binary.cc 24.2 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14
/* Copyright (c) 2021 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. */

15
#include "paddle/phi/infermeta/binary.h"
F
From00 已提交
16 17 18 19

#include <algorithm>
#include <vector>
#include "paddle/phi/common/data_type.h"
20
#include "paddle/phi/core/ddim.h"
21
#include "paddle/phi/kernels/funcs/common_shape.h"
C
Chen Weihang 已提交
22

23
namespace phi {
24

F
From00 已提交
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
void CompareInferMeta(const MetaTensor& x,
                      const MetaTensor& y,
                      int axis,
                      MetaTensor* out) {
  auto dim_x = x.dims();
  auto dim_y = y.dims();

  if (dim_x == dim_y) {
    out->share_meta(x);
  } else {
    int max_dim = std::max(dim_x.size(), dim_y.size());
    int axis = std::abs(dim_x.size() - dim_y.size());
    std::vector<int> x_dims_array(max_dim);
    std::vector<int> y_dims_array(max_dim);
    std::vector<int> out_dims_array(max_dim);
    funcs::GetBroadcastDimsArrays(dim_x,
                                  dim_y,
                                  x_dims_array.data(),
                                  y_dims_array.data(),
                                  out_dims_array.data(),
                                  max_dim,
                                  axis);

    out->set_dims(make_ddim(out_dims_array));
    out->share_lod(x);
  }

  out->set_dtype(DataType::BOOL);
}

void CompareAllInferMeta(const MetaTensor& x,
                         const MetaTensor& y,
                         MetaTensor* out) {
  auto dim_x = x.dims();
  auto dim_y = y.dims();
  PADDLE_ENFORCE_GE(
      dim_x.size(),
      dim_y.size(),
      errors::InvalidArgument(
          "The size of dim_y should not be greater than dim_x's."));
  out->share_lod(x);
  out->set_dims(make_ddim({1}));
  out->set_dtype(DataType::BOOL);
}

70 71
void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
  auto x_dims = x.dims();
72 73 74
  auto x_rank = static_cast<size_t>(x_dims.size());
  PADDLE_ENFORCE_EQ(true,
                    1 == x_rank || 2 == x_rank,
75
                    phi::errors::PreconditionNotMet(
76 77 78 79
                        "ShapeError: The dimensions of input tensor X (%s) "
                        "should be 1 or 2",
                        x_dims.to_str()));

80
  auto y_dims = y.dims();
81 82
  PADDLE_ENFORCE_EQ(
      true,
83
      x_rank == static_cast<size_t>(y_dims.size()),
84
      phi::errors::PreconditionNotMet(
85 86 87 88 89 90 91 92 93 94 95 96 97 98
          "ShapeError: The shape of input tensor Y: %s should match with "
          "input tenosr X: %s",
          y_dims.to_str(),
          x_dims.to_str()));
  bool shape_match = true;
  for (size_t i = 0; i < x_rank; ++i) {
    if (x_dims[i] != y_dims[i]) {
      shape_match = false;
      break;
    }
  }

  PADDLE_ENFORCE_EQ(true,
                    shape_match,
99
                    phi::errors::PreconditionNotMet(
100 101 102 103 104 105 106
                        "ShapeError: The shape of input tensor X: %s should "
                        "be exactly the same "
                        "with input tensor Y: %s",
                        x_dims.to_str(),
                        y_dims.to_str()));

  x_dims[x_dims.size() - 1] = 1;
107 108 109
  out->set_dims(x_dims);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
110 111
}

112 113 114 115 116
void MatmulInferMeta(const MetaTensor& x,
                     const MetaTensor& y,
                     bool trans_x,
                     bool trans_y,
                     MetaTensor* out) {
117 118
  std::vector<int64_t> dims_x = phi::vectorize(x.dims());
  std::vector<int64_t> dims_y = phi::vectorize(y.dims());
Z
zyfncg 已提交
119 120 121
  auto ndims_x = dims_x.size();
  auto ndims_y = dims_y.size();
  PADDLE_ENFORCE_GT(ndims_x,
122
                    0UL,
123
                    phi::errors::InvalidArgument(
Z
zyfncg 已提交
124 125 126
                        "The Input(x) dims size must be greater than 0,"
                        " but reviced dims size is 0. "));
  PADDLE_ENFORCE_GT(ndims_y,
127
                    0UL,
128
                    phi::errors::InvalidArgument(
Z
zyfncg 已提交
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
                        "The Input(y) dims size must be greater than 0,"
                        " but reviced dims size is 0. "));

