unary.cc 67.5 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/unary.h"
16

L
Linjie Chen 已提交
17
#include <algorithm>
18
#include <set>
W
WJJ1995 已提交
19

20
#include "paddle/fluid/framework/convert_utils.h"
21
#include "paddle/phi/common/data_type.h"
22
#include "paddle/phi/common/type_traits.h"
23
#include "paddle/phi/core/enforce.h"
24
#include "paddle/phi/core/infermeta_utils.h"
F
From00 已提交
25
#include "paddle/phi/kernels/funcs/pooling.h"
26
#include "paddle/phi/kernels/funcs/unfold_functor.h"
27

28
namespace phi {
29

Z
zyfncg 已提交
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105
void ArgMinMaxInferMeta(const MetaTensor& x,
                        int64_t axis,
                        bool keepdims,
                        bool flatten,
                        int dtype,
                        MetaTensor* out,
                        MetaConfig config) {
  const auto& x_dims = x.dims();

  PADDLE_ENFORCE_GE(
      axis,
      -x_dims.size(),
      phi::errors::InvalidArgument("'axis'(%d) must be greater than or equal to"
                                   " -Rank(X)(%d).",
                                   axis,
                                   -x_dims.size()));
  PADDLE_ENFORCE_LT(axis,
                    x_dims.size(),
                    phi::errors::InvalidArgument(
                        "'axis'(%d) must be less than Rank(X)(%d) of Input(X).",
                        axis,
                        x_dims.size()));

  PADDLE_ENFORCE_EQ(
      (dtype < 0 || dtype == 2 || dtype == 3),
      true,
      phi::errors::InvalidArgument(
          "The attribute of dtype in argmin/argmax must be [%s] or [%s], but "
          "received [%s]",
          paddle::framework::DataTypeToString(
              paddle::framework::proto::VarType::INT32),
          paddle::framework::DataTypeToString(
              paddle::framework::proto::VarType::INT64),
          paddle::framework::DataTypeToString(
              static_cast<paddle::framework::proto::VarType::Type>(dtype))));

  auto x_rank = x_dims.size();
  if (axis < 0) axis += x_rank;
  if (config.is_runtime) {
    if (dtype == paddle::framework::proto::VarType::INT32) {
      int64_t all_element_num = 0;
      if (flatten) {
        all_element_num = phi::product(x_dims);

      } else {
        all_element_num = x_dims[axis];
      }
      PADDLE_ENFORCE_LE(
          all_element_num,
          INT_MAX,
          phi::errors::InvalidArgument(
              "The element num of the argmin/argmax input at axis is "
              "%d, is larger than int32 maximum value:%d, you must "
              "set the dtype of argmin/argmax to 'int64'.",
              all_element_num,
              INT_MAX));
    }
  }
  std::vector<int64_t> vec;
  if (flatten) {
    vec.emplace_back(static_cast<int64_t>(1));
  } else {
    for (int64_t i = 0; i < axis; i++) vec.emplace_back(x_dims[i]);
    if (keepdims) {
      vec.emplace_back(static_cast<int64_t>(1));
    }
    for (int64_t i = axis + 1; i < x_rank; i++) vec.emplace_back(x_dims[i]);
  }
  out->set_dims(phi::make_ddim(vec));
  if (dtype == 2) {
    out->set_dtype(DataType::INT32);
  } else if (dtype == 3) {
    out->set_dtype(DataType::INT64);
  }
}

L
Linjie Chen 已提交
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
void ArgsortInferMeta(const MetaTensor& input,
                      int axis,
                      bool descending,
                      MetaTensor* output,
                      MetaTensor* indices) {
  auto in_dims = input.dims();
  auto num_dims = in_dims.size();
  PADDLE_ENFORCE_GE(
      axis,
      -num_dims,
      phi::errors::InvalidArgument("'axis'(%d) must be greater than or equal to"
                                   " -num_dims(%d).",
                                   axis,
                                   -num_dims));
  PADDLE_ENFORCE_LT(
      axis,
      num_dims,
      phi::errors::InvalidArgument(
          "'axis'(%d) must be less than num_dims(%d).", axis, num_dims));

  output->share_dims(input);
  output->set_dtype(input.dtype());
  indices->share_dims(input);
  indices->set_dtype(DataType::INT64);
  output->share_lod(input);
  indices->share_lod(input);
}

