jit_kernel_crf_decode.cc 17.9 KB
Newer Older
T
tensor-tang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155
/* 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/jit_kernel.h"
#include <limits>
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"

namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {

namespace jit = platform::jit;

/* CRF Decode JitKernel */
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
 public:
  explicit CRFDecodeKernelImpl(int tag_num) : CRFDecodeKernel<T>() {
    this->num_ = tag_num;
  }
  void Compute(const int seq_len, const T* x, const T* w, T* alpha,
               int* track) const override {
    constexpr int state_trans_base_idx = 2;
    for (int i = 0; i < this->num_; ++i) {
      alpha[i] = w[i] + x[i];
    }
    for (int k = 1; k < seq_len; ++k) {
      for (int i = 0; i < this->num_; ++i) {
        T max_score = -std::numeric_limits<T>::max();
        int max_j = 0;
        for (int j = 0; j < this->num_; ++j) {
          T score = alpha[(k - 1) * this->num_ + j] +
                    w[(j + state_trans_base_idx) * this->num_ + i];
          if (score > max_score) {
            max_score = score;
            max_j = j;
          }
        }
        alpha[k * this->num_ + i] = max_score + x[k * this->num_ + i];
        track[k * this->num_ + i] = max_j;
      }
    }
  }
};

#define INIT_ALPHA(step_size)                                               \
  /* Setup the alpha initial value.*/                                       \
  int i_offset = 0;                                                         \
  int last_offset = this->rest_ - step_size;                                \
  for (int i = 0; i <= this->end_; ++i) {                                   \
    /* weights, input and alpha values. */                                  \
    __m256 w_content, x_content, alpha_content;                             \
    /* Load the relevant data into the variables from un-aligned address.*/ \
    w_content = _mm256_loadu_ps(w + i_offset);                              \
    x_content = _mm256_loadu_ps(x + i_offset);                              \
    alpha_content = _mm256_add_ps(w_content, x_content);                    \
    _mm256_storeu_ps(alpha + i_offset, alpha_content);                      \
    i_offset += step_size;                                                  \
    if (i == this->end_ - 1) {                                              \
      if (this->rest_ > 0) {                                                \
        i_offset += last_offset;                                            \
      } else {                                                              \
        break;                                                              \
      }                                                                     \
    }                                                                       \
  }

#define UPDATE_ALPHA(step_size)                                               \
  /* Update the alpha and track values. */                                    \
  __m256 x_content = _mm256_loadu_ps(x + seq_offset + this->num_ + j_offset); \
  max_score = _mm256_add_ps(max_score, x_content);                            \
  _mm256_storeu_ps(alpha + seq_offset + this->num_ + j_offset, max_score);    \
  _mm256_storeu_si256(                                                        \
      reinterpret_cast<__m256i*>(track + seq_offset + this->num_ + j_offset), \
      max_j);                                                                 \
  /* Calculate the offset of next step*/                                      \
  j_offset += step_size;                                                      \
  if (j == this->end_ - 1) {                                                  \
    if (this->rest_ > 0) {                                                    \
      j_offset += last_offset;                                                \
    } else {                                                                  \
      break;                                                                  \
    }                                                                         \
  }

#define INTRIAVX_FLOAT(block)                                                  \
  template <>                                                                  \
  CRFDecodeKernelImpl<float, jit::avx, block>::CRFDecodeKernelImpl(            \
      int tag_num)                                                             \
      : CRFDecodeKernel<float>() {                                             \
    this->num_ = tag_num;                                                      \
    this->end_ = this->num_ / AVX_FLOAT_BLOCK;                                 \
    this->rest_ = this->num_ % AVX_FLOAT_BLOCK;                                \
  }                                                                            \
  template <>                                                                  \
  void CRFDecodeKernelImpl<float, jit::avx, block>::Compute(                   \
      const int seq_len, const float* x, const float* w, float* alpha,         \
      int* track) const {                                                      \
    INIT_ALPHA(AVX_FLOAT_BLOCK)                                                \
    /* Use the column-major strategy to get the location of maximum score.*/   \
    int seq_offset = 0;                                                        \
    constexpr int state_trans_base_idx = 2;                                    \
    for (int k = 1; k < seq_len; ++k) {                                        \
      int j_offset = 0;                                                        \
      for (int j = 0; j <= this->end_; ++j) {                                  \
        /* Initialize the variables of maximum score and location.*/           \
        __m256 max_score = _mm256_set1_ps(-std::numeric_limits<float>::max()); \
        __m256i max_j = _mm256_set1_epi32(0);                                  \
        /* Calculate the offset of transition_weights.*/                       \
        int trans_offset = state_trans_base_idx * this->num_ + j_offset;       \
        for (int i = 0; i < this->num_; ++i) {                                 \
          /* Initalize the content of alpha variable with related offset.*/    \
          __m256 alpha_content = _mm256_broadcast_ss(alpha + seq_offset + i);  \
          /* Obtain the content of weights from un-aligned address.*/          \
          __m256 w_content = _mm256_loadu_ps(w + trans_offset);                \
          __m256 score_v = _mm256_add_ps(alpha_content, w_content);            \
          __m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS);         \
          /* According to the mask value, update the index of the max_score.*/ \
          /* AVX instructions.*/                                               \
          __m128i lo_max_j = _mm256_extractf128_si256(max_j, 0);               \
          __m128i hi_max_j = _mm256_extractf128_si256(max_j, 1);               \
          __m128i lo_mask = _mm256_extractf128_si256((__m256i)mask, 0);        \
          __m128i hi_mask = _mm256_extractf128_si256((__m256i)mask, 1);        \
          lo_max_j = _mm_andnot_si128(lo_mask, lo_max_j);                      \
          hi_max_j = _mm_andnot_si128(hi_mask, hi_max_j);                      \
          lo_mask = _mm_and_si128(lo_mask, _mm_set1_epi32(i));                 \
          hi_mask = _mm_and_si128(hi_mask, _mm_set1_epi32(i));                 \
          lo_max_j = _mm_or_si128(lo_mask, lo_max_j);                          \
          hi_max_j = _mm_or_si128(hi_mask, hi_max_j);                          \
          max_j = _mm256_insertf128_si256(max_j, lo_max_j, 0);                 \
          max_j = _mm256_insertf128_si256(max_j, hi_max_j, 1);                 \
          /* AVX done*/                                                        \
          /* Update the max_score value.*/                                     \
          max_score = _mm256_max_ps(max_score, score_v);                       \
          trans_offset += this->num_;                                          \
        }                                                                      \
        UPDATE_ALPHA(AVX_FLOAT_BLOCK)                                          \
      }                                                                        \
      seq_offset += this->num_;                                                \
    }                                                                          \
  }