  bool x_broadcasted = false, y_broadcasted = false;
  if (ndims_x == 1) {
    dims_x.insert(dims_x.begin(), 1);
    ndims_x = 2;
    x_broadcasted = true;
  }

  if (ndims_y == 1) {
    dims_y.push_back(1);
    ndims_y = 2;
    y_broadcasted = true;
  }

  size_t M, N;
  if (trans_x) {
    M = dims_x[ndims_x - 1];
  } else {
    M = dims_x[ndims_x - 2];
  }
  if (trans_y) {
    N = dims_y[ndims_y - 2];
  } else {
    N = dims_y[ndims_y - 1];
  }

  std::vector<int64_t> new_dims;
  if (ndims_x > ndims_y) {
    new_dims.assign(dims_x.begin(), dims_x.end() - 2);
  } else if (ndims_x < ndims_y) {
    new_dims.assign(dims_y.begin(), dims_y.end() - 2);
  } else {
    new_dims.reserve(ndims_x);
    for (size_t i = 0; i < ndims_x - 2; ++i) {
      new_dims.push_back(std::max(dims_x[i], dims_y[i]));
    }
  }
  if (!x_broadcasted) {
    new_dims.push_back(M);
  }
  if (!y_broadcasted) {
    new_dims.push_back(N);
  }
  if (x_broadcasted && y_broadcasted) {
    new_dims.push_back(1);
  }

178
  auto ddim_out = phi::make_ddim(new_dims);
Z
zyfncg 已提交
179

180 181 182
  out->set_dims(ddim_out);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
Z
zyfncg 已提交
183 184
}

185 186 187 188
void ElementwiseInferMeta(const MetaTensor& x,
                          const MetaTensor& y,
                          MetaTensor* out) {
  return ElementwiseRawInferMeta(x, y, -1, std::move(out));
189 190
}

191 192 193 194 195 196 197
void ElementwiseRawInferMeta(const MetaTensor& x,
                             const MetaTensor& y,
                             int axis,
                             MetaTensor* out) {
  if (x.dims() != y.dims()) {
    auto x_dims = x.dims();
    auto y_dims = y.dims();
198 199 200 201
    int max_dim = std::max(x_dims.size(), y_dims.size());
    if (x_dims.size() == y_dims.size()) {
      PADDLE_ENFORCE_EQ((axis == -1) || (axis == 0),
                        true,
202
                        phi::errors::InvalidArgument(
203 204 205 206 207 208 209 210 211
                            "axis should be -1 or 0 while the dimension of "
                            "tensor X (%s) is equal to the dimension of "
                            "tensor Y (%s), but received axis: %s",
                            x_dims.size(),
                            y_dims.size(),
                            axis));
    }
    PADDLE_ENFORCE_EQ((axis >= (-1 * max_dim)) && (axis < max_dim),
                      true,
212
                      phi::errors::InvalidArgument(
213 214 215 216 217 218 219 220 221 222
                          "The axis range must be [%s, %s), but axis is %s. "
                          "Please set the axis again.",
                          -1 * max_dim,
                          max_dim,
                          axis));
    axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
                     : axis);
    std::vector<int> x_dims_array(max_dim);
    std::vector<int> y_dims_array(max_dim);
    std::vector<int> out_dims_array(max_dim);
223 224 225 226 227 228 229
    funcs::GetBroadcastDimsArrays(x_dims,
                                  y_dims,
                                  x_dims_array.data(),
                                  y_dims_array.data(),
                                  out_dims_array.data(),
                                  max_dim,
                                  axis);
230
    auto out_dims = phi::make_ddim(out_dims_array);
231 232 233
    out->set_dims(out_dims);
  } else {
    out->set_dims(x.dims());
234
  }
235 236 237 238

  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
  out->share_lod(x);
239 240
}