134 135 136 137
void CastInferMeta(const MetaTensor& x, DataType out_dtype, MetaTensor* out) {
  out->set_dims(x.dims());
  out->set_dtype(out_dtype);
  out->set_layout(x.layout());
138 139
}

140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
void CholeskyInferMeta(const MetaTensor& x, bool upper, MetaTensor* out) {
  auto dims = x.dims();
  auto rank = dims.size();
  PADDLE_ENFORCE_GE(rank,
                    2,
                    errors::InvalidArgument(
                        "The Input(X) should have at least 2 dimensions. But "
                        "received a %d dimension tensor.",
                        rank));
  PADDLE_ENFORCE_EQ(
      dims[rank - 2],
      dims[rank - 1],
      errors::InvalidArgument(
          "The inner-most 2 dimensions of Input(X) all should be symmetric "
          "positive-definite matrices and have the same size. But received "
          "X's shape[-2] = %d and shape[-1] = %d.",
          dims[rank - 2],
          dims[rank - 1]));
  out->set_dims(x.dims());
  out->set_dtype(x.dtype());
}

162 163 164 165 166 167 168
void CopyToInferMeta(const MetaTensor& x,
                     Backend backend,
                     bool blocking,
                     MetaTensor* out) {
  UnchangedInferMeta(x, out);
}

169
void CreateLikeInferMeta(const MetaTensor& x, DataType dtype, MetaTensor* out) {
170 171
  out->set_dims(x.dims());
  out->set_dtype(dtype == DataType::UNDEFINED ? x.dtype() : dtype);
172
  out->set_layout(x.layout());
173 174
}

175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
void CumsumInferMeta(const MetaTensor& x,
                     int axis,
                     bool flatten,
                     bool exclusive,
                     bool reverse,
                     MetaTensor* out) {
  auto x_dims = x.dims();
  if (flatten) {
    out->set_dims(phi::make_ddim({phi::product(x_dims)}));
    out->set_dtype(x.dtype());
  } else {
    out->set_dims(x_dims);
    out->set_dtype(x.dtype());
  }

  out->share_lod(x);
}

Z
zyfncg 已提交
193 194 195 196 197
void DiagInferMeta(const MetaTensor& x,
                   int offset,
                   float padding_value,
                   MetaTensor* out) {
  auto x_dims = x.dims();
198

Z
zyfncg 已提交
199 200 201 202 203 204 205 206 207 208 209 210 211 212
  if (x_dims.size() == 1UL) {
    int64_t size_ = x_dims[0] + std::abs(offset);
    out->set_dims({size_, size_});
    out->set_dtype(x.dtype());
  } else if (x_dims.size() == 2UL) {
    int64_t size_ = 0;
    if (offset >= 0) {
      // Note(LutaoChu): Do not use std::min here, otherwise the calculation
      // of `size_` will have unexpected result on Windows Python3.8
      if (x_dims[0] < x_dims[1] - offset) {
        size_ = x_dims[0];
      } else {
        size_ = x_dims[1] - offset;
      }
213
    } else {
Z
zyfncg 已提交
214 215 216 217 218 219 220
      // Note(LutaoChu): Do not use std::min here, otherwise the calculation
      // of `size_` will have unexpected result on Windows Python3.8
      if (x_dims[0] + offset < x_dims[1]) {
        size_ = x_dims[0] + offset;
      } else {
        size_ = x_dims[1];
      }
221
    }
Z
zyfncg 已提交
222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
    out->set_dims({size_});
    out->set_dtype(x.dtype());
  } else {
    PADDLE_THROW(phi::errors::InvalidArgument(
        "The input tensor X's dimensions of DiagV2Op should be either 1 or "
        "2, but received %d.",
        x_dims.size()));
  }
}

void DiagonalInferMeta(const MetaTensor& input,
                       int offset,
                       int axis1,
                       int axis2,
                       MetaTensor* out) {
  auto x_dims = input.dims();
  int offset_ = offset;
  int axis1_ = axis1 < 0 ? x_dims.size() + axis1 : axis1;
  int axis2_ = axis2 < 0 ? x_dims.size() + axis2 : axis2;

