test.cc 37.6 KB
Newer Older
T
tensor-tang 已提交
1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
2 3 4 5 6 7 8 9 10 11 12 13

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. */
T
tensor-tang 已提交
14

15
#include <algorithm>
T
tensor-tang 已提交
16 17 18 19 20 21
#include <random>
#include <string>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
T
tensor-tang 已提交
22
#include "paddle/fluid/operators/jit/kernels.h"
T
tensor-tang 已提交
23
#include "paddle/fluid/platform/cpu_info.h"
T
tensor-tang 已提交
24
#include "paddle/fluid/platform/place.h"
T
tensor-tang 已提交
25

26
DEFINE_double(acc, 1e-5, "Test accuracy threshold.");
27

T
tensor-tang 已提交
28 29 30 31 32 33 34 35 36 37 38 39
template <typename T>
void RandomVec(const int n, T* a, const T lower = static_cast<T>(-20.f),
               const T upper = static_cast<T>(20.f)) {
  static unsigned int seed = 100;
  std::mt19937 rng(seed++);
  std::uniform_real_distribution<double> uniform_dist(0, 1);
  for (int i = 0; i < n; ++i) {
    a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
  }
}

template <typename T>
40
void ExpectEQ(const T* target, const T* refer, size_t n) {
T
tensor-tang 已提交
41
  if (std::is_floating_point<T>::value) {
42
    for (size_t i = 0; i < n; ++i) {
43
      EXPECT_NEAR(target[i], refer[i], FLAGS_acc);
T
tensor-tang 已提交
44 45
    }
  } else {
46
    for (size_t i = 0; i < n; ++i) {
T
tensor-tang 已提交
47 48 49 50 51
      EXPECT_EQ(target[i], refer[i]);
    }
  }
}

T
tensor-tang 已提交
52 53
std::vector<int> TestSizes() {
  std::vector<int> s;
T
tensor-tang 已提交
54
  for (int i = 1; i < 32; ++i) {
T
tensor-tang 已提交
55 56
    s.push_back(i);
  }
T
tensor-tang 已提交
57 58 59 60
  // test some large size
  s.push_back(100);
  s.push_back(1000);
  s.push_back(2000);
T
tensor-tang 已提交
61 62 63
  return s;
}

T
tensor-tang 已提交
64
namespace jit = paddle::operators::jit;
65
using CPUPlace = paddle::platform::CPUPlace;
T
tensor-tang 已提交
66 67 68

template <typename KernelTuples, typename... Args>
struct TestFuncWithRefer {
69 70 71
  void operator()(const typename KernelTuples::func_type tgt, Args... args) {
    LOG(FATAL) << "Should specify this function.";
  }
T
tensor-tang 已提交
72 73 74 75 76 77
};

template <typename T>
struct TestFuncWithRefer<jit::XYZNTuples<T>, std::vector<T>, std::vector<T>,
                         std::vector<T>> {
  void operator()(const typename jit::XYZNTuples<T>::func_type tgt,
78 79
                  const std::vector<T>& x, const std::vector<T>& y,
                  const std::vector<T>& zref) {
T
tensor-tang 已提交
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
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(zref.size(), x.size());
    EXPECT_EQ(zref.size(), y.size());
    const T* x_data = x.data();
    const T* y_data = y.data();
    const T* zref_data = zref.data();
    const int d = zref.size();

    std::vector<T> ztgt(d);
    T* ztgt_data = ztgt.data();
    // test normal
    tgt(x_data, y_data, ztgt_data, d);
    ExpectEQ<T>(ztgt_data, zref_data, d);
    // test inplace x
    std::copy(x.begin(), x.end(), ztgt.begin());
    tgt(ztgt_data, y_data, ztgt_data, d);
    ExpectEQ<T>(ztgt_data, zref_data, d);
    // test inplace y
    std::copy(y.begin(), y.end(), ztgt.begin());
    tgt(x_data, ztgt_data, ztgt_data, d);
    ExpectEQ<T>(ztgt_data, zref_data, d);
  }
};

template <typename T>
struct TestFuncWithRefer<jit::AXYNTuples<T>, T, std::vector<T>,
                         std::vector<T>> {
  void operator()(const typename jit::AXYNTuples<T>::func_type tgt, const T a,
                  const std::vector<T>& x, const std::vector<T>& yref) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(yref.size(), x.size());
    const T* x_data = x.data();
    const T* yref_data = yref.data();
    const int d = yref.size();
    std::vector<T> ytgt(d);
    T* ytgt_data = ytgt.data();
    // test normal
    tgt(&a, x_data, ytgt_data, d);
    ExpectEQ<T>(ytgt_data, yref_data, d);
    // test inplace x
    std::copy(x.begin(), x.end(), ytgt.begin());
    tgt(&a, ytgt_data, ytgt_data, d);
    ExpectEQ<T>(ytgt_data, yref_data, d);
  }
};

126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
template <typename T>
struct TestFuncWithRefer<jit::SoftmaxTuples<T>, std::vector<T>, std::vector<T>,
                         int, int> {
  void operator()(const typename jit::SoftmaxTuples<T>::func_type tgt,
                  const std::vector<T>& x, const std::vector<T>& yref, int n,
                  int bs) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(yref.size(), x.size());
    EXPECT_EQ(x.size(), static_cast<size_t>(n * bs));
    const T* x_data = x.data();
    const T* yref_data = yref.data();
    std::vector<T> ytgt(n * bs);
    T* ytgt_data = ytgt.data();
    // test normal
    tgt(x_data, ytgt_data, n, bs);
    ExpectEQ<T>(ytgt_data, yref_data, n * bs);
    // test inplace x
    std::copy(x.begin(), x.end(), ytgt.begin());
    tgt(ytgt_data, ytgt_data, n, bs);
    ExpectEQ<T>(ytgt_data, yref_data, n * bs);
  }
};

template <typename T>
struct TestFuncWithRefer<jit::XRNTuples<T>, std::vector<T>, T> {
  void operator()(const typename jit::XRNTuples<T>::func_type tgt,
                  const std::vector<T>& x, const T ref_res) {
    EXPECT_TRUE(tgt != nullptr);
    T tgt_res;
    tgt(x.data(), &tgt_res, x.size());
    ExpectEQ<T>(&tgt_res, &ref_res, 1);
  }
};