241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276
void HuberLossInferMeta(const MetaTensor& input,
                        const MetaTensor& label,
                        float delta,
                        MetaTensor* out,
                        MetaTensor* residual,
                        MetaConfig config) {
  auto input_dims = input.dims();
  auto label_dims = label.dims();

  PADDLE_ENFORCE_EQ(input_dims.size(),
                    label_dims.size(),
                    phi::errors::InvalidArgument(
                        "Input(input) rank and Input(label) rank should be "
                        "same, but received input rank(%d) != label rank(%d)",
                        input_dims.size(),
                        label_dims.size()));

  bool contain_unknown_dim = phi::contain_unknown_dim(input_dims) ||
                             phi::contain_unknown_dim(label_dims);
  if (config.is_runtime || !contain_unknown_dim) {
    PADDLE_ENFORCE_EQ(
        input_dims,
        label_dims,
        phi::errors::InvalidArgument(
            "The Input(input) and Input(label) should have the same "
            "shape, but received input shape [%s] != label shape [%s]",
            input_dims,
            label_dims));
  }

  auto out_dims = label_dims;
  residual->set_dims(out_dims);
  out->set_dims(out_dims);
  out->share_lod(input);
}

277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335
void TriangularSolveInferMeta(const MetaTensor& x,
                              const MetaTensor& y,
                              bool upper,
                              bool transpose,
                              bool unitriangular,
                              MetaTensor* out) {
  auto x_dims = x.dims();
  auto y_dims = y.dims();

  auto x_dims_n = x_dims.size();
  auto y_dims_n = y_dims.size();

  PADDLE_ENFORCE_GE(x_dims_n,
                    2,
                    phi::errors::InvalidArgument(
                        "The input tensor X's dimensions of TriangularSolveOp "
                        "should be >= 2. But received X's "
                        "dimensions = %d, X's shape = [%s]",
                        x_dims.size(),
                        x_dims));

  PADDLE_ENFORCE_GE(y_dims_n,
                    2,
                    phi::errors::InvalidArgument(
                        "The input tensor Y's dimensions of TriangularSolveOp "
                        "should be >=2. But received Y's "
                        "dimensions = %d, Y's shape = [%s]",
                        y_dims.size(),
                        y_dims));

  PADDLE_ENFORCE_EQ(x_dims[x_dims_n - 2],
                    x_dims[x_dims_n - 1],
                    phi::errors::InvalidArgument(
                        "The inner-most 2 dimensions of Input(X) all should "
                        "be square matrices "
                        "But received X's shape[-2] = %d and shape[-1] = %d.",
                        x_dims[x_dims_n - 2],
                        x_dims[x_dims_n - 1]));

  std::vector<int64_t> x_dims_vec = phi::vectorize(x_dims);
  std::vector<int64_t> y_dims_vec = phi::vectorize(y_dims);

  std::vector<int64_t> x_dims_vec_cut(x_dims_vec.begin(), x_dims_vec.end() - 2);
  std::vector<int64_t> y_dims_vec_cut(y_dims_vec.begin(), y_dims_vec.end() - 2);

  std::vector<int64_t> expand_batch_portion =
      funcs::MatrixGetBroadcastBatchPortion(x_dims_vec_cut, y_dims_vec_cut);

  std::vector<int64_t> y_broadcast_dims({expand_batch_portion});
  y_broadcast_dims.insert(y_broadcast_dims.end(),
                          {y_dims_vec[y_dims_n - 2], y_dims_vec[y_dims_n - 1]});

  // dim of 'out' is the same with 'Y' after broadcast
  out->set_dims(phi::make_ddim(y_broadcast_dims));
  out->set_dtype(y.dtype());
  out->set_layout(y.layout());
  out->share_lod(y);
}

