test.cc 19.9 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
/* 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 <random>
#include <string>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
T
tensor-tang 已提交
21
#include "paddle/fluid/operators/jit/kernels.h"
T
tensor-tang 已提交
22
#include "paddle/fluid/platform/cpu_info.h"
T
tensor-tang 已提交
23
#include "paddle/fluid/platform/place.h"
T
tensor-tang 已提交
24

T
tensor-tang 已提交
25 26 27 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>
void ExpectEQ(const T* target, const T* refer, int n) {
  if (std::is_floating_point<T>::value) {
    for (int i = 0; i < n; ++i) {
40
      EXPECT_NEAR(target[i], refer[i], 1e-5);
T
tensor-tang 已提交
41 42 43 44 45 46 47 48
    }
  } else {
    for (int i = 0; i < n; ++i) {
      EXPECT_EQ(target[i], refer[i]);
    }
  }
}

T
tensor-tang 已提交
49 50
std::vector<int> TestSizes() {
  std::vector<int> s;
T
tensor-tang 已提交
51
  for (int i = 1; i < 32; ++i) {
T
tensor-tang 已提交
52 53
    s.push_back(i);
  }
T
tensor-tang 已提交
54 55 56 57
  // test some large size
  s.push_back(100);
  s.push_back(1000);
  s.push_back(2000);
T
tensor-tang 已提交
58 59 60
  return s;
}

T
tensor-tang 已提交
61 62 63 64 65 66 67 68 69 70 71
namespace jit = paddle::operators::jit;

template <typename KernelTuples, typename... Args>
struct TestFuncWithRefer {
  void operator()(const typename KernelTuples::func_type tgt, Args... args) {}
};

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,
72 73
                  const std::vector<T>& x, const std::vector<T>& y,
                  const std::vector<T>& zref) {
T
tensor-tang 已提交
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
    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);
  }
};

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>,
                         std::vector<T>, std::vector<T>, std::vector<T>> {
  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();

    paddle::operators::jit::lstm_t step;
    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>,
                         std::vector<T>> {
  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();
    paddle::operators::jit::gru_t step;
    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);
  }
};

template <paddle::operators::jit::KernelType KT, typename KernelTuples,
          typename PlaceType, typename... Args>
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
231
      auto i = dynamic_cast<const jit::KernelMore<KernelTuples>*>(impl.get());
T
tensor-tang 已提交
232 233
      if (i && i->UseMe(attr)) {
        auto more = i->GetFunc();
T
tensor-tang 已提交
234
        VLOG(10) << "Test More Kernel : " << i->ImplType();
T
tensor-tang 已提交
235 236 237 238 239
        test(more, args...);
      }
    }
  }
  // test result from Get function
T
tensor-tang 已提交
240
  // VLOG(10) << "Test Get function ";
T
tensor-tang 已提交
241 242
  auto tgt = jit::Get<KT, KernelTuples, PlaceType>(attr);
  test(tgt, args...);
T
tensor-tang 已提交
243 244
}

245 246
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestXYZNKernel() {
T
tensor-tang 已提交
247
  namespace jit = paddle::operators::jit;
248
  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
T
tensor-tang 已提交
249
  for (int d : TestSizes()) {
250
    auto ref = jit::GetRefer<KT, jit::XYZNTuples<T>>();
T
tensor-tang 已提交
251 252
    EXPECT_TRUE(ref != nullptr);

T
tensor-tang 已提交
253
    std::vector<T> x(d), y(d), zref(d);
T
tensor-tang 已提交
254 255 256
    RandomVec<T>(d, x.data());
    RandomVec<T>(d, y.data());

T
tensor-tang 已提交
257 258 259 260 261 262 263 264 265 266 267
    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
268
    ref(x_data, y_data, zref_data, d);
T
tensor-tang 已提交
269 270 271 272 273
    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
274 275
    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
276 277
  }
}
T
tensor-tang 已提交
278

279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestAXYNKernel() {
  namespace jit = paddle::operators::jit;
  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
301 302
    TestAllImpls<KT, jit::AXYNTuples<T>, PlaceType, T, std::vector<T>,
                 std::vector<T>>(d, a, x, yref);
303 304 305
  }
}

306 307 308 309 310 311 312 313 314 315
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestXYNKernel() {
  namespace jit = paddle::operators::jit;
  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
316
    RandomVec<T>(d, x.data(), -2.f, 2.f);
317 318 319 320 321 322 323 324 325 326
    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
327 328
    TestAllImpls<KT, jit::XYNTuples<T>, PlaceType, std::vector<T>,
                 std::vector<T>>(d, x, yref);
329 330 331
  }
}

