checkF;
  T r_checkO;
  T r_checkIGrad;
  T r_checkFGrad;
  T r_checkOGrad;

  T *value_in = value.gate_value;
  T *value_ig = value.gate_value + frame_size;
  T *value_fg = value.gate_value + frame_size * 2;
  T *value_og = value.gate_value + frame_size * 3;
  T *grad_in = grad.gate_grad;
  T *grad_ig = grad.gate_grad + frame_size;
  T *grad_fg = grad.gate_grad + frame_size * 2;
  T *grad_og = grad.gate_grad + frame_size * 3;

  for (int i = 0; i < frame_size; i++) {
    r_value_in = value_in[i];
    r_value_ig = value_ig[i];
    r_value_fg = value_fg[i];
    r_value_og = value_og[i];
    r_checkI = value.check_ig ? value.check_ig[i] : 0;
    r_checkF = value.check_fg ? value.check_fg[i] : 0;
    r_checkO = value.check_og ? value.check_og[i] : 0;
    r_state = value.state_value[i];
    r_state_atv = value.state_active_value[i];
    r_output_grad = grad.output_grad[i];
    r_state_grad = grad.state_grad[i];
    if (value.prev_state_value) {
      r_prev_state = value.prev_state_value[i];
D
dangqingqing 已提交
127 128
    }

129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
    op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_grad_in, r_grad_ig,
       r_grad_fg, r_grad_og, r_prev_state, r_prev_state_grad, r_state,
       r_state_grad, r_state_atv, r_output_grad, r_checkI, r_checkF, r_checkO,
       r_checkIGrad, r_checkFGrad, r_checkOGrad, active_node, active_gate,
       active_state);

    grad_in[i] = r_grad_in;
    grad_ig[i] = r_grad_ig;
    grad_fg[i] = r_grad_fg;
    grad_og[i] = r_grad_og;
    grad.state_grad[i] = r_state_grad;

    if (grad.prev_state_grad) grad.prev_state_grad[i] = r_prev_state_grad;
    if (value.prev_state_value) {
      if (grad.check_ig_grad) grad.check_ig_grad[i] += r_checkIGrad;
      if (grad.check_fg_grad) grad.check_fg_grad[i] += r_checkFGrad;
D
dangqingqing 已提交
145
    }
146
    if (grad.check_og_grad) grad.check_og_grad[i] += r_checkOGrad;
D
dangqingqing 已提交
147 148 149
  }
}

150
template <class T, class Op>
151 152
void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
                                   int frame_size,
153 154 155
                                   ActivationType active_node,
                                   ActivationType active_gate,
                                   ActivationType active_state) {
D
dangqingqing 已提交
156
#ifdef __AVX__
157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182
  __m256 r_value_in;
  __m256 r_value_ig;
  __m256 r_value_fg;
  __m256 r_value_og;
  __m256 r_checkI = _mm256_set1_ps(0.0f);
  __m256 r_checkF = _mm256_set1_ps(0.0f);
  __m256 r_checkO = _mm256_set1_ps(0.0f);
  __m256 r_state;
  __m256 r_prev_state = _mm256_set1_ps(0.0f);
  __m256 r_state_atv;
  __m256 r_out;

  __m256 *value_in = (__m256 *)value.gate_value;
  __m256 *value_ig = (__m256 *)(value.gate_value + frame_size);
  __m256 *value_fg = (__m256 *)(value.gate_value + frame_size * 2);
  __m256 *value_og = (__m256 *)(value.gate_value + frame_size * 3);

  for (int i = 0; i < frame_size / 8; i++) {
    r_value_in = value_in[i];
    r_value_ig = value_ig[i];
    r_value_fg = value_fg[i];
    r_value_og = value_og[i];
    if (value.check_ig) {
      r_checkI = ((__m256 *)value.check_ig)[i];
      r_checkF = ((__m256 *)value.check_fg)[i];
      r_checkO = ((__m256 *)value.check_og)[i];
D
dangqingqing 已提交
183
    }
D
dangqingqing 已提交
184

185 186
    if (value.prev_state_value) {
      r_prev_state = ((__m256 *)value.prev_state_value)[i];
D
dangqingqing 已提交
187 188
    }

189 190 191 192 193 194 195 196 197 198 199
    op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_prev_state, r_state,
       r_state_atv, r_out, r_checkI, r_checkF, r_checkO, active_node,
       active_gate, active_state);

    value_in[i] = r_value_in;
    value_ig[i] = r_value_ig;
    value_fg[i] = r_value_fg;
    value_og[i] = r_value_og;
    ((__m256 *)value.state_value)[i] = r_state;
    ((__m256 *)value.state_active_value)[i] = r_state_atv;
    ((__m256 *)value.output_value)[i] = r_out;
D
dangqingqing 已提交
200 201 202 203
  }
#endif
}