S
seemingwang 已提交
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
void IndexSampleInferMeta(const MetaTensor& x,
                          const MetaTensor& y,
                          MetaTensor* out,
                          MetaConfig config) {
  auto input_dims = x.dims();
  PADDLE_ENFORCE_EQ(input_dims.size(),
                    2,
                    errors::InvalidArgument(
                        "Inputs(X) shape of IndexSample op should be 2-D, but "
                        "got X's shape = [%s], please check X shape.",
                        input_dims));

  auto index_dims = y.dims();
  PADDLE_ENFORCE_EQ(
      index_dims.size(),
      2,
      errors::InvalidArgument(
          "Inputs(Index) shape of IndexSample op should be 2-D, but "
          "got Index's shape [%s] , please check index shape.",
          input_dims));
  if (config.is_runtime) {
    PADDLE_ENFORCE_EQ(input_dims[0],
                      index_dims[0],
                      errors::InvalidArgument(
                          "Inputs(X)'s value of dimension 0 must same with "
                          "Inputs(Index)'s value of dimension 0, but "
                          "got %d of Inputs(X), and got %d of Inputs(Index), "
                          "please check Inputs shape.",
                          input_dims[0],
                          index_dims[0]));
  }
  out->set_dtype(x.dtype());
  out->set_dims(index_dims);
  out->share_lod(y);
}
0
0x45f 已提交
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
void CrossInferMeta(const MetaTensor& x,
                    const MetaTensor& y,
                    int axis,
                    MetaTensor* out) {
  auto x_dim = x.dims();
  auto y_dim = y.dims();
  auto dim = axis;

  bool dims_match = phi::funcs::CheckDims(x_dim, y_dim);
  PADDLE_ENFORCE_EQ(
      dims_match,
      true,
      phi::errors::InvalidArgument("The 'shape' of Input(X) should be equal to "
                                   "the 'shape' of Input(Y). But received "
                                   "Input(X).dimensions = [%s], "
                                   "Input(Y).dimensions = [%s]",
                                   x_dim,
                                   y_dim));

  if (dim != DDim::kMaxRank) {
    PADDLE_ENFORCE_EQ(
        dim < x_dim.size() && dim >= (0 - x_dim.size()),
        true,
        phi::errors::OutOfRange(
            "Attr(dim) is out of range, It's expected "
            "to be in range of [-%d, %d]. But received Attr(dim) = %d.",
            x_dim.size(),
            x_dim.size() - 1,
            dim));
    if (dim < 0) {
      dim += x_dim.size();
    }
    PADDLE_ENFORCE_EQ(x_dim[dim] == 3 && y_dim[dim] == 3,
                      true,
                      phi::errors::InvalidArgument(
                          "Input(X/Y).dims()[dim] should be equal to 3."
                          "But received Input(X/Y).dims()[dim] = %d.",
                          x_dim[dim]));
  }
  out->set_dims(x_dim);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
  out->share_lod(x);
}

416
void Atan2InferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
417
  out->share_meta(x);
418 419
}

420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438
void SegmentPoolInferMeta(const MetaTensor& x,
                          const MetaTensor& segment_ids,
                          const std::string& pooltype,
                          MetaTensor* out,
                          MetaTensor* summed_ids,
                          MetaConfig config) {
  auto dims = x.dims();
  dims[0] = -1;
  out->set_dims(dims);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());

  if (pooltype == "MEAN") {
    summed_ids->set_dims({-1, 1});
    summed_ids->set_dtype(x.dtype());
    summed_ids->set_layout(x.layout());
  }
}

439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473
void BCELossInferMeta(const MetaTensor& input,
                      const MetaTensor& label,
                      MetaTensor* out,
                      MetaConfig config) {
  auto input_dims = input.dims();
  auto label_dims = label.dims();

  int rank = input_dims.size();
  PADDLE_ENFORCE_EQ(rank,
                    label_dims.size(),
                    phi::errors::InvalidArgument(
                        "Input(X) and Input(Label) shall have the same rank."
                        "But received: the rank of Input(X) is [%d], "
                        "the rank of Input(Label) is [%d].",
                        rank,
                        label_dims.size()));

  bool check = true;
  if ((!config.is_runtime) &&
      (phi::product(input_dims) <= 0 || phi::product(label_dims) <= 0)) {
    check = false;
  }

  if (check) {
    PADDLE_ENFORCE_EQ(input_dims,
                      label_dims,
                      phi::errors::InvalidArgument(
                          "Input(X) and Input(Label) shall have the same "
                          "shape. But received: the shape of Input(X) is "
                          "[%s], the shape of Input(Label) is [%s].",
                          input_dims,
                          label_dims));
  }

  out->set_dims(input_dims);
L
Linjie Chen 已提交
474
  out->set_dtype(input.dtype());
475 476 477
  out->share_lod(input);
}