T
tensor-tang 已提交
160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182
template <typename T>
struct TestFuncWithRefer<jit::XYNTuples<T>, std::vector<T>, std::vector<T>> {
  void operator()(const typename jit::XYNTuples<T>::func_type tgt,
                  const std::vector<T>& x, const std::vector<T>& yref) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(yref.size(), x.size());
    const T* x_data = x.data();
    const T* yref_data = yref.data();
    const int d = yref.size();
    std::vector<T> ytgt(d);
    T* ytgt_data = ytgt.data();
    // test normal
    tgt(x_data, ytgt_data, d);
    ExpectEQ<T>(ytgt_data, yref_data, d);
    // test inplace x
    std::copy(x.begin(), x.end(), ytgt.begin());
    tgt(ytgt_data, ytgt_data, d);
    ExpectEQ<T>(ytgt_data, yref_data, d);
  }
};

template <typename T>
struct TestFuncWithRefer<jit::LSTMTuples<T>, std::vector<T>, std::vector<T>,
183 184
                         std::vector<T>, std::vector<T>, std::vector<T>,
                         typename jit::LSTMTuples<T>::attr_type> {
T
tensor-tang 已提交
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
  void operator()(const typename jit::LSTMTuples<T>::func_type tgt,
                  const std::vector<T>& xsrc, const std::vector<T>& wp,
                  const std::vector<T>& ct_1, const std::vector<T>& ct_ref,
                  const std::vector<T>& ht_ref,
                  const typename jit::LSTMTuples<T>::attr_type& attr) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(ct_ref.size(), ht_ref.size());
    EXPECT_EQ(ct_1.size(), ht_ref.size());
    EXPECT_EQ(xsrc.size(), 4 * ht_ref.size());
    EXPECT_EQ(wp.size(), 3 * ht_ref.size());

    // x could be changed after compute, so copy to save src
    int d = ht_ref.size();
    std::vector<T> x(xsrc.size()), ct(ct_ref.size()), ht(ht_ref.size());
    std::vector<T> checked(2 * d);
    std::copy(xsrc.begin(), xsrc.end(), x.begin());

    const T* ct_1_data = ct_1.data();
    const T* wp_data = wp.data();
    const T* ct_ref_data = ct_ref.data();
    const T* ht_ref_data = ht_ref.data();
    T* x_data = x.data();
    T* ct_data = ct.data();
    T* ht_data = ht.data();
    T* checked_data = checked.data();

211
    jit::lstm_t step;
T
tensor-tang 已提交
212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228
    step.gates = x_data;
    step.ct_1 = ct_1_data;
    step.ct = ct_data;
    step.ht = ht_data;
    if (attr.use_peephole) {
      step.wp = wp_data;
      step.checked = checked_data;
    }

    tgt(&step, &attr);
    ExpectEQ<T>(ct_data, ct_ref_data, d);
    ExpectEQ<T>(ht_data, ht_ref_data, d);
  }
};

template <typename T>
struct TestFuncWithRefer<jit::GRUTuples<T>, std::vector<T>, std::vector<T>,
229 230
                         std::vector<T>,
                         typename jit::GRUTuples<T>::attr_type> {
T
tensor-tang 已提交
231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246
  void operator()(const typename jit::GRUTuples<T>::func_type tgt,
                  const std::vector<T>& xsrc, const std::vector<T>& ht_1,
                  const std::vector<T>& ht_ref,
                  const typename jit::GRUTuples<T>::attr_type& attr) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(ht_1.size(), ht_ref.size());
    EXPECT_EQ(xsrc.size(), 3 * ht_ref.size());

    // x could be changed after compute, so copy to save src
    int d = ht_ref.size();
    std::vector<T> x(xsrc.size()), ht(ht_ref.size());
    std::copy(xsrc.begin(), xsrc.end(), x.begin());
    const T* ht_1_data = ht_1.data();
    const T* ht_ref_data = ht_ref.data();
    T* x_data = x.data();
    T* ht_data = ht.data();
247
    jit::gru_t step;
T
tensor-tang 已提交
248 249 250 251 252 253 254 255
    step.gates = x_data;
    step.ht_1 = ht_1_data;
    step.ht = ht_data;
    tgt(&step, &attr);
    ExpectEQ<T>(ht_data, ht_ref_data, d);
  }
};

256
template <typename T>
257 258
struct TestFuncWithRefer<jit::SeqPoolTuples<T>, std::vector<T>, std::vector<T>,
                         typename jit::SeqPoolTuples<T>::attr_type> {
259 260 261 262
  void operator()(const typename jit::SeqPoolTuples<T>::func_type tgt,
                  const std::vector<T>& x, const std::vector<T>& yref,
                  const typename jit::SeqPoolTuples<T>::attr_type& attr) {
    EXPECT_TRUE(tgt != nullptr);
263
    EXPECT_EQ(x.size() % yref.size(), static_cast<size_t>(0));
264 265 266 267 268 269 270 271 272 273
    int w = yref.size();
    std::vector<T> y(w);
    const T* x_data = x.data();
    const T* yref_data = yref.data();
    T* y_data = y.data();
    tgt(x_data, y_data, &attr);
    ExpectEQ<T>(y_data, yref_data, w);
  }
};

274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299
template <typename T>
struct TestFuncWithRefer<jit::EmbSeqPoolTuples<T>, std::vector<T>,
                         std::vector<int64_t>, std::vector<T>,
                         typename jit::EmbSeqPoolTuples<T>::attr_type> {
  void operator()(const typename jit::EmbSeqPoolTuples<T>::func_type tgt,
                  const std::vector<T>& table, const std::vector<int64_t>& idx,
                  const std::vector<T>& oref,
                  const typename jit::EmbSeqPoolTuples<T>::attr_type& attr) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(table.size(),
              static_cast<size_t>(attr.table_height * attr.table_width));
    EXPECT_EQ(idx.size(),
              static_cast<size_t>(attr.index_height * attr.index_width));
    EXPECT_EQ(oref.size(),
              static_cast<size_t>(attr.table_width * attr.index_width));
    const T* table_data = table.data();
    const int64_t* idx_data = idx.data();
    const T* oref_data = oref.data();
    int o_w = oref.size();
    std::vector<T> out(o_w);
    T* o_data = out.data();
    tgt(table_data, idx_data, o_data, &attr);
    ExpectEQ<T>(o_data, oref_data, o_w);
  }
};