T
tensor-tang 已提交
156
#define INTRIAVX2_FLOAT(isa, block)                                            \
T
tensor-tang 已提交
157
  template <>                                                                  \
T
tensor-tang 已提交
158
  CRFDecodeKernelImpl<float, isa, block>::CRFDecodeKernelImpl(int tag_num)     \
T
tensor-tang 已提交
159 160 161 162 163 164
      : CRFDecodeKernel<float>() {                                             \
    this->num_ = tag_num;                                                      \
    this->end_ = this->num_ / AVX2_FLOAT_BLOCK;                                \
    this->rest_ = this->num_ % AVX2_FLOAT_BLOCK;                               \
  }                                                                            \
  template <>                                                                  \
T
tensor-tang 已提交
165
  void CRFDecodeKernelImpl<float, isa, block>::Compute(                        \
T
tensor-tang 已提交
166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222
      const int seq_len, const float* x, const float* w, float* alpha,         \
      int* track) const {                                                      \
    INIT_ALPHA(AVX2_FLOAT_BLOCK)                                               \
    /* Use the column-major strategy to get the location of maximum score.*/   \
    int seq_offset = 0;                                                        \
    constexpr int state_trans_base_idx = 2;                                    \
    for (int k = 1; k < seq_len; ++k) {                                        \
      int j_offset = 0;                                                        \
      for (int j = 0; j <= this->end_; ++j) {                                  \
        /* Initialize the variables of maximum score and location.*/           \
        __m256 max_score = _mm256_set1_ps(-std::numeric_limits<float>::max()); \
        __m256i max_j = _mm256_set1_epi32(0);                                  \
        /* Calculate the offset of transition_weights.*/                       \
        int trans_offset = state_trans_base_idx * this->num_ + j_offset;       \
        for (int i = 0; i < this->num_; ++i) {                                 \
          /* Initalize the content of alpha variable with related offset.*/    \
          __m256 alpha_content = _mm256_broadcast_ss(alpha + seq_offset + i);  \
          /* Obtain the content of weights from un-aligned address.*/          \
          __m256 w_content = _mm256_loadu_ps(w + trans_offset);                \
          __m256 score_v = _mm256_add_ps(alpha_content, w_content);            \
          __m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS);         \
          /* According to the mask value, update the index of the max_score.*/ \
          /* AVX2 instructions.*/                                              \
          max_j = _mm256_or_si256(                                             \
              _mm256_andnot_si256((__m256i)mask, max_j),                       \
              _mm256_and_si256((__m256i)mask, _mm256_set1_epi32(i)));          \
          /* Update the max_score value.*/                                     \
          max_score = _mm256_max_ps(max_score, score_v);                       \
          trans_offset += this->num_;                                          \
        }                                                                      \
        UPDATE_ALPHA(AVX2_FLOAT_BLOCK)                                         \
      }                                                                        \
      seq_offset += this->num_;                                                \
    }                                                                          \
  }