0
0x45f 已提交
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
void BincountInferMeta(const MetaTensor& x,
                       const paddle::optional<const MetaTensor&> weights,
                       int minlength,
                       MetaTensor* out) {
  auto input_dim = x.dims();

  PADDLE_ENFORCE_GE(minlength,
                    0,
                    phi::errors::InvalidArgument(
                        "The minlength should be greater than or equal to 0."
                        "But received minlength is %d",
                        minlength));

  PADDLE_ENFORCE_EQ(
      input_dim.size(),
      1,
      phi::errors::InvalidArgument("The 'shape' of Input(X) must be 1-D tensor."
                                   "But the dimension of Input(X) is [%d]",
                                   input_dim.size()));

  if (weights.is_initialized()) {
    auto weights_dim = weights->dims();
    PADDLE_ENFORCE_EQ(weights_dim.size(),
                      1,
                      phi::errors::InvalidArgument(
                          "The 'shape' of Input(Weights) must be 1-D tensor."
                          "But the dimension of Input(Weights) is [%d]",
                          weights_dim.size()));

    PADDLE_ENFORCE_EQ(
        weights_dim[0],
        input_dim[0],
        phi::errors::InvalidArgument(
            "The 'shape' of Input(Weights) must be equal to the 'shape' of "
            "Input(X)."
            "But received: the 'shape' of Input(Weights) is [%s],"
            "the 'shape' of Input(X) is [%s]",
            weights_dim,
            input_dim));
  }
  out->set_dims(phi::make_ddim({-1}));
  if (weights.is_initialized()) {
    out->set_dtype(weights->dtype());
  } else {
    out->set_dtype(x.dtype());
  }

  out->share_lod(x);
}

Z
Zhong Hui 已提交
528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550
void DistInferMeta(const MetaTensor& x,
                   const MetaTensor& y,
                   float p,
                   MetaTensor* out) {
  auto x_dims = x.dims();
  auto y_dims = y.dims();

  PADDLE_ENFORCE_NE(phi::product(x_dims),
                    0,
                    phi::errors::InvalidArgument(
                        "The Input(X) has not been initialized properly. The "
                        "shape of Input(X) = [%s].",
                        x_dims));
  PADDLE_ENFORCE_NE(phi::product(y_dims),
                    0,
                    phi::errors::InvalidArgument(
                        "The Input(Y) has not been initialized properly. The "
                        "shape of Input(Y) = [%s].",
                        y_dims));
  out->set_dims({1});
  out->set_dtype(x.dtype());
}

551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583
void GatherNdInferMeta(const MetaTensor& x,
                       const MetaTensor& index,
                       MetaTensor* out) {
  auto x_dims = x.dims();
  auto x_dims_size = x_dims.size();
  auto index_dims = index.dims();
  auto index_dims_size = index_dims.size();

  PADDLE_ENFORCE_LE(
      index_dims[index_dims_size - 1],
      x_dims_size,
      phi::errors::InvalidArgument(
          "Input(Index).shape[-1] should be no greater than Input(X).rank"));
  PADDLE_ENFORCE_GE(index_dims_size,
                    1UL,
                    phi::errors::InvalidArgument(
                        "The rank of Input(Index) should be greater than 1"));

  std::vector<int64_t> result_dims;
  // The result dims is
  //   Index.shape[:-1] + X.shape[Index.shape[-1]:]
  for (int i = 0; i < index_dims_size - 1; ++i) {
    result_dims.emplace_back(index_dims[i]);
  }
  for (int i = index_dims[index_dims_size - 1]; i < x_dims_size; ++i) {
    result_dims.emplace_back(x_dims[i]);
  }

  out->set_dims(phi::make_ddim(result_dims));
  out->share_lod(x);
  out->set_dtype(x.dtype());
}

C
crystal 已提交
584 585 586 587 588 589 590 591 592 593 594 595 596
void GatherTreeMeta(const MetaTensor& ids,
                    const MetaTensor& parents,
                    MetaTensor* out) {
  auto ids_dims = ids.dims();
  auto parents_dims = parents.dims();
  PADDLE_ENFORCE_EQ(ids_dims == parents_dims,
                    true,
                    phi::errors::InvalidArgument(
                        "The shape of Input(Parents) must be same with the "
                        "shape of Input(Ids)."));
  out->set_dims(ids_dims);
}