300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338
template <typename T>
struct TestFuncWithRefer<jit::SgdTuples<T>, T, std::vector<T>, std::vector<T>,
                         std::vector<int64_t>, std::vector<T>,
                         typename jit::SgdTuples<T>::attr_type> {
  void operator()(const typename jit::SgdTuples<T>::func_type tgt, const T lr,
                  const std::vector<T>& param, const std::vector<T>& grad,
                  const std::vector<int64_t>& rows, const std::vector<T>& oref,
                  const typename jit::SgdTuples<T>::attr_type& attr) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(param.size(),
              static_cast<size_t>(attr.param_height * attr.param_width));
    EXPECT_EQ(grad.size(),
              static_cast<size_t>(attr.grad_height * attr.grad_width));
    EXPECT_EQ(rows.size(), static_cast<size_t>(attr.selected_rows_size));
    EXPECT_EQ(param.size(), oref.size());
    const T* param_data = param.data();
    const T* grad_data = grad.data();
    const int64_t* rows_data = rows.data();
    const T* oref_data = oref.data();

    std::vector<T> out(oref.size());
    T* o_data = out.data();
    tgt(&lr, param_data, grad_data, rows_data, o_data, &attr);
    // only the selected rows should be equal
    for (size_t i = 0; i < rows.size(); ++i) {
      ExpectEQ<T>(o_data + rows[i] * attr.grad_width,
                  oref_data + rows[i] * attr.grad_width, attr.grad_width);
    }

    // inplace
    std::copy(param.begin(), param.end(), out.begin());
    tgt(&lr, o_data, grad_data, rows_data, o_data, &attr);
    for (size_t i = 0; i < rows.size(); ++i) {
      ExpectEQ<T>(o_data + rows[i] * attr.grad_width,
                  oref_data + rows[i] * attr.grad_width, attr.grad_width);
    }
  }
};

T
tensor-tang 已提交
339
template <typename T>
340
struct TestFuncWithRefer<jit::MatMulTuples<T>, std::vector<T>, std::vector<T>,
341 342
                         std::vector<T>,
                         typename jit::MatMulTuples<T>::attr_type> {
T
tensor-tang 已提交
343 344
  void operator()(const typename jit::MatMulTuples<T>::func_type tgt,
                  const std::vector<T>& a, const std::vector<T>& b,
345 346
                  const std::vector<T>& cref,
                  const typename jit::MatMulTuples<T>::attr_type& attr) {
T
tensor-tang 已提交
347
    EXPECT_TRUE(tgt != nullptr);
348 349 350
    EXPECT_EQ(a.size(), static_cast<size_t>(attr.m * attr.k));
    EXPECT_EQ(b.size(), static_cast<size_t>(attr.k * attr.n));
    EXPECT_EQ(cref.size(), static_cast<size_t>(attr.m * attr.n));
T
tensor-tang 已提交
351 352 353 354 355
    std::vector<T> c(cref.size());
    const T* a_data = a.data();
    const T* b_data = b.data();
    const T* cref_data = cref.data();
    T* c_data = c.data();
356 357
    tgt(a_data, b_data, c_data, &attr);
    ExpectEQ<T>(c_data, cref_data, attr.m * attr.n);
T
tensor-tang 已提交
358 359 360
  }
};

361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417
template <typename T>
struct TestFuncWithRefer<jit::LayerNormTuples<T>, std::vector<T>,
                         std::vector<T>, std::vector<T>, std::vector<T>,
                         std::vector<T>, std::vector<T>, int, float, int> {
  void operator()(const typename jit::LayerNormTuples<T>::func_type tgt,
                  std::vector<T>& x, std::vector<T>& outref,  // NOLINT
                  std::vector<T>& mean, std::vector<T>& var,  // NOLINT
                  const std::vector<T>& scale, const std::vector<T>& bias,
                  int left, const float epsilon, int right) {
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(x.size(), static_cast<size_t>(left * right));
    EXPECT_EQ(outref.size(), static_cast<size_t>(left * right));
    EXPECT_EQ(mean.size(), static_cast<size_t>(left));
    EXPECT_EQ(var.size(), static_cast<size_t>(left));
    EXPECT_EQ(scale.size(), static_cast<size_t>(right));
    EXPECT_EQ(bias.size(), static_cast<size_t>(right));
    std::vector<T> outtgt(outref.size());
    const T* scale_data = scale.data();
    const T* bias_data = bias.data();
    T* x_data = x.data();
    T* mean_data = mean.data();
    T* var_data = var.data();
    T* outref_data = outref.data();
    T* outtgt_data = outtgt.data();

    tgt(x_data, outtgt_data, mean_data, var_data, scale_data, bias_data, left,
        epsilon, right);
    ExpectEQ<T>(outtgt_data, outref_data, left * right);
  }
};

template <typename T>
struct TestFuncWithRefer<jit::CRFDecodingTuples<T>, int, std::vector<T>,
                         std::vector<T>, std::vector<T>, std::vector<int>,
                         int> {
  void operator()(const typename jit::CRFDecodingTuples<T>::func_type tgt,
                  const int seq_len, const std::vector<T>& x,
                  const std::vector<T>& w, std::vector<T>& alpharef,  // NOLINT
                  std::vector<int>& trackref, int tag_num) {          // NOLINT
    constexpr int state_trans_base_idx = 2;
    EXPECT_TRUE(tgt != nullptr);
    EXPECT_EQ(x.size(), static_cast<size_t>(seq_len * tag_num));
    EXPECT_EQ(w.size(),
              static_cast<size_t>((tag_num + state_trans_base_idx) * tag_num));
    EXPECT_EQ(alpharef.size(), static_cast<size_t>(seq_len * tag_num));
    EXPECT_EQ(trackref.size(), static_cast<size_t>(seq_len * tag_num));
    std::vector<T> alphatgt(alpharef.size());
    std::vector<int> tracktgt(trackref.size());

    memcpy(trackref.data(), tracktgt.data(), tag_num * sizeof(int));
    tgt(seq_len, (const T*)x.data(), (const T*)w.data(), alphatgt.data(),
        tracktgt.data(), tag_num);
    ExpectEQ<T>(alpharef.data(), alphatgt.data(), seq_len * tag_num);
    ExpectEQ<int>(trackref.data(), tracktgt.data(), seq_len * tag_num);
  }
};

