kernel_base.h 9.5 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. */

#pragma once
16
#include <cstdint>
17
#include "paddle/fluid/operators/jit/macro.h"
T
tensor-tang 已提交
18 19 20 21
#include "paddle/fluid/platform/macros.h"

namespace paddle {
namespace operators {
T
tensor-tang 已提交
22
namespace jit {
T
tensor-tang 已提交
23

24
typedef enum {
T
tensor-tang 已提交
25
  kNone = 0,
26 27 28
  // sort by alphabet
  kCRFDecoding = 1,
  kEmbSeqPool = 2,
T
tensor-tang 已提交
29 30 31
  kGRUH1,
  kGRUHtPart1,
  kGRUHtPart2,
32 33 34 35
  kHSum,  // horizontal max
  kHMax,  // horizontal sum
  kLSTMCtHt,
  kLSTMC1H1,
T
tensor-tang 已提交
36
  kLayerNorm,
37
  kMatMul,
T
tensor-tang 已提交
38
  kNCHW16CMulNC,
T
tensor-tang 已提交
39
  kSeqPool,
40
  kSoftmax,
D
dengkaipeng 已提交
41 42
  kStrideASum,
  kStrideScal,
43 44 45
  kVAdd,
  kVAddBias,
  kVAddRelu,
46
  kVBroadcast,
47
  kVCopy,
48 49 50 51 52
  kVExp,
  kVIdentity,
  kVMul,
  kVRelu,
  kVScal,
53
  kSgd,
54 55 56 57
  kVSigmoid,
  kVSquare,
  kVSub,
  kVTanh,
58
} KernelType;
T
tensor-tang 已提交
59

60 61
typedef enum {
  kNonePoolType = 0,
T
tensor-tang 已提交
62
  kSum = 1,
63 64 65 66
  kAvg,
  kSqrt,
} SeqPoolType;

67
// x, y, z, n
T
tensor-tang 已提交
68
template <typename T>
69
struct XYZNTuple {
T
tensor-tang 已提交
70 71 72 73 74
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(const T*, const T*, T*, int);
};

75
// a, x, y, n
76
template <typename T>
77
struct AXYNTuple : public XYZNTuple<T> {};
78

79 80 81 82 83 84 85 86
// a, x, y, n, stride
template <typename T>
struct AXYNSTuple {
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(const T*, const T*, T*, int, int);
};

87
// x, y, n
88
template <typename T>
89
struct XYNTuple {
90 91 92 93 94
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(const T*, T*, int);
};

95
// x, returned value, n
96
template <typename T>
97 98
struct XRNTuple : public XYNTuple<T> {};

99 100 101 102 103 104 105 106
// x, returned value, n, stride
template <typename T>
struct XRNSTuple {
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(const T*, T*, int, int);
};

107 108 109 110 111 112 113 114 115 116 117 118 119 120 121
#define DECLARE_KERNELTUPLE(kernel_tuple, type)        \
  template <typename T>                                \
  struct type##Tuple : public kernel_tuple<T> {        \
    static constexpr KernelType kernel_type = k##type; \
  }

// Tuple should be corresponding to the KernelType
DECLARE_KERNELTUPLE(XYZNTuple, VMul);
DECLARE_KERNELTUPLE(XYZNTuple, VAdd);
DECLARE_KERNELTUPLE(XYZNTuple, VAddRelu);
DECLARE_KERNELTUPLE(XYZNTuple, VSub);

DECLARE_KERNELTUPLE(AXYNTuple, VScal);
DECLARE_KERNELTUPLE(AXYNTuple, VAddBias);

122 123
DECLARE_KERNELTUPLE(AXYNSTuple, StrideScal);

124 125 126 127 128 129 130 131 132 133
DECLARE_KERNELTUPLE(XYNTuple, VRelu);
DECLARE_KERNELTUPLE(XYNTuple, VIdentity);
DECLARE_KERNELTUPLE(XYNTuple, VSquare);
DECLARE_KERNELTUPLE(XYNTuple, VExp);
DECLARE_KERNELTUPLE(XYNTuple, VSigmoid);
DECLARE_KERNELTUPLE(XYNTuple, VTanh);
DECLARE_KERNELTUPLE(XYNTuple, VCopy);