597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638
void LogLossInferMeta(const MetaTensor& input,
                      const MetaTensor& label,
                      float epsilon,
                      MetaTensor* out,
                      MetaConfig config) {
  auto pred_dims = input.dims();
  auto label_dims = label.dims();

  if (config.is_runtime ||
      (phi::product(pred_dims) > 0 && phi::product(label_dims) > 0)) {
    PADDLE_ENFORCE_EQ(
        pred_dims,
        label_dims,
        phi::errors::InvalidArgument(
            "The dimensions of Input(Predicted) must be equal to the"
            "dimensions of Input(Labels), but received dimensions of "
            "Input(Predicted)"
            "is [%s], received dimensions of Input(Labels) is [%s].",
            pred_dims,
            label_dims));
  }
  PADDLE_ENFORCE_EQ(pred_dims.size(),
                    2,
                    phi::errors::InvalidArgument(
                        "The dimensions of Input(Predicted) must be 2,"
                        "But received dimensions of Input(Predicted)"
                        "is [%d]",
                        pred_dims.size()));
  if (config.is_runtime) {
    PADDLE_ENFORCE_EQ(pred_dims[1],
                      1,
                      phi::errors::InvalidArgument(
                          "Each row of Input(Predicted) contains a real value, "
                          "so the 2nd dimension of Input(X) must be 1,"
                          "But got [%d]",
                          pred_dims[1]));
  }
  out->set_dims({pred_dims[0], 1});
  out->set_dtype(input.dtype());
  out->share_lod(input);
}

F
furnace 已提交
639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668
void MvInferMeta(const MetaTensor& x, const MetaTensor& vec, MetaTensor* out) {
  auto dim_x = x.dims();
  auto dim_vec = vec.dims();
  PADDLE_ENFORCE_EQ(
      dim_x.size(),
      2,
      phi::errors::InvalidArgument("The rank of input X should be 2, but is %d",
                                   dim_x.size()));
  PADDLE_ENFORCE_EQ(
      dim_vec.size(),
      1,
      phi::errors::InvalidArgument(
          "The rank of input Vec should be 1, but is %d", dim_vec.size()));
  PADDLE_ENFORCE_EQ(dim_x[1],
                    dim_vec[0],
                    phi::errors::InvalidArgument(
                        "X's second dimension is expected to be equal to "
                        "Vec's first dimension"
                        "but recieved X'shape = [%s], Vec's shape = [%s]",
                        dim_x,
                        dim_vec));

  auto dim_out = phi::make_ddim({dim_x[0]});

  out->set_dims(dim_out);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
  out->share_lod(x);
}

669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709
void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
                                            const MetaTensor& label,
                                            bool normalize,
                                            int ignore_index,
                                            MetaTensor* out,
                                            MetaConfig config) {
  auto x_dims = x.dims();
  auto labels_dims = label.dims();
  int rank = x_dims.size();
  PADDLE_ENFORCE_EQ(rank,
                    labels_dims.size(),
                    phi::errors::InvalidArgument(
                        "Input(X) and Input(Label) shall have the same rank."
                        "But received: the rank of Input(X) is [%d], "
                        "the rank of Input(Label) is [%d].",
                        rank,
                        labels_dims.size()));

  bool check = true;
  if ((!config.is_runtime) &&
      (phi::product(x_dims) <= 0 || phi::product(labels_dims) <= 0)) {
    check = false;
  }

  if (check) {
    PADDLE_ENFORCE_EQ(
        phi::slice_ddim(x_dims, 0, rank),
        phi::slice_ddim(labels_dims, 0, rank),
        phi::errors::InvalidArgument(
            "Input(X) and Input(Label) shall have the same shape "
            "except the last dimension. But received: the shape of "
            "Input(X) is [%s], the shape of Input(Label) is [%s].",
            x_dims,
            labels_dims));
  }

  out->set_dims(x_dims);
  out->set_dtype(x.dtype());
  out->share_lod(x);
}

710
}  // namespace phi