418 419
template <jit::KernelType KT, typename KernelTuples, typename PlaceType,
          typename... Args>
T
tensor-tang 已提交
420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
void TestAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
  TestFuncWithRefer<KernelTuples, Args...> test;
  // test jitcode
  auto jitcode = jit::GetJitCode<KT, KernelTuples, PlaceType>(attr);
  if (jitcode) {
    VLOG(10) << "Test Jitcode Kernel ";
    test(jitcode, args...);
  }
  // test all impls in more
  jit::KernelKey kkey(KT, PlaceType());
  auto& pool = jit::KernelPool().Instance().AllKernels();
  auto iter = pool.find(kkey);
  if (iter != pool.end()) {
    auto& impls = iter->second;
    for (auto& impl : impls) {
T
tensor-tang 已提交
435
      auto i = dynamic_cast<const jit::KernelMore<KernelTuples>*>(impl.get());
T
tensor-tang 已提交
436 437
      if (i && i->UseMe(attr)) {
        auto more = i->GetFunc();
T
tensor-tang 已提交
438
        VLOG(10) << "Test More Kernel : " << i->ImplType();
T
tensor-tang 已提交
439 440 441 442 443
        test(more, args...);
      }
    }
  }
  // test result from Get function
T
tensor-tang 已提交
444
  // VLOG(10) << "Test Get function ";
T
tensor-tang 已提交
445 446
  auto tgt = jit::Get<KT, KernelTuples, PlaceType>(attr);
  test(tgt, args...);
T
tensor-tang 已提交
447 448
}

449
template <jit::KernelType KT, typename T, typename PlaceType>
450
void TestXYZNKernel() {
451
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
T
tensor-tang 已提交
452
  for (int d : TestSizes()) {
453
    auto ref = jit::GetRefer<KT, jit::XYZNTuples<T>>();
T
tensor-tang 已提交
454 455
    EXPECT_TRUE(ref != nullptr);

T
tensor-tang 已提交
456
    std::vector<T> x(d), y(d), zref(d);
T
tensor-tang 已提交
457 458 459
    RandomVec<T>(d, x.data());
    RandomVec<T>(d, y.data());

T
tensor-tang 已提交
460 461 462 463 464 465 466 467 468 469 470
    std::vector<T> xinp(d), yinp(d);  // inplace test
    std::copy(x.begin(), x.end(), xinp.begin());
    std::copy(y.begin(), y.end(), yinp.begin());

    const T* x_data = x.data();
    const T* y_data = y.data();
    T* zref_data = zref.data();
    T* xinp_data = xinp.data();
    T* yinp_data = yinp.data();

    // test refer code inplace
T
tensor-tang 已提交
471
    ref(x_data, y_data, zref_data, d);
T
tensor-tang 已提交
472 473 474 475 476
    ref(x_data, yinp_data, yinp_data, d);
    ref(xinp_data, y_data, xinp_data, d);
    ExpectEQ<T>(xinp_data, zref_data, d);
    ExpectEQ<T>(yinp_data, zref_data, d);

T
tensor-tang 已提交
477 478
    TestAllImpls<KT, jit::XYZNTuples<T>, PlaceType, std::vector<T>,
                 std::vector<T>, std::vector<T>>(d, x, y, zref);
T
tensor-tang 已提交
479 480
  }
}
T
tensor-tang 已提交
481

482
template <jit::KernelType KT, typename T, typename PlaceType>
483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502
void TestAXYNKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  for (int d : TestSizes()) {
    auto ref = jit::GetRefer<KT, jit::AXYNTuples<T>>();
    EXPECT_TRUE(ref != nullptr);

    const T a = static_cast<T>(3);
    std::vector<T> x(d), yref(d);
    std::vector<T> xinp(d);  // inplace test
    RandomVec<T>(d, x.data());
    std::copy(x.begin(), x.end(), xinp.begin());

    const T* x_data = x.data();
    T* yref_data = yref.data();
    T* xinp_data = xinp.data();
    // test refer code inplace
    ref(&a, x_data, yref_data, d);
    ref(&a, xinp_data, xinp_data, d);
    ExpectEQ<T>(xinp_data, yref_data, d);

T
tensor-tang 已提交
503 504
    TestAllImpls<KT, jit::AXYNTuples<T>, PlaceType, T, std::vector<T>,
                 std::vector<T>>(d, a, x, yref);
505 506 507
  }
}

508 509 510
template <jit::KernelType KT, typename T, typename PlaceType>
void TestXRNKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
511 512
  auto last_acc = FLAGS_acc;
  FLAGS_acc = 1e-4;
513 514 515 516
  for (int d : TestSizes()) {
    auto ref = jit::GetRefer<KT, jit::XRNTuples<T>>();
    EXPECT_TRUE(ref != nullptr);
    std::vector<T> x(d);
T
tensor-tang 已提交
517
    RandomVec<T>(d, x.data(), -2.f, 2.f);
518 519 520 521 522
    T ref_res;
    ref(x.data(), &ref_res, d);
    TestAllImpls<KT, jit::XRNTuples<T>, PlaceType, std::vector<T>, T>(d, x,
                                                                      ref_res);
  }
523
  FLAGS_acc = last_acc;
524 525 526
}

template <jit::KernelType KT, typename T, typename PlaceType>
527 528 529 530 531 532 533 534
void TestXYNKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  for (int d : TestSizes()) {
    auto ref = jit::GetRefer<KT, jit::XYNTuples<T>>();
    EXPECT_TRUE(ref != nullptr);

    std::vector<T> x(d), yref(d);
    std::vector<T> xinp(d);  // inplace test
535
    RandomVec<T>(d, x.data(), -2.f, 2.f);
536 537 538 539 540 541 542 543 544 545
    std::copy(x.begin(), x.end(), xinp.begin());

    const T* x_data = x.data();
    T* yref_data = yref.data();
    T* xinp_data = xinp.data();
    // test refer code inplace
    ref(x_data, yref_data, d);
    ref(xinp_data, xinp_data, d);
    ExpectEQ<T>(xinp_data, yref_data, d);

T
tensor-tang 已提交
546 547
    TestAllImpls<KT, jit::XYNTuples<T>, PlaceType, std::vector<T>,
                 std::vector<T>>(d, x, yref);
548 549 550
  }
}

