cpu_lstm_compute.cc 2.9 KB
Newer Older
T
tensor-tang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
/* 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 "paddle/fluid/operators/math/cpu_lstm_compute.h"
T
tensor-tang 已提交
16 17 18 19 20
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/platform/cpu_info.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
21

T
tensor-tang 已提交
22 23
namespace paddle {
namespace operators {
T
tensor-tang 已提交
24 25 26 27 28 29 30 31 32 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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
namespace math {

// TODO(TJ): ugly workaround, clean me
template <typename T>
void lstm_compute_ctht(T* gates, const T* ct_1, T* ct, T* ht) {
  // gates: W_ch, W_ih, W_fh, W_oh
  vec_sigmoid<T, platform::jit::avx>(24, gates + 8, gates + 8);
  vec_tanh<T, platform::jit::avx>(8, gates, gates);
  const T *i = gates + 8, *f = gates + 16, *o = gates + 24;
  const T min = SIGMOID_THRESHOLD_MIN;
  const T max = SIGMOID_THRESHOLD_MAX;
  for (int d = 0; d < 8; ++d) {
    // C_t = C_t-1 * fgated + cand_gated * igated
    ct[d] = ct_1[d] * f[d] + gates[d] * i[d];
    // H_t = act_cell(C_t) * ogated
    T tmp = ct[d] * 2;
    tmp = static_cast<T>(0) - ((tmp < min) ? min : ((tmp > max) ? max : tmp));
    vec_exp<T>(1, &tmp, &tmp);
    tmp = static_cast<T>(2) / (static_cast<T>(1) + tmp) - static_cast<T>(1);
    ht[d] = tmp * o[d];
  }
}

#ifdef __AVX__
namespace detail {
namespace forward {
namespace avx {
__m256 Sigmoid(const __m256 a);
__m256 Tanh(const __m256 a);
}  // namespace avx
}  // namespace forward
}  // namespace detail

template <>
void lstm_compute_ctht<float>(float* gates, const float* ct_1, float* ct,
                              float* ht) {
  namespace act = detail::forward::avx;
  // gates: W_ch, W_ih, W_fh, W_oh
  __m256 c, i, f, o;
  c = _mm256_loadu_ps(gates);
  i = _mm256_loadu_ps(gates + 8);
  f = _mm256_loadu_ps(gates + 16);
  o = _mm256_loadu_ps(gates + 24);

  /* C_t = C_t-1 * fgated + cand_gated * igated*/
  c = _mm256_mul_ps(act::Tanh(c), act::Sigmoid(i));
  i = _mm256_loadu_ps(ct_1);
  f = _mm256_mul_ps(i, act::Sigmoid(f));
  f = _mm256_add_ps(c, f);
  _mm256_storeu_ps(ct, f);

  /* H_t = act_cell(C_t) * ogated */
  o = _mm256_mul_ps(act::Tanh(f), act::Sigmoid(o));
  _mm256_storeu_ps(ht, o);
}
#endif

template void lstm_compute_ctht<float>(float* gates, const float* ct_1,
                                       float* ct, float* ht);
template void lstm_compute_ctht<double>(double* gates, const double* ct_1,
                                        double* ct, double* ht);

}  // namespace math
T
tensor-tang 已提交
87 88
}  // namespace operators
}  // namespace paddle