DECLARE_KERNELTUPLE(XRNTuple, HMax);
DECLARE_KERNELTUPLE(XRNTuple, HSum);
134

D
dengkaipeng 已提交
135
DECLARE_KERNELTUPLE(XRNSTuple, StrideASum);
136

T
tensor-tang 已提交
137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
typedef struct {
  void* gates;  // gates: x_ch, x_ih, x_fh, x_oh
  const void* ct_1;
  void* ct;
  void* ht;
  /* weight_peephole and checked data are only used in peephole*/
  const void* wp{nullptr};  //  W_ic, W_fc, W_oc
  void* checked{nullptr};   // size: 2 * d
} lstm_t;

typedef struct {
  void* gates;  // gates: {x_update, x_reset; x_state}
  const void* ht_1;
  void* ht;
} gru_t;

struct rnn_attr_s {
  int d;
  KernelType act_gate, act_cand;
  rnn_attr_s() = default;
T
tensor-tang 已提交
157
  explicit rnn_attr_s(int _d, KernelType _act_gate, KernelType _act_cand)
T
tensor-tang 已提交
158 159 160 161 162 163 164
      : d(_d), act_gate(_act_gate), act_cand(_act_cand) {}
};

struct lstm_attr_s : public rnn_attr_s {
  bool use_peephole;
  KernelType act_cell;
  lstm_attr_s() = default;
T
tensor-tang 已提交
165 166
  explicit lstm_attr_s(int _d, KernelType _act_gate, KernelType _act_cand,
                       KernelType _act_cell, bool _use_peephole = false)
T
tensor-tang 已提交
167 168 169 170 171 172 173 174 175
      : rnn_attr_s(_d, _act_gate, _act_cand),
        use_peephole(_use_peephole),
        act_cell(_act_cell) {}
};

typedef struct rnn_attr_s gru_attr_t;
typedef struct lstm_attr_s lstm_attr_t;

template <typename T>
176
struct LSTMTuple {
T
tensor-tang 已提交
177 178 179 180 181
  typedef T data_type;
  typedef lstm_attr_t attr_type;
  typedef void (*func_type)(lstm_t*, const lstm_attr_t*);
};

182
template <typename T>
183
struct GRUTuple {
184 185 186 187 188
  typedef T data_type;
  typedef gru_attr_t attr_type;
  typedef void (*func_type)(gru_t*, const gru_attr_t*);
};

189 190 191 192 193 194 195 196 197
DECLARE_KERNELTUPLE(LSTMTuple, LSTMCtHt);
DECLARE_KERNELTUPLE(LSTMTuple, LSTMC1H1);

DECLARE_KERNELTUPLE(GRUTuple, GRUH1);
DECLARE_KERNELTUPLE(GRUTuple, GRUHtPart1);
DECLARE_KERNELTUPLE(GRUTuple, GRUHtPart2);

#undef DECLARE_KERNELTUPLE

198
template <typename T>
199 200
struct VBroadcastTuple {
  static constexpr KernelType kernel_type = kVBroadcast;
201 202 203 204 205
  typedef T data_type;
  typedef int64_t attr_type;
  typedef void (*func_type)(const T*, T*, int64_t, int64_t);
};

206
typedef struct seq_pool_attr_s {
T
tensor-tang 已提交
207
  int h, w;  // h should always be the first one
T
tensor-tang 已提交
208
  SeqPoolType type;
209
  seq_pool_attr_s() = default;
T
tensor-tang 已提交
210
  explicit seq_pool_attr_s(int width, SeqPoolType pool_type, int height = 1)
211
      : h(height), w(width), type(pool_type) {}
T
tensor-tang 已提交
212 213 214
} seq_pool_attr_t;

template <typename T>
215 216
struct SeqPoolTuple {
  static constexpr KernelType kernel_type = kSeqPool;
T
tensor-tang 已提交
217 218 219 220 221
  typedef T data_type;
  typedef seq_pool_attr_t attr_type;
  typedef void (*func_type)(const T*, T*, const seq_pool_attr_t*);
};

222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
typedef struct emb_seq_pool_attr_s {
  int64_t table_height, table_width;
  int64_t index_height, index_width;
  int64_t out_width;
  SeqPoolType pool_type;
  emb_seq_pool_attr_s() = default;
  explicit emb_seq_pool_attr_s(int64_t tbl_height, int64_t tbl_width,
                               int64_t idx_height, int64_t idx_width,
                               int64_t output_width,
                               SeqPoolType seqpool_type = SeqPoolType::kSum)
      : table_height(tbl_height),
        table_width(tbl_width),
        index_height(idx_height),
        index_width(idx_width),
        out_width(output_width),
        pool_type(seqpool_type) {}
} emb_seq_pool_attr_t;

template <typename T>
241 242
struct EmbSeqPoolTuple {
  static constexpr KernelType kernel_type = kEmbSeqPool;
243 244 245 246 247 248
  typedef T data_type;
  typedef emb_seq_pool_attr_t attr_type;
  typedef void (*func_type)(const T*, const int64_t*, T*,
                            const emb_seq_pool_attr_t*);
};