551
template <jit::KernelType KT, typename T, typename PlaceType>
T
tensor-tang 已提交
552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567
void TestLSTMKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  std::vector<std::string> all_acts = {"sigmoid", "tanh", "relu", "identity"};
  for (int d : TestSizes()) {
    for (bool use_peephole : {true, false}) {
      for (auto& act_gate : all_acts) {
        for (auto& act_cand : all_acts) {
          for (auto& act_cell : all_acts) {
            const jit::lstm_attr_t attr(
                d, jit::to_kerneltype(act_gate), jit::to_kerneltype(act_cand),
                jit::to_kerneltype(act_cell), use_peephole);
            auto ref = jit::GetRefer<KT, jit::LSTMTuples<T>>();
            EXPECT_TRUE(ref != nullptr);
            std::vector<T> xsrc(4 * d), wp(3 * d), ct_1(d);
            std::vector<T> ct_ref(d), ht_ref(d), checked(2 * d);
            RandomVec<T>(4 * d, xsrc.data(), -2.f, 2.f);
568 569
            RandomVec<T>(3 * d, wp.data(), -1.f, 1.f);
            RandomVec<T>(d, ct_1.data(), -1.f, 1.f);
T
tensor-tang 已提交
570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588
            // x could be changed after compute, so copy to save src
            std::vector<T> x(xsrc.size());
            std::copy(xsrc.begin(), xsrc.end(), x.begin());
            const T* ct_1_data = ct_1.data();
            const T* wp_data = wp.data();
            T* x_data = x.data();
            T* checked_data = checked.data();
            T* ct_ref_data = ct_ref.data();
            T* ht_ref_data = ht_ref.data();
            jit::lstm_t step;
            step.gates = x_data;
            step.ct_1 = ct_1_data;
            step.ct = ct_ref_data;
            step.ht = ht_ref_data;
            if (use_peephole) {
              step.wp = wp_data;
              step.checked = checked_data;
            }
            ref(&step, &attr);
T
tensor-tang 已提交
589
            VLOG(10) << attr;
T
tensor-tang 已提交
590 591 592 593
            TestAllImpls<KT, jit::LSTMTuples<T>, PlaceType, std::vector<T>,
                         std::vector<T>, std::vector<T>, std::vector<T>,
                         std::vector<T>>(attr, xsrc, wp, ct_1, ct_ref, ht_ref,
                                         attr);
T
tensor-tang 已提交
594 595 596 597 598 599 600
          }
        }
      }
    }
  }
}

601
template <jit::KernelType KT, typename T, typename PlaceType>
602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625
void TestGRUKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  std::vector<std::string> all_acts = {"sigmoid", "tanh", "relu", "identity"};
  for (int d : TestSizes()) {
    for (auto& act_gate : all_acts) {
      for (auto& act_cand : all_acts) {
        const jit::gru_attr_t attr(d, jit::to_kerneltype(act_gate),
                                   jit::to_kerneltype(act_cand));
        auto ref = jit::GetRefer<KT, jit::GRUTuples<T>>();
        EXPECT_TRUE(ref != nullptr);
        std::vector<T> xsrc(3 * d), ht_1(d), ht_ref(d);
        RandomVec<T>(3 * d, xsrc.data(), -2.f, 2.f);
        RandomVec<T>(d, ht_1.data(), -2.f, 2.f);
        // x could be changed after compute, so copy to save src
        std::vector<T> x(xsrc.size());
        std::copy(xsrc.begin(), xsrc.end(), x.begin());
        const T* ht_1_data = ht_1.data();
        T* x_data = x.data();
        T* ht_ref_data = ht_ref.data();
        jit::gru_t step;
        step.gates = x_data;
        step.ht_1 = ht_1_data;
        step.ht = ht_ref_data;
        ref(&step, &attr);
T
tensor-tang 已提交
626
        VLOG(10) << attr;
T
tensor-tang 已提交
627 628 629
        TestAllImpls<KT, jit::GRUTuples<T>, PlaceType, std::vector<T>,
                     std::vector<T>, std::vector<T>>(attr, xsrc, ht_1, ht_ref,
                                                     attr);
630 631 632 633 634
      }
    }
  }
}

635
template <jit::KernelType KT, typename T, typename PlaceType>
636 637
void TestSeqPoolKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
638 639
  std::vector<jit::SeqPoolType> pool_types = {
      jit::SeqPoolType::kSum, jit::SeqPoolType::kAvg, jit::SeqPoolType::kSqrt};
640
  for (auto type : pool_types) {
T
tensor-tang 已提交
641 642 643 644
    for (int w : TestSizes()) {
      jit::seq_pool_attr_t attr(w, type);
      for (int h : TestSizes()) {
        attr.h = h;
645 646 647 648 649 650 651 652 653 654 655 656 657 658 659
        auto ref = jit::GetRefer<KT, jit::SeqPoolTuples<T>>();
        EXPECT_TRUE(ref != nullptr);
        std::vector<T> x(h * w), yref(w);
        RandomVec<T>(h * w, x.data(), -2.f, 2.f);
        const T* x_data = x.data();
        T* yref_data = yref.data();
        ref(x_data, yref_data, &attr);
        VLOG(10) << attr;
        TestAllImpls<KT, jit::SeqPoolTuples<T>, PlaceType, std::vector<T>,
                     std::vector<T>>(attr, x, yref, attr);
      }
    }
  }
}

660
template <jit::KernelType KT, typename T, typename PlaceType>
T
tensor-tang 已提交
661 662
void TestMatMulKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
663 664 665 666
  auto last_acc = FLAGS_acc;
  // TODO(intel): fix MKL acc issue
  // https://github.com/PaddlePaddle/Paddle/issues/15447
  FLAGS_acc = 1e-3;
T
tensor-tang 已提交
667 668 669 670 671 672
  for (int m : {1, 2, 3, 4}) {
    for (int n : {1, 2, 3, 4}) {
      for (int k : TestSizes()) {
        auto ref = jit::GetRefer<KT, jit::MatMulTuples<T>>();
        EXPECT_TRUE(ref != nullptr);
        std::vector<T> a(m * k), b(k * n), c(m * n);
673 674
        RandomVec<T>(m * k, a.data(), -2.f, 2.f);
        RandomVec<T>(k * n, b.data(), -2.f, 2.f);
T
tensor-tang 已提交
675 676 677
        const T* a_data = a.data();
        const T* b_data = b.data();
        T* c_data = c.data();
678 679
        const jit::matmul_attr_t attr{m, n, k};
        ref(a_data, b_data, c_data, &attr);
T
tensor-tang 已提交
680
        TestAllImpls<KT, jit::MatMulTuples<T>, PlaceType, std::vector<T>,
681
                     std::vector<T>, std::vector<T>>(attr, a, b, c, attr);
T
tensor-tang 已提交
682 683 684
      }
    }
  }
685
  FLAGS_acc = last_acc;
T
tensor-tang 已提交
686 687
}