T
tensor-tang 已提交
332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestLSTMKernel() {
  namespace jit = paddle::operators::jit;
  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);
            RandomVec<T>(3 * d, wp.data(), -2.f, 2.f);
            RandomVec<T>(d, ct_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* 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
371
            VLOG(10) << attr;
T
tensor-tang 已提交
372 373 374 375
            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
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
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestGRUKernel() {
  namespace jit = paddle::operators::jit;
  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
409
        VLOG(10) << attr;
T
tensor-tang 已提交
410 411 412
        TestAllImpls<KT, jit::GRUTuples<T>, PlaceType, std::vector<T>,
                     std::vector<T>, std::vector<T>>(attr, xsrc, ht_1, ht_ref,
                                                     attr);
413 414 415 416 417
      }
    }
  }
}

T
tensor-tang 已提交
418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
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);
  }
}

T
tensor-tang 已提交
471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553
// XYZNTuple
TEST(JITKernel, vmul) {
  namespace jit = paddle::operators::jit;
  TestXYZNKernel<jit::vmul, float, paddle::platform::CPUPlace>();
  TestXYZNKernel<jit::vmul, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vadd) {
  namespace jit = paddle::operators::jit;
  TestXYZNKernel<jit::vadd, float, paddle::platform::CPUPlace>();
  TestXYZNKernel<jit::vadd, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vaddrelu) {
  namespace jit = paddle::operators::jit;
  TestXYZNKernel<jit::vaddrelu, float, paddle::platform::CPUPlace>();
  TestXYZNKernel<jit::vaddrelu, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vsub) {
  namespace jit = paddle::operators::jit;
  TestXYZNKernel<jit::vsub, float, paddle::platform::CPUPlace>();
  TestXYZNKernel<jit::vsub, double, paddle::platform::CPUPlace>();
}

// AXYNTuples
TEST(JITKernel, vscal) {
  namespace jit = paddle::operators::jit;
  TestAXYNKernel<jit::vscal, float, paddle::platform::CPUPlace>();
  TestAXYNKernel<jit::vscal, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vaddbias) {
  namespace jit = paddle::operators::jit;
  TestAXYNKernel<jit::vaddbias, float, paddle::platform::CPUPlace>();
  TestAXYNKernel<jit::vaddbias, double, paddle::platform::CPUPlace>();
}

// XYNTuples
TEST(JITKernel, vrelu) {
  namespace jit = paddle::operators::jit;
  TestXYNKernel<jit::vrelu, float, paddle::platform::CPUPlace>();
  TestXYNKernel<jit::vrelu, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, videntity) {
  namespace jit = paddle::operators::jit;
  TestXYNKernel<jit::videntity, float, paddle::platform::CPUPlace>();
  TestXYNKernel<jit::videntity, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vexp) {
  namespace jit = paddle::operators::jit;
  TestXYNKernel<jit::vexp, float, paddle::platform::CPUPlace>();
  TestXYNKernel<jit::vexp, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vsigmoid) {
  namespace jit = paddle::operators::jit;
  TestXYNKernel<jit::vsigmoid, float, paddle::platform::CPUPlace>();
  TestXYNKernel<jit::vsigmoid, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, vtanh) {
  namespace jit = paddle::operators::jit;
  TestXYNKernel<jit::vtanh, float, paddle::platform::CPUPlace>();
  TestXYNKernel<jit::vtanh, double, paddle::platform::CPUPlace>();
}

// LSTM
TEST(JITKernel, lstmctht) {
  namespace jit = paddle::operators::jit;
  TestLSTMKernel<jit::lstmctht, float, paddle::platform::CPUPlace>();
  TestLSTMKernel<jit::lstmctht, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, lstmc1h1) {
  namespace jit = paddle::operators::jit;
  TestLSTMKernel<jit::lstmc1h1, float, paddle::platform::CPUPlace>();
  TestLSTMKernel<jit::lstmc1h1, double, paddle::platform::CPUPlace>();
}

// GRU
554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571
TEST(JITKernel, gruh1) {
  namespace jit = paddle::operators::jit;
  TestGRUKernel<jit::gruh1, float, paddle::platform::CPUPlace>();
  TestGRUKernel<jit::gruh1, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, gruhtpart1) {
  namespace jit = paddle::operators::jit;
  TestGRUKernel<jit::gruhtpart1, float, paddle::platform::CPUPlace>();
  TestGRUKernel<jit::gruhtpart1, double, paddle::platform::CPUPlace>();
}

TEST(JITKernel, gruhtpart2) {
  namespace jit = paddle::operators::jit;
  TestGRUKernel<jit::gruhtpart2, float, paddle::platform::CPUPlace>();
  TestGRUKernel<jit::gruhtpart2, double, paddle::platform::CPUPlace>();
}

T
tensor-tang 已提交
572 573 574 575 576 577 578 579
TEST(JITKernel, nchw16cmulnc) {
  namespace jit = paddle::operators::jit;
  TestNCHW16CMulNCKernel<jit::nchw16cmulnc, float,
                         paddle::platform::CPUPlace>();
  TestNCHW16CMulNCKernel<jit::nchw16cmulnc, double,
                         paddle::platform::CPUPlace>();
}

580
// TODO(yihua/TJ): add crf decoding and layer norm unit tests
T
tensor-tang 已提交
581

582 583 584
TEST(JITKernel, pool) {
  // TODO(TJ): add some test
}