  PADDLE_ENFORCE_GE(
      x_dims.size(),
      2,
      phi::errors::OutOfRange("Input's dim is out of range (expected at "
                              "least 2 dimensions, but got %ld).",
                              x_dims.size()));
  PADDLE_ENFORCE_LT(
      axis1_,
      x_dims.size(),
      phi::errors::OutOfRange(
          "Attr(axis1) is out of range (expected to be in range of [%ld, "
          "%ld], but got %ld).",
          -(x_dims.size()),
          (x_dims.size() - 1),
          axis1));
  PADDLE_ENFORCE_LT(
      axis2_,
      x_dims.size(),
      phi::errors::OutOfRange(
          "Attr(axis2) is out of range (expected to be in range of [%ld, "
          "%ld], but got %ld).",
          -(x_dims.size()),
          (x_dims.size() - 1),
          axis2));
  PADDLE_ENFORCE_NE(
      axis1_,
      axis2_,
      phi::errors::InvalidArgument("The dimensions should not be identical "
                                   "%d vs %d.",
                                   axis1,
                                   axis2));

  auto out_dims = vectorize(x_dims);
  // from out_dims get the dim size of axis1_.
  auto axis1_size = out_dims[axis1_];
  auto axis2_size = out_dims[axis2_];
  // delete two dims by attr axis1 and axis2 from out_dims.
  /* example:
     out_dim = [2, 3, 4];
     axis1 = 0;
     axis2 = 1;
     according to the attr of axis1 and axis2, we get:
     out_dim = [4].
  */
  out_dims.erase(out_dims.begin() + std::max(axis1_, axis2_));
  out_dims.erase(out_dims.begin() + std::min(axis1_, axis2_));

  if (offset_ == 0) {
    out_dims.push_back(std::min(axis1_size, axis2_size));
  } else if (offset_ > 0) {
    if ((axis2_size - offset_) > 0) {
      out_dims.push_back(std::min(axis1_size, axis2_size - offset_));
    } else {
      out_dims.push_back(0);
    }
  } else {
    if ((axis1_size + offset_) > 0) {
      out_dims.push_back(std::min(axis1_size + offset_, axis2_size));
    } else {
      out_dims.push_back(0);
    }
  }
  out->set_dims(phi::make_ddim(out_dims));
}

H
hong 已提交
307 308 309 310 311 312 313 314 315 316 317
void DropoutInferMeta(const MetaTensor& x, MetaTensor* out, MetaTensor* mask) {
  auto x_dims = x.dims();
  out->set_dims(x_dims);
  out->share_lod(x);
  out->set_dtype(x.dtype());

  if (mask != nullptr) {
    mask->set_dims(x_dims);
  }
}

Z
zyfncg 已提交
318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405
void EighInferMeta(const MetaTensor& x,
                   const std::string& uplo,
                   MetaTensor* out_w,
                   MetaTensor* out_v) {
  auto input_dim = x.dims();
  auto rank = input_dim.size();

  PADDLE_ENFORCE_GE(rank,
                    2,
                    phi::errors::InvalidArgument(
                        "The Input(X) should have at least 2 dimensions."
                        "But received a %d dimension tensor.",
                        rank));
  PADDLE_ENFORCE_EQ(
      input_dim[rank - 2],
      input_dim[rank - 1],
      phi::errors::InvalidArgument(
          "Eigh op is designed for square matrix, consequently"
          "inner-most 2 dimensions of Input(X) should be symmetric."
          "But received X's shape[-2] = %d and shape[-1] = %d.",
          input_dim[rank - 2],
          input_dim[rank - 1]));

  std::vector<int64_t> values_dim;

  for (auto i = 0; i < rank - 1; i++) {
    values_dim.emplace_back(input_dim[i]);
  }
  out_w->set_dims(phi::make_ddim(values_dim));
  out_v->set_dims(input_dim);
}