204 205
template <class T, class Op>
void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
206
                                    LstmMetaGrad<T> grad, int frame_size,
207 208 209
                                    ActivationType active_node,
                                    ActivationType active_gate,
                                    ActivationType active_state) {
D
dangqingqing 已提交
210
#ifdef __AVX__
211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249
  __m256 r_value_in;
  __m256 r_value_ig;
  __m256 r_value_fg;
  __m256 r_value_og;
  __m256 r_grad_in;
  __m256 r_grad_ig;
  __m256 r_grad_fg;
  __m256 r_grad_og;
  __m256 r_prev_state = _mm256_set1_ps(0.0f);
  __m256 r_prev_state_grad;
  __m256 r_state_grad;
  __m256 r_state;
  __m256 r_state_atv;
  __m256 r_output_grad;
  __m256 r_checkI = _mm256_set1_ps(0.0f);
  __m256 r_checkF = _mm256_set1_ps(0.0f);
  __m256 r_checkO = _mm256_set1_ps(0.0f);
  __m256 r_checkIGrad;
  __m256 r_checkFGrad;
  __m256 r_checkOGrad;

  __m256 *value_in = (__m256 *)value.gate_value;
  __m256 *value_ig = (__m256 *)(value.gate_value + frame_size);
  __m256 *value_fg = (__m256 *)(value.gate_value + frame_size * 2);
  __m256 *value_og = (__m256 *)(value.gate_value + frame_size * 3);
  __m256 *grad_in = (__m256 *)grad.gate_grad;
  __m256 *grad_ig = (__m256 *)(grad.gate_grad + frame_size);
  __m256 *grad_fg = (__m256 *)(grad.gate_grad + frame_size * 2);
  __m256 *grad_og = (__m256 *)(grad.gate_grad + frame_size * 3);

  for (int i = 0; i < frame_size / 8; i++) {
    r_value_in = value_in[i];
    r_value_ig = value_ig[i];
    r_value_fg = value_fg[i];
    r_value_og = value_og[i];
    if (value.check_ig) {
      r_checkI = ((__m256 *)value.check_ig)[i];
      r_checkF = ((__m256 *)value.check_fg)[i];
      r_checkO = ((__m256 *)value.check_og)[i];
D
dangqingqing 已提交
250
    }
251 252 253 254 255 256
    r_state = ((__m256 *)value.state_value)[i];
    r_state_atv = ((__m256 *)value.state_active_value)[i];
    r_output_grad = ((__m256 *)grad.output_grad)[i];
    r_state_grad = ((__m256 *)grad.state_grad)[i];
    if (value.prev_state_value) {
      r_prev_state = ((__m256 *)value.prev_state_value)[i];
D
dangqingqing 已提交
257 258
    }

259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275
    op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_grad_in, r_grad_ig,
       r_grad_fg, r_grad_og, r_prev_state, r_prev_state_grad, r_state,
       r_state_grad, r_state_atv, r_output_grad, r_checkI, r_checkF, r_checkO,
       r_checkIGrad, r_checkFGrad, r_checkOGrad, active_node, active_gate,
       active_state);

    grad_in[i] = r_grad_in;
    grad_ig[i] = r_grad_ig;
    grad_fg[i] = r_grad_fg;
    grad_og[i] = r_grad_og;
    ((__m256 *)grad.state_grad)[i] = r_state_grad;

    if (grad.prev_state_grad)
      ((__m256 *)grad.prev_state_grad)[i] = r_prev_state_grad;
    if (value.prev_state_value) {
      if (grad.check_ig_grad) ((__m256 *)grad.check_ig_grad)[i] += r_checkIGrad;
      if (grad.check_fg_grad) ((__m256 *)grad.check_fg_grad)[i] += r_checkFGrad;
D
dangqingqing 已提交
276
    }
277
    if (grad.check_og_grad) ((__m256 *)grad.check_og_grad)[i] += r_checkOGrad;
D
dangqingqing 已提交
278 279 280 281 282
  }
#endif
}

template <class T, class Op>
283
void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frame_size,
284 285 286
                      ActivationType active_node,
                      ActivationType active_gate,
                      ActivationType active_state) {
287 288
  if (Op::avx && !(frame_size & (8 - 1)) && (std::is_same<T, float>::value)) {
    avx_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
289
                                     active_gate, active_state);
D
dangqingqing 已提交
290
  } else {
291
    naive_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
292
                                       active_gate, active_state);
D
dangqingqing 已提交
293 294 295 296
  }
}

template <class T, class Op>
297
void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
298 299 300
                       int frame_size, ActivationType active_node,
                       ActivationType active_gate,
                       ActivationType active_state) {
301 302
  if (Op::avx && !(frame_size & (8 - 1)) && (std::is_same<T, float>::value)) {
    avx_lstm_backward_one_sequence<T>(op, value, grad, frame_size, active_node,
303
                                      active_gate, active_state);
D
dangqingqing 已提交
304
  } else {
305 306
    naive_lstm_backward_one_sequence<T>(op, value, grad, frame_size,
                                        active_node, active_gate, active_state);
D
dangqingqing 已提交
307 308 309 310 311 312 313 314 315
  }
}

#endif

}  // namespace detail
}  // namespace math
}  // namespace operators
}  // namespace paddle