688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712
template <jit::KernelType KT, typename T, typename PlaceType>
void TestSoftmaxKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  for (int bs : {1, 2, 10}) {
    for (int n : TestSizes()) {
      auto ref = jit::GetRefer<KT, jit::SoftmaxTuples<T>>();
      EXPECT_TRUE(ref != nullptr);
      std::vector<T> x(bs * n), y(bs * n);
      RandomVec<T>(bs * n, x.data(), -2.f, 2.f);
      const T* x_data = x.data();
      T* y_data = y.data();

      std::vector<T> xinp(x.size());  // inplace test
      std::copy(x.begin(), x.end(), xinp.begin());
      ref(x_data, y_data, n, bs);
      T* xinp_data = xinp.data();
      ref(xinp_data, xinp_data, n, bs);
      ExpectEQ<T>(xinp_data, y_data, n * bs);

      TestAllImpls<KT, jit::SoftmaxTuples<T>, PlaceType, std::vector<T>,
                   std::vector<T>>(n, x, y, n, bs);
    }
  }
}

713 714 715 716 717 718 719 720 721 722 723 724
template <jit::KernelType KT, typename T, typename PlaceType>
void TestEmbSeqPoolKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  int64_t tbl_h = 1e4;
  std::vector<jit::SeqPoolType> pool_types = {
      jit::SeqPoolType::kSum};  // only support sum yet
  for (int tbl_w : TestSizes()) {
    std::vector<T> table(tbl_h * tbl_w);
    RandomVec<T>(tbl_h * tbl_w, table.data(), -2.f, 2.f);
    const T* table_data = table.data();
    for (auto type : pool_types) {
      for (int idx_w : {1, 2, 10, 16}) {
725
        for (int idx_h : {1, 2, 9, 13, 16}) {
726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
          auto ref = jit::GetRefer<KT, jit::EmbSeqPoolTuples<T>>();
          EXPECT_TRUE(ref != nullptr);
          std::vector<int64_t> idx(idx_h * idx_w);
          RandomVec<int64_t>(idx_h * idx_w, idx.data(), 0, tbl_h - 1);
          int64_t out_w = tbl_w * idx_w;
          std::vector<T> oref(out_w);
          const int64_t* idx_data = idx.data();
          T* o_data = oref.data();
          jit::emb_seq_pool_attr_t attr(tbl_h, tbl_w, idx_h, idx_w, out_w,
                                        type);
          ref(table_data, idx_data, o_data, &attr);

          TestAllImpls<KT, jit::EmbSeqPoolTuples<T>, PlaceType, std::vector<T>,
                       std::vector<int64_t>, std::vector<T>>(attr, table, idx,
                                                             oref, attr);
        }
      }
    }
  }
}

747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
template <jit::KernelType KT, typename T, typename PlaceType>
void TestSgdKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  const T lr = 0.1;
  auto UnDuplicatedRandomVec = [](int n, const int64_t lower,
                                  const int64_t upper) -> std::vector<int64_t> {
    PADDLE_ENFORCE_LE(static_cast<size_t>(upper - lower), n - 1);
    PADDLE_ENFORCE_GT(n, 0);
    std::vector<int64_t> all, out;
    for (int i = 0; i < n; ++i) {
      all.push_back(i);
    }
    std::random_shuffle(all.begin(), all.end());
    out.insert(out.begin(), all.begin(), all.begin() + n);
    return out;
  };
  for (int param_h : {1, 10}) {
    for (int grad_w : TestSizes()) {
      std::vector<T> param(param_h * grad_w);
      std::vector<T> param_out(param_h * grad_w);
      RandomVec<T>(param_h * grad_w, param.data(), -2.f, 2.f);
      const T* param_data = param.data();
      T* out_data = param_out.data();
      for (int rows_size = 1; rows_size <= param_h; ++rows_size) {
        std::vector<T> grad(rows_size * grad_w);
        std::vector<int64_t> rows =
            UnDuplicatedRandomVec(rows_size, 0, rows_size - 1);
        RandomVec<T>(rows_size * grad_w, grad.data(), -2.f, 2.f);
        const int64_t* rows_data = rows.data();
        const T* grad_data = grad.data();
        auto ref = jit::GetRefer<KT, jit::SgdTuples<T>>();
        EXPECT_TRUE(ref != nullptr);
        jit::sgd_attr_t attr(param_h, grad_w, rows_size, grad_w, rows_size);
        ref(&lr, param_data, grad_data, rows_data, out_data, &attr);

        // inplace test
        std::vector<T> inp(param.size());
        std::copy(param.begin(), param.end(), inp.begin());
        T* inp_data = inp.data();
        ref(&lr, inp_data, grad_data, rows_data, inp_data, &attr);
        // only the selected rows should be equal
        for (int i = 0; i < rows_size; ++i) {
          ExpectEQ<T>(inp_data + rows[i] * grad_w, out_data + rows[i] * grad_w,
                      grad_w);
        }

        TestAllImpls<KT, jit::SgdTuples<T>, PlaceType, T, std::vector<T>,
                     std::vector<T>, std::vector<int64_t>, std::vector<T>>(
            attr, lr, param, grad, rows, param_out, attr);
      }
    }
  }
}