void FlattenInferMeta(const MetaTensor& x,
                      int start_axis,
                      int stop_axis,
                      MetaTensor* out) {
  auto x_dims = x.dims();
  int in_dims_size = x_dims.size();
  if (start_axis < 0) {
    start_axis = start_axis + in_dims_size;
  }
  if (stop_axis < 0) {
    stop_axis = stop_axis + in_dims_size;
  }
  PADDLE_ENFORCE_GE(
      stop_axis,
      start_axis,
      phi::errors::InvalidArgument("The stop_axis should be greater"
                                   "than or equal to start_axis."));

  int64_t outer = 1;
  std::vector<int32_t> out_shape;
  out_shape.reserve(in_dims_size - stop_axis + start_axis);

  for (int i = 0; i < start_axis; ++i) {
    out_shape.push_back(x_dims[i]);
  }
  for (int i = start_axis; i <= stop_axis; i++) {
    if (x_dims[i] == -1 || outer == -1) {
      outer = -1;
    } else {
      outer *= x_dims[i];
    }
  }
  out_shape.push_back(outer);
  for (int i = stop_axis + 1; i < in_dims_size; i++) {
    out_shape.push_back(x_dims[i]);
  }
  const auto& out_dims = phi::make_ddim(out_shape);
  out->set_dims(out_dims);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());

  if (x_dims[0] == out_dims[0]) {
    // Only pass LoD when the first dimension of output and Input(X)
    // are the same.
    out->share_lod(x);
  }
}

void GumbelSoftmaxInferMeta(const MetaTensor& x,
                            float temperature,
                            bool hard,
                            int axis,
                            MetaTensor* out) {
  UnchangedInferMetaCheckAxis(x, axis, out);
}

H
hong 已提交
406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425
void HistogramInferMeta(
    const MetaTensor& input, int64_t bins, int min, int max, MetaTensor* out) {
  PADDLE_ENFORCE_GE(bins,
                    1,
                    phi::errors::InvalidArgument(
                        "The bins should be greater than or equal to 1."
                        "But received nbins is %d",
                        bins));
  PADDLE_ENFORCE_GE(
      max,
      min,
      phi::errors::InvalidArgument("max must be larger or equal to min."
                                   "But received max is %d, min is %d",
                                   max,
                                   min));

  out->set_dims({bins});
  out->share_lod(input);
}

Z
zyfncg 已提交
426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492
void IncrementInferMeta(const MetaTensor& x, float value, MetaTensor* out) {
  PADDLE_ENFORCE_EQ(
      product(x.dims()),
      1UL,
      errors::InvalidArgument("The number of elements in Input(X) should be 1."
                              "Now the number is %d.",
                              product(x.dims())));
  out->set_dims(x.dims());
  out->share_lod(x);
  out->set_dtype(x.dtype());
}

static phi::DDim ValidateShape(const std::vector<int64_t> shape,
                               const phi::DDim& in_dims) {
  const int64_t in_size = phi::product(in_dims);
  auto in_dims_vec = phi::vectorize(in_dims);
  bool all_positive = std::all_of(in_dims_vec.cbegin(),
                                  in_dims_vec.cend(),
                                  [](int64_t i) { return i > 0; });
  // only one dimension can be set to -1, whose size will be automatically
  // infered.
  const int64_t unk_dim_val = -1;
  const int64_t copy_dim_val = 0;

  std::vector<int64_t> output_shape(shape.size(), 0);
  int64_t capacity = 1;
  int unk_dim_idx = -1;
  for (size_t i = 0; i < shape.size(); ++i) {
    if (shape[i] == unk_dim_val) {
      PADDLE_ENFORCE_EQ(
          unk_dim_idx,
          -1,
          phi::errors::InvalidArgument(
              "Only one dimension value of 'shape' in ReshapeOp can "
              "be -1. But received shape = [%s], shape[%d] is also -1.",
              phi::make_ddim(shape),
              i));
      unk_dim_idx = i;
    } else if (shape[i] == copy_dim_val) {
      PADDLE_ENFORCE_LT(
          static_cast<int>(i),
          in_dims.size(),
          phi::errors::InvalidArgument(
              "The index of 0 in `shape` must be less than "
              "the input tensor X's dimensions. "
              "But received shape = [%s], shape[%d] = 0, X's shape = [%s], "
              "X's dimensions = %d.",
              phi::make_ddim(shape),
              i,
              in_dims,
              in_dims.size()));
    } else {
      PADDLE_ENFORCE_GT(
          shape[i],
          0,
          phi::errors::InvalidArgument(
              "Each dimension value of 'shape' in ReshapeOp must not "
              "be negative except one unknown dimension. "
              "But received  shape = [%s], shape[%d] = %d.",
              phi::make_ddim(shape),
              i,
              shape[i]));
    }