249 250 251 252 253 254 255 256 257 258 259 260 261 262 263
typedef struct sgd_attr_s {
  int64_t param_height, param_width;
  int64_t grad_height, grad_width;
  int64_t selected_rows_size;
  sgd_attr_s() = default;
  explicit sgd_attr_s(int64_t param_h, int64_t param_w, int64_t grad_h,
                      int64_t grad_w, int64_t selected_rows_sz)
      : param_height(param_h),
        param_width(param_w),
        grad_height(grad_h),
        grad_width(grad_w),
        selected_rows_size(selected_rows_sz) {}
} sgd_attr_t;

template <typename T>
264 265
struct SgdTuple {
  static constexpr KernelType kernel_type = kSgd;
266 267 268 269 270 271
  typedef T data_type;
  typedef sgd_attr_t attr_type;
  typedef void (*func_type)(const T*, const T*, const T*, const int64_t*, T*,
                            const sgd_attr_t*);
};

272 273 274 275 276 277 278 279
typedef struct matmul_attr_s {
  int m, n, k;
  void* packed_weight{nullptr};
  matmul_attr_s() = default;
  explicit matmul_attr_s(int m_, int n_, int k_, void* packed_weight_ = nullptr)
      : m(m_), n(n_), k(k_), packed_weight(packed_weight_) {}
} matmul_attr_t;

T
tensor-tang 已提交
280
template <typename T>
281 282
struct MatMulTuple {
  static constexpr KernelType kernel_type = kMatMul;
T
tensor-tang 已提交
283
  typedef T data_type;
284 285
  typedef matmul_attr_t attr_type;
  typedef void (*func_type)(const T*, const T*, T*, const matmul_attr_t*);
T
tensor-tang 已提交
286 287
};

288
template <typename T>
289 290
struct CRFDecodingTuple {
  static constexpr KernelType kernel_type = kCRFDecoding;
291 292 293 294 295 296
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(const int, const T*, const T*, T*, int*, int);
};

template <typename T>
297 298
struct LayerNormTuple {
  static constexpr KernelType kernel_type = kLayerNorm;
299 300 301 302 303 304
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(T*, T*, T*, T*, const T*, const T*, int,
                            const float, int);
};

305
template <typename T>
306 307
struct SoftmaxTuple {
  static constexpr KernelType kernel_type = kSoftmax;
308 309
  typedef T data_type;
  typedef int attr_type;
310
  typedef void (*func_type)(const T*, T*, int, int, int);
311 312
};

T
tensor-tang 已提交
313 314
// nChw16c = nChw16c .* NC
template <typename T>
315 316
struct NCHW16CMulNCTuple {
  static constexpr KernelType kernel_type = kNCHW16CMulNC;
T
tensor-tang 已提交
317 318 319 320 321
  typedef T data_type;
  typedef int attr_type;
  typedef void (*func_type)(const T*, const T*, T*, int, int);
};

T
tensor-tang 已提交
322 323 324 325
// Just for adding to kernel pool without template
class Kernel {
 public:
  Kernel() = default;
T
tensor-tang 已提交
326
  virtual ~Kernel() = default;
327
  virtual const char* ImplType() const = 0;
T
tensor-tang 已提交
328 329 330
  DISABLE_COPY_AND_ASSIGN(Kernel);
};

331
template <typename KernelTuple>
T
tensor-tang 已提交
332
class KernelMore : public Kernel {
333
 public:
334 335 336
  using T = typename KernelTuple::data_type;
  using Func = typename KernelTuple::func_type;
  using Attr = typename KernelTuple::attr_type;
T
tensor-tang 已提交
337
  virtual Func GetFunc() const { return func; }
338 339
  // specify this kernel can be used, means it should not fail if use it.
  virtual bool CanBeUsed(const Attr& attr) const = 0;
T
tensor-tang 已提交
340 341 342 343 344

 protected:
  Func func{nullptr};
};

345 346
template <typename KernelTuple>
class ReferKernel : public KernelMore<KernelTuple> {
T
tensor-tang 已提交
347 348
 public:
  // Refer code can always be used
349
  bool CanBeUsed(const typename KernelTuple::attr_type& attr) const override {
T
tensor-tang 已提交
350 351
    return true;
  }
T
tensor-tang 已提交
352
  const char* ImplType() const override { return "Refer"; }
T
tensor-tang 已提交
353 354
};

T
tensor-tang 已提交
355
}  // namespace jit
T
tensor-tang 已提交
356 357
}  // namespace operators
}  // namespace paddle