lstm_cpu_kernel.h 11.4 KB
Newer Older
1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
D
dangqingqing 已提交
2 3 4 5 6 7 8 9 10 11 12 13 14 15

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>
Y
Yi Wang 已提交
17 18
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
D
dangqingqing 已提交
19

P
peizhilin 已提交
20 21 22 23 24 25
#if defined(_WIN32)
#if defined(__AVX2__) || defined(__AVX__)
inline __m256 operator+=(__m256 a, __m256 b) { return _mm256_add_ps(a, b); }
#endif
#endif

D
dangqingqing 已提交
26 27 28 29 30 31 32 33
namespace paddle {
namespace operators {
namespace math {
namespace detail {

#ifndef __NVCC__

template <class T, class Op>
34
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
D
dangqingqing 已提交
35
                                     int frame_size, ActivationType active_node,
36 37
                                     ActivationType active_gate,
                                     ActivationType active_state) {
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
  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
66 67
    }

68 69 70
    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);
71 72 73 74 75 76 77 78

    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
79 80 81 82
  }
}

template <class T, class Op>
83
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
84
                                      LstmMetaGrad<T> grad, int frame_size,
85 86 87
                                      ActivationType active_node,
                                      ActivationType active_gate,
                                      ActivationType active_state) {
88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
  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
132 133
    }

134 135 136 137 138
    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);
139 140 141 142 143 144 145 146 147 148 149

    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
150
    }
151
    if (grad.check_og_grad) grad.check_og_grad[i] += r_checkOGrad;
D
dangqingqing 已提交
152 153 154
  }
}

155
template <class T, class Op>
156
void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
D
dangqingqing 已提交
157
                                   int frame_size, ActivationType active_node,
158 159
                                   ActivationType active_gate,
                                   ActivationType active_state) {
D
dangqingqing 已提交
160
#ifdef __AVX__
161 162 163 164 165 166 167 168 169 170 171 172
  __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;

173 174 175 176 177 178
  __m256 *value_in = reinterpret_cast<__m256 *>(value.gate_value);
  __m256 *value_ig = reinterpret_cast<__m256 *>(value.gate_value + frame_size);
  __m256 *value_fg =
      reinterpret_cast<__m256 *>(value.gate_value + frame_size * 2);
  __m256 *value_og =
      reinterpret_cast<__m256 *>(value.gate_value + frame_size * 3);
179 180 181 182 183 184 185

  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) {
186 187 188
      r_checkI = (reinterpret_cast<__m256 *>(value.check_ig))[i];
      r_checkF = (reinterpret_cast<__m256 *>(value.check_fg))[i];
      r_checkO = (reinterpret_cast<__m256 *>(value.check_og))[i];
D
dangqingqing 已提交
189
    }
D
dangqingqing 已提交
190

191
    if (value.prev_state_value) {
192
      r_prev_state = (reinterpret_cast<__m256 *>(value.prev_state_value))[i];
D
dangqingqing 已提交
193 194
    }

195 196 197
    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);
198 199 200 201 202

    value_in[i] = r_value_in;
    value_ig[i] = r_value_ig;
    value_fg[i] = r_value_fg;
    value_og[i] = r_value_og;
203 204 205
    (reinterpret_cast<__m256 *>(value.state_value))[i] = r_state;
    (reinterpret_cast<__m256 *>(value.state_active_value))[i] = r_state_atv;
    (reinterpret_cast<__m256 *>(value.output_value))[i] = r_out;
D
dangqingqing 已提交
206 207 208 209
  }
#endif
}

210 211
template <class T, class Op>
void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
212
                                    LstmMetaGrad<T> grad, int frame_size,
213 214 215
                                    ActivationType active_node,
                                    ActivationType active_gate,
                                    ActivationType active_state) {
D
dangqingqing 已提交
216
#ifdef __AVX__
217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237
  __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;