    // NOTE all non-zero values will be converted to True (include negative
    // value)
    capacity *= (shape[i] ? shape[i] : in_dims[i]);
493 494 495 496 497 498 499 500 501 502 503 504 505
    output_shape[i] = (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
  }

  if (unk_dim_idx != -1) {
    if (all_positive) {
      // in_size < 0 and is un-determinate in compile time, skip the check,
      // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
      // capacity = -24, in_size = -8, output_shape[0] = 0
      // the following check will fail.
      output_shape[unk_dim_idx] = -in_size / capacity;
      PADDLE_ENFORCE_EQ(
          output_shape[unk_dim_idx] * capacity,
          -in_size,
506
          phi::errors::InvalidArgument(
507 508 509 510 511 512 513
              "The 'shape' attribute in ReshapeOp is invalid. "
              "The input tensor X'size must be divisible by known "
              "capacity of 'shape'. "
              "But received X's shape = [%s], X's size = %d, "
              "'shape' is [%s], known capacity of 'shape' is %d.",
              in_dims,
              in_size,
514
              phi::make_ddim(shape),
515 516 517 518 519 520 521 522 523
              capacity));
    } else {
      output_shape[unk_dim_idx] = -1;
    }
  } else {
    if (all_positive) {
      PADDLE_ENFORCE_EQ(
          capacity,
          in_size,
524
          phi::errors::InvalidArgument(
525 526 527 528 529 530 531
              "The 'shape' in ReshapeOp is invalid. "
              "The input tensor X'size must be equal to the capacity of "
              "'shape'. "
              "But received X's shape = [%s], X's size = %d, 'shape' is "
              "[%s], the capacity of 'shape' is %d.",
              in_dims,
              in_size,
532
              phi::make_ddim(shape),
533 534 535 536 537 538 539 540 541 542 543
              capacity));
    }
  }

  // support reshape with zero-input(input tensor with product(shape) == 0)
  // by now we require that if the input tensor is zero shape, the target
  // shape of output must be zero
  if (in_size == 0) {
    PADDLE_ENFORCE_LE(
        capacity,
        in_size,
544
        phi::errors::InvalidArgument(
545 546 547 548 549 550
            "The 'shape' in ReshapeOp is invalid. "
            "The input tensor X's shape = [%s], X's capacity = %d."
            "But the target shape of Out is [%s],  the "
            "capacity of 'Out' is %d.",
            in_dims,
            in_size,
551
            phi::make_ddim(shape),
552 553 554
            capacity));
  }

555
  return phi::make_ddim(output_shape);
556 557
}

558 559 560
void InferMetaFromVecValue(const MetaTensor& x,
                           const std::vector<int64_t>& shape,
                           MetaTensor* out) {
561 562
  PADDLE_ENFORCE_EQ(!shape.empty(),
                    true,
563
                    phi::errors::InvalidArgument(
564 565
                        "The parameter 'shape' in ReshapeOp must be set. "
                        "But received 'shape' is empty."));
566
  auto x_dims = x.dims();
567
  auto out_dims = ValidateShape(shape, x_dims);
568 569 570 571
  out->set_dims(out_dims);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
  if (x_dims[0] == out_dims[0]) {
572 573
    // Only pass LoD when the first dimension of output and Input(X)
    // are the same.
574
    out->share_lod(x);
575 576 577
  }
}

W
WJJ1995 已提交
578 579 580 581 582
void IsEmptyInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->set_dims(phi::make_ddim({1}));
  out->set_dtype(DataType::BOOL);
}