#define INTRIAVX512_FLOAT(block)                                               \
  template <>                                                                  \
  CRFDecodeKernelImpl<float, jit::avx512f, block>::CRFDecodeKernelImpl(        \
      int tag_num)                                                             \
      : CRFDecodeKernel<float>() {                                             \
    this->num_ = tag_num;                                                      \
    this->end_ = this->num_ / AVX512_FLOAT_BLOCK;                              \
    this->rest_ = this->num_ % AVX512_FLOAT_BLOCK;                             \
  }                                                                            \
  template <>                                                                  \
  void CRFDecodeKernelImpl<float, jit::avx512f, block>::Compute(               \
      const int seq_len, const float* x, const float* w, float* alpha,         \
      int* track) const {                                                      \
    INIT_ALPHA(AVX512_FLOAT_BLOCK)                                             \
    /* Use the column-major strategy to get the location of maximum score.*/   \
    int seq_offset = 0;                                                        \
    constexpr int state_trans_base_idx = 2;                                    \
    for (int k = 1; k < seq_len; ++k) {                                        \
      int j_offset = 0;                                                        \
      for (int j = 0; j <= this->end_; ++j) {                                  \
        /* Initialize the variables of maximum score and location.*/           \
T
tensor-tang 已提交
223
        __m512 max_score = _mm512_set1_ps(-std::numeric_limits<float>::max()); \
T
tensor-tang 已提交
224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243
        __m512i max_j = _mm512_setzero_si512();                                \
        /* Calculate the offset of transition_weights.*/                       \
        int trans_offset = state_trans_base_idx * this->num_ + j_offset;       \
        for (int i = 0; i < this->num_; ++i) {                                 \
          /* Initalize the content of alpha variable with related offset.*/    \
          __m512 alpha_content = _mm512_set1_ps(*(alpha + seq_offset + i));    \
          /* Obtain the content of weights from un-aligned address.*/          \
          __m512 w_content = _mm512_loadu_ps(w + trans_offset);                \
          __m512 score_v = _mm512_add_ps(alpha_content, w_content);            \
          __mmask16 mask = _mm512_cmp_ps_mask(score_v, max_score, _CMP_GT_OS); \
          /* AVX512 instructions.*/                                            \
          max_j = _mm512_mask_set1_epi32(max_j, mask, i);                      \
          /* Update the max_score value.*/                                     \
          max_score = _mm512_max_ps(max_score, score_v);                       \
          trans_offset += this->num_;                                          \
        }                                                                      \
        /* Update the alpha and track values.*/                                \
        __m512 x_content =                                                     \
            _mm512_loadu_ps(x + seq_offset + this->num_ + j_offset);           \
        max_score = _mm512_add_ps(max_score, x_content);                       \
T
tensor-tang 已提交
244
        _mm512_storeu_ps(alpha + seq_offset + this->num_ + j_offset,           \
T
tensor-tang 已提交
245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262
                         max_score);                                           \
        _mm512_storeu_si512(reinterpret_cast<__m512i*>(track + seq_offset +    \
                                                       this->num_ + j_offset), \
                            max_j);                                            \
        /* Calculate the offset of next step*/                                 \
        j_offset += AVX512_FLOAT_BLOCK;                                        \
        if (j == this->end_ - 1) {                                             \
          if (this->rest_ > 0) {                                               \
            j_offset += last_offset;                                           \
          } else {                                                             \
            break;                                                             \
          }                                                                    \
        }                                                                      \
      }                                                                        \
      seq_offset += this->num_;                                                \
    }                                                                          \
  }

D
dzhwinter 已提交
263
#ifndef _WIN32  // commented out crf decoding
T
tensor-tang 已提交
264 265 266 267 268 269 270
#ifdef __AVX__
INTRIAVX_FLOAT(kEQ8);
INTRIAVX_FLOAT(kGT8LT16);
INTRIAVX_FLOAT(kEQ16);
INTRIAVX_FLOAT(kGT16);
#endif
#ifdef __AVX2__
T
tensor-tang 已提交
271 272 273 274
INTRIAVX2_FLOAT(jit::avx2, kEQ8);
INTRIAVX2_FLOAT(jit::avx2, kGT8LT16);
INTRIAVX2_FLOAT(jit::avx2, kEQ16);
INTRIAVX2_FLOAT(jit::avx2, kGT16);
T
tensor-tang 已提交
275
#endif
D
dzhwinter 已提交
276
#endif  // WIN32
T
tensor-tang 已提交
277
#ifdef __AVX512F__
T
tensor-tang 已提交
278 279
INTRIAVX2_FLOAT(jit::avx512f, kEQ8);
INTRIAVX2_FLOAT(jit::avx512f, kGT8LT16);
T
tensor-tang 已提交
280 281 282 283 284 285 286 287 288 289
INTRIAVX512_FLOAT(kEQ16);
INTRIAVX512_FLOAT(kGT16);
#endif

#undef INTRIAVX512_FLOAT
#undef INTRIAVX2_FLOAT
#undef INTRIAVX_FLOAT
#undef INIT_ALPHA
#undef UPDATE_ALPHA

T
tensor-tang 已提交
290
REGISTER_JITKERNEL_DEPRECATED(crf_decode, CRFDecodeKernel);
T
tensor-tang 已提交
291 292 293 294 295

}  // namespace jitkernel
}  // namespace math
}  // namespace operators
}  // namespace paddle