801
template <jit::KernelType KT, typename T, typename PlaceType>
T
tensor-tang 已提交
802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853
void TestNCHW16CMulNCKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  const int n = 3, c = 16 * 4, h = 10, w = 10;
  auto ref = jit::GetRefer<KT, jit::NCHW16CMulNCTuples<T>>();
  EXPECT_TRUE(ref != nullptr);
  int sz = n * c * h * w;
  std::vector<T> x(sz), y(n * c), zref(sz);
  std::vector<T> ztgt(sz), zjit(sz);
  RandomVec<T>(sz, x.data(), -2.f, 2.f);
  RandomVec<T>(n * c, y.data(), -2.f, 2.f);

  const T* x_data = x.data();
  const T* y_data = y.data();
  T* zref_data = zref.data();
  T* ztgt_data = ztgt.data();
  T* zjit_data = zjit.data();
  constexpr int simd_width = ZMM_FLOAT_BLOCK;
  int C = c / simd_width;
  auto tgt = jit::Get<KT, jit::NCHW16CMulNCTuples<T>, PlaceType>(0);
  auto jitcode = jit::GetJitCode<KT, jit::NCHW16CMulNCTuples<T>, PlaceType>(0);
  EXPECT_TRUE(tgt != nullptr);

  if (std::is_same<T, float>::value &&
      paddle::platform::MayIUse(paddle::platform::avx512f)) {
    EXPECT_TRUE(jitcode != nullptr);
  }
  for (int ni = 0; ni < n; ni++) {
    for (int ci = 0; ci < C; ci++) {
      auto ptr_x =
          x_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
      auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
      auto ptr_zref =
          zref_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
      auto ptr_ztgt =
          ztgt_data + ni * C * h * w * simd_width + ci * h * w * simd_width;

      ref(ptr_x, ptr_y, ptr_zref, h, w);
      tgt(ptr_x, ptr_y, ptr_ztgt, h, w);

      if (jitcode) {
        auto ptr_zjit =
            zjit_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
        jitcode(ptr_x, ptr_y, ptr_zjit, h, w);
      }
    }
  }
  ExpectEQ<T>(ztgt_data, zref_data, sz);
  if (jitcode) {
    ExpectEQ<T>(zjit_data, zref_data, sz);
  }
}

854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestLayerNormKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  const T epsilon = 9.99999975e-06;
  for (int n : {1, 2, 10}) {
    for (int x_dim_0 : {1, 9, 17, 50}) {
      int left = n * x_dim_0;
      for (int x_dim_1 : TestSizes()) {
        int right = x_dim_1;
        auto ref = jit::GetRefer<KT, jit::LayerNormTuples<T>>();
        EXPECT_TRUE(ref != nullptr);
        int sz = left * right;
        std::vector<T> x(sz), mean(left), var(left), scale(right), bias(right),
            outref(sz);
        RandomVec<T>(sz, x.data(), -2.f, 2.f);
        RandomVec<T>(left, mean.data(), -2.f, 2.f);
        RandomVec<T>(left, var.data(), -2.f, 2.f);
        RandomVec<T>(right, scale.data(), -2.f, 2.f);
        RandomVec<T>(right, bias.data(), -2.f, 2.f);

        const T* scale_data = scale.data();
        const T* bias_data = bias.data();
        T* x_data = x.data();
        T* mean_data = mean.data();
        T* var_data = var.data();
        T* outref_data = outref.data();

        ref(x_data, outref_data, mean_data, var_data, scale_data, bias_data,
            left, epsilon, right);

        TestAllImpls<KT, jit::LayerNormTuples<T>, PlaceType, std::vector<T>,
                     std::vector<T>, std::vector<T>, std::vector<T>,
                     std::vector<T>, std::vector<T>, int, float>(
            right, x, outref, mean, var, scale, bias, left, epsilon, right);
      }
    }
  }
}

template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestCRFDecodingKernel() {
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
  constexpr int state_trans_base_idx = 2;
  for (int seq_len : {1, 11, 17, 50}) {
    for (int tag_num : TestSizes()) {
      auto ref = jit::GetRefer<KT, jit::CRFDecodingTuples<T>>();
      EXPECT_TRUE(ref != nullptr);
      int x_sz = seq_len * tag_num;
      int w_sz = (tag_num + state_trans_base_idx) * tag_num;
      std::vector<T> x(x_sz), w(w_sz), alpharef(x_sz);
      std::vector<int> trackref(x_sz);
      RandomVec<T>(x_sz, x.data(), -2.f, 2.f);
      RandomVec<T>(w_sz, w.data(), -2.f, 2.f);

      ref(seq_len, (const T*)x.data(), (const T*)w.data(), alpharef.data(),
          trackref.data(), tag_num);

      TestAllImpls<KT, jit::CRFDecodingTuples<T>, PlaceType, int,
                   std::vector<T>, std::vector<T>, std::vector<T>,
                   std::vector<int>, int>(tag_num, seq_len, x, w, alpharef,
                                          trackref, tag_num);
    }
  }
}

T
tensor-tang 已提交
919
// XYZNTuple
T
tensor-tang 已提交
920
TEST(JITKernel, kVMul) {
921 922
  TestXYZNKernel<jit::kVMul, float, CPUPlace>();
  TestXYZNKernel<jit::kVMul, double, CPUPlace>();
T
tensor-tang 已提交
923 924
}

T
tensor-tang 已提交
925
TEST(JITKernel, kVAdd) {
926 927
  TestXYZNKernel<jit::kVAdd, float, CPUPlace>();
  TestXYZNKernel<jit::kVAdd, double, CPUPlace>();
T
tensor-tang 已提交
928 929
}

T
tensor-tang 已提交
930
TEST(JITKernel, kVAddRelu) {
931 932
  TestXYZNKernel<jit::kVAddRelu, float, CPUPlace>();
  TestXYZNKernel<jit::kVAddRelu, double, CPUPlace>();
T
tensor-tang 已提交
933 934
}

T
tensor-tang 已提交
935
TEST(JITKernel, kVSub) {
936 937
  TestXYZNKernel<jit::kVSub, float, CPUPlace>();
  TestXYZNKernel<jit::kVSub, double, CPUPlace>();
T
tensor-tang 已提交
938 939 940
}

// AXYNTuples
T
tensor-tang 已提交
941
TEST(JITKernel, kVScal) {
942 943
  TestAXYNKernel<jit::kVScal, float, CPUPlace>();
  TestAXYNKernel<jit::kVScal, double, CPUPlace>();
T
tensor-tang 已提交
944 945
}