Z
zyfncg 已提交
583 584 585 586 587
void IsfiniteInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->set_dims(x.dims());
  out->set_dtype(DataType::BOOL);
}

588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648
void KthvalueInferMeta(const MetaTensor& x,
                       int k,
                       int axis,
                       bool keepdim,
                       MetaTensor* out,
                       MetaTensor* indices,
                       MetaConfig config) {
  auto input_dims = x.dims();
  const int& dim_size = input_dims.size();
  PADDLE_ENFORCE_LT(axis,
                    dim_size,
                    phi::errors::InvalidArgument(
                        "the axis must be [-%d, %d), but received %d .",
                        dim_size,
                        dim_size,
                        axis));
  PADDLE_ENFORCE_GE(axis,
                    -dim_size,
                    phi::errors::InvalidArgument(
                        "the axis must be [-%d, %d), but received %d .",
                        dim_size,
                        dim_size,
                        axis));
  if (axis < 0) axis += dim_size;
  PADDLE_ENFORCE_GE(
      k,
      1,
      phi::errors::InvalidArgument(
          "the k in the kthvalue must >= 1, but received %d .", k));
  PADDLE_ENFORCE_GE(
      input_dims.size(),
      1,
      phi::errors::InvalidArgument("input of kthvalue must have >= 1d shape"));
  if (config.is_runtime) {
    PADDLE_ENFORCE_GE(
        input_dims[axis],
        k,
        phi::errors::InvalidArgument(
            "input of kthvalue must have >= %d columns in axis of %d",
            k,
            axis));
  }
  std::vector<int64_t> dimvec;
  for (int64_t i = 0; i < axis; i++) {
    dimvec.emplace_back(input_dims[i]);
  }
  if (keepdim) {
    dimvec.emplace_back(static_cast<int64_t>(1));
  }
  for (int64_t i = axis + 1; i < dim_size; i++) {
    dimvec.emplace_back(input_dims[i]);
  }
  DDim dims = phi::make_ddim(dimvec);
  out->set_dims(dims);
  out->share_lod(x);
  out->set_dtype(x.dtype());
  indices->set_dims(dims);
  indices->share_lod(x);
  indices->set_dtype(x.dtype());
}

649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670
void MatrixPowerInferMeta(const MetaTensor& x, int n, MetaTensor* out) {
  auto dims = x.dims();
  auto n_dim = dims.size();
  PADDLE_ENFORCE_GE(n_dim,
                    2,
                    phi::errors::InvalidArgument(
                        "The Input(X) should have at least 2 dimensions. But "
                        "received a %d dimension tensor.",
                        n_dim));
  PADDLE_ENFORCE_EQ(dims[n_dim - 2],
                    dims[n_dim - 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.",
                        dims[n_dim - 2],
                        dims[n_dim - 1]));
  out->set_dims(dims);
  out->share_lod(x);
  out->set_dtype(x.dtype());
}

F
From00 已提交
671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742
void MaxPoolWithIndexInferMeta(const MetaTensor& x,
                               const std::vector<int>& kernel_size,
                               const std::vector<int>& strides,
                               const std::vector<int>& paddings,
                               bool global_pooling,
                               bool adaptive,
                               MetaTensor* out,
                               MetaTensor* mask,
                               MetaConfig config) {
  std::vector<int> paddings_ = paddings;
  std::vector<int> kernel_size_ = kernel_size;

  auto x_dims = x.dims();

  PADDLE_ENFORCE(
      x_dims.size() == 4 || x_dims.size() == 5,
      errors::InvalidArgument(
          "Pooling intput should be 4-D or 5-D tensor but received %dD-Tensor",
          x_dims.size()));

  if (global_pooling) {
    kernel_size_.resize(static_cast<size_t>(x_dims.size()) - 2);
    for (size_t i = 0; i < kernel_size_.size(); ++i) {
      paddings_[i] = 0;
      kernel_size_[i] = static_cast<int>(x_dims[i + 2]);
    }
  }