238 239 240 241 242 243 244 245 246 247
  __m256 *value_in = reinterpret_cast<__m256 *>(value.gate_value);
  __m256 *value_ig = reinterpret_cast<__m256 *>(value.gate_value + frame_size);
  __m256 *value_fg =
      reinterpret_cast<__m256 *>(value.gate_value + frame_size * 2);
  __m256 *value_og =
      reinterpret_cast<__m256 *>(value.gate_value + frame_size * 3);
  __m256 *grad_in = reinterpret_cast<__m256 *>(grad.gate_grad);
  __m256 *grad_ig = reinterpret_cast<__m256 *>(grad.gate_grad + frame_size);
  __m256 *grad_fg = reinterpret_cast<__m256 *>(grad.gate_grad + frame_size * 2);
  __m256 *grad_og = reinterpret_cast<__m256 *>(grad.gate_grad + frame_size * 3);
248 249 250 251 252 253 254

  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) {
255 256 257
      r_checkI = (reinterpret_cast<__m256 *>(value.check_ig))[i];
      r_checkF = (reinterpret_cast<__m256 *>(value.check_fg))[i];
      r_checkO = (reinterpret_cast<__m256 *>(value.check_og))[i];
D
dangqingqing 已提交
258
    }
259 260 261 262
    r_state = (reinterpret_cast<__m256 *>(value.state_value))[i];
    r_state_atv = (reinterpret_cast<__m256 *>(value.state_active_value))[i];
    r_output_grad = (reinterpret_cast<__m256 *>(grad.output_grad))[i];
    r_state_grad = (reinterpret_cast<__m256 *>(grad.state_grad))[i];
263
    if (value.prev_state_value) {
264
      r_prev_state = (reinterpret_cast<__m256 *>(value.prev_state_value))[i];
D
dangqingqing 已提交
265 266
    }

267 268 269 270 271
    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);
272 273 274 275 276

    grad_in[i] = r_grad_in;
    grad_ig[i] = r_grad_ig;
    grad_fg[i] = r_grad_fg;
    grad_og[i] = r_grad_og;
277
    (reinterpret_cast<__m256 *>(grad.state_grad))[i] = r_state_grad;
278 279

    if (grad.prev_state_grad)
280
      (reinterpret_cast<__m256 *>(grad.prev_state_grad))[i] = r_prev_state_grad;
281
    if (value.prev_state_value) {
282 283 284 285
      if (grad.check_ig_grad)
        (reinterpret_cast<__m256 *>(grad.check_ig_grad))[i] += r_checkIGrad;
      if (grad.check_fg_grad)
        (reinterpret_cast<__m256 *>(grad.check_fg_grad))[i] += r_checkFGrad;
D
dangqingqing 已提交
286
    }
287 288
    if (grad.check_og_grad)
      (reinterpret_cast<__m256 *>(grad.check_og_grad))[i] += r_checkOGrad;
D
dangqingqing 已提交
289 290 291 292 293
  }
#endif
}

template <class T, class Op>
294
void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frame_size,
D
dangqingqing 已提交
295
                      ActivationType active_node, ActivationType active_gate,
296
                      ActivationType active_state) {
297 298
  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,
299
                                     active_gate, active_state);
D
dangqingqing 已提交
300
  } else {
301
    naive_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
302
                                       active_gate, active_state);
D
dangqingqing 已提交
303 304 305 306
  }
}

template <class T, class Op>
307
void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
308 309 310
                       int frame_size, ActivationType active_node,
                       ActivationType active_gate,
                       ActivationType active_state) {
311 312
  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,
313
                                      active_gate, active_state);
D
dangqingqing 已提交
314
  } else {
315 316
    naive_lstm_backward_one_sequence<T>(op, value, grad, frame_size,
                                        active_node, active_gate, active_state);
D
dangqingqing 已提交
317 318 319 320 321 322 323 324 325
  }
}

#endif

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