T
tensor-tang 已提交
946
TEST(JITKernel, kVAddBias) {
947 948 949 950 951 952 953 954 955 956 957 958 959
  TestAXYNKernel<jit::kVAddBias, float, CPUPlace>();
  TestAXYNKernel<jit::kVAddBias, double, CPUPlace>();
}

// XRNTuples
TEST(JITKernel, kHMax) {
  TestXRNKernel<jit::kHMax, float, CPUPlace>();
  TestXRNKernel<jit::kHMax, double, CPUPlace>();
}

TEST(JITKernel, kHSum) {
  TestXRNKernel<jit::kHSum, float, CPUPlace>();
  TestXRNKernel<jit::kHSum, double, CPUPlace>();
T
tensor-tang 已提交
960 961 962
}

// XYNTuples
T
tensor-tang 已提交
963
TEST(JITKernel, kVRelu) {
964 965
  TestXYNKernel<jit::kVRelu, float, CPUPlace>();
  TestXYNKernel<jit::kVRelu, double, CPUPlace>();
T
tensor-tang 已提交
966 967
}

T
tensor-tang 已提交
968
TEST(JITKernel, kVIdentity) {
969 970
  TestXYNKernel<jit::kVIdentity, float, CPUPlace>();
  TestXYNKernel<jit::kVIdentity, double, CPUPlace>();
T
tensor-tang 已提交
971 972
}

T
tensor-tang 已提交
973
TEST(JITKernel, kVSquare) {
974 975
  TestXYNKernel<jit::kVSquare, float, CPUPlace>();
  TestXYNKernel<jit::kVSquare, double, CPUPlace>();
T
tensor-tang 已提交
976 977
}

T
tensor-tang 已提交
978
TEST(JITKernel, kVExp) {
979 980
  TestXYNKernel<jit::kVExp, float, CPUPlace>();
  TestXYNKernel<jit::kVExp, double, CPUPlace>();
T
tensor-tang 已提交
981 982
}

T
tensor-tang 已提交
983
TEST(JITKernel, kVSigmoid) {
984 985
  TestXYNKernel<jit::kVSigmoid, float, CPUPlace>();
  TestXYNKernel<jit::kVSigmoid, double, CPUPlace>();
T
tensor-tang 已提交
986 987
}

T
tensor-tang 已提交
988
TEST(JITKernel, kVTanh) {
989 990
  TestXYNKernel<jit::kVTanh, float, CPUPlace>();
  TestXYNKernel<jit::kVTanh, double, CPUPlace>();
T
tensor-tang 已提交
991 992 993
}

// LSTM
T
tensor-tang 已提交
994
TEST(JITKernel, kLSTMCtHt) {
995 996
  TestLSTMKernel<jit::kLSTMCtHt, float, CPUPlace>();
  TestLSTMKernel<jit::kLSTMCtHt, double, CPUPlace>();
T
tensor-tang 已提交
997 998
}

T
tensor-tang 已提交
999
TEST(JITKernel, kLSTMC1H1) {
1000 1001
  TestLSTMKernel<jit::kLSTMC1H1, float, CPUPlace>();
  TestLSTMKernel<jit::kLSTMC1H1, double, CPUPlace>();
T
tensor-tang 已提交
1002 1003 1004
}

// GRU
T
tensor-tang 已提交
1005
TEST(JITKernel, kGRUH1) {
1006 1007
  TestGRUKernel<jit::kGRUH1, float, CPUPlace>();
  TestGRUKernel<jit::kGRUH1, double, CPUPlace>();
1008 1009
}

T
tensor-tang 已提交
1010
TEST(JITKernel, kGRUHtPart1) {
1011 1012
  TestGRUKernel<jit::kGRUHtPart1, float, CPUPlace>();
  TestGRUKernel<jit::kGRUHtPart1, double, CPUPlace>();
1013 1014
}

T
tensor-tang 已提交
1015
TEST(JITKernel, kGRUHtPart2) {
1016 1017
  TestGRUKernel<jit::kGRUHtPart2, float, CPUPlace>();
  TestGRUKernel<jit::kGRUHtPart2, double, CPUPlace>();
1018 1019
}

1020
TEST(JITKernel, kSeqPool) {
1021 1022
  TestSeqPoolKernel<jit::kSeqPool, float, CPUPlace>();
  TestSeqPoolKernel<jit::kSeqPool, double, CPUPlace>();
1023 1024
}

T
tensor-tang 已提交
1025
TEST(JITKernel, kMatMul) {
1026 1027 1028 1029 1030 1031 1032
  TestMatMulKernel<jit::kMatMul, float, CPUPlace>();
  TestMatMulKernel<jit::kMatMul, double, CPUPlace>();
}

TEST(JITKernel, kSoftmax) {
  TestSoftmaxKernel<jit::kSoftmax, float, CPUPlace>();
  TestSoftmaxKernel<jit::kSoftmax, double, CPUPlace>();
T
tensor-tang 已提交
1033 1034
}

1035 1036 1037 1038 1039
TEST(JITKernel, kEmbSeqPool) {
  TestEmbSeqPoolKernel<jit::kEmbSeqPool, float, CPUPlace>();
  TestEmbSeqPoolKernel<jit::kEmbSeqPool, double, CPUPlace>();
}

1040 1041 1042 1043 1044
TEST(JITKernel, kSgd) {
  TestSgdKernel<jit::kSgd, float, CPUPlace>();
  TestSgdKernel<jit::kSgd, double, CPUPlace>();
}

T
tensor-tang 已提交
1045
TEST(JITKernel, kNCHW16CMulNC) {
1046 1047
  TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, float, CPUPlace>();
  TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, double, CPUPlace>();
T
tensor-tang 已提交
1048 1049
}

1050 1051 1052 1053 1054 1055 1056 1057 1058 1059
TEST(JITKernel, kLayerNorm) {
  TestLayerNormKernel<jit::kLayerNorm, float, paddle::platform::CPUPlace>();
  TestLayerNormKernel<jit::kLayerNorm, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, kCRFDecoding) {
  TestCRFDecodingKernel<jit::kCRFDecoding, float, paddle::platform::CPUPlace>();
  TestCRFDecodingKernel<jit::kCRFDecoding, double,
                        paddle::platform::CPUPlace>();
}
T
tensor-tang 已提交
1060

1061 1062 1063
TEST(JITKernel, pool) {
  // TODO(TJ): add some test
}