  PADDLE_ENFORCE_EQ(
      x_dims.size() - kernel_size_.size(),
      2U,
      errors::InvalidArgument(
          "The input size %d minus the kernel size %d should equal to 2.",
          x_dims.size(),
          kernel_size_.size()));
  PADDLE_ENFORCE_EQ(
      kernel_size_.size(),
      strides.size(),
      errors::InvalidArgument(
          "Strides size %d and pooling size %d should be the same.",
          strides.size(),
          kernel_size_.size()));
  PADDLE_ENFORCE_EQ(
      kernel_size_.size(),
      paddings_.size(),
      errors::InvalidArgument(
          "Paddings size %d and pooling size %d should be the same.",
          paddings_.size(),
          kernel_size_.size()));

  std::vector<int64_t> output_shape({x_dims[0], x_dims[1]});
  if (adaptive) {
    output_shape.insert(
        output_shape.end(), kernel_size_.begin(), kernel_size_.end());
  } else {
    for (size_t i = 0; i < kernel_size_.size(); ++i) {
      if ((!config.is_runtime) && (x_dims[i + 2] < 0)) {
        output_shape.push_back(x_dims[i + 2]);
      } else {
        output_shape.push_back(funcs::MaxPoolOutputSize(
            x_dims[i + 2], kernel_size_[i], paddings_[i], strides[i]));
      }
    }
  }

  out->set_dims(make_ddim(output_shape));
  out->set_dtype(x.dtype());

  mask->set_dims(make_ddim(output_shape));
  mask->set_dtype(paddle::experimental::CppTypeToDataType<int>::Type());
}

743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785
void ModeInferMeta(const MetaTensor& x,
                   int axis,
                   bool keepdim,
                   MetaTensor* out,
                   MetaTensor* indices) {
  auto input_dims = x.dims();
  const int& dim_size = input_dims.size();
  PADDLE_ENFORCE_EQ(
      (axis < dim_size) && (axis >= (-1 * dim_size)),
      true,
      errors::InvalidArgument(
          "the axis of ModeOp must be [-%d, %d), but you set axis is %d",
          dim_size,
          dim_size,
          axis));
  PADDLE_ENFORCE_GE(
      input_dims.size(),
      1,
      errors::InvalidArgument("input of ModeOp must have >= 1d shape"));
  if (axis < 0) axis += dim_size;
  std::vector<int64_t> dimvec;
  for (int64_t i = 0; i < axis; i++) {
    dimvec.emplace_back(input_dims[i]);
  }
  if (keepdim) {
    dimvec.emplace_back(static_cast<int64_t>(1));
  }
  for (int64_t i = axis + 1; i < dim_size; i++) {
    dimvec.emplace_back(input_dims[i]);
  }
  DDim dims = phi::make_ddim(dimvec);
  PADDLE_ENFORCE_GE(input_dims.size(),
                    1,
                    errors::InvalidArgument("input shape should >= 1d"));
  out->set_dims(dims);
  out->share_lod(x);
  out->set_dtype(x.dtype());

  indices->set_dims(dims);
  indices->share_lod(x);
  indices->set_dtype(x.dtype());
}

786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820
void MultinomialInferMeta(const MetaTensor& x,
                          int num_samples,
                          bool replacement,
                          MetaTensor* out) {
  auto x_dim = x.dims();
  int64_t x_rank = x_dim.size();
  PADDLE_ENFORCE_GT(x_rank,
                    0,
                    errors::InvalidArgument(
                        "The number of dimensions of the input probability "
                        "distribution should be > 0, but got %d.",
                        x_rank));
  PADDLE_ENFORCE_LE(x_rank,
                    2,
                    errors::InvalidArgument(
                        "The number of dimensions of the input probability "
                        "distribution should be <= 2, but got %d.",
                        x_rank));

  std::vector<int64_t> out_dims(x_rank);
  for (int64_t i = 0; i < x_rank - 1; i++) {
    out_dims[i] = x_dim[i];
  }

  PADDLE_ENFORCE_GT(
      num_samples,
      0,
      errors::InvalidArgument(
          "The number of samples should be > 0, but got %d.", num_samples));
  out_dims[x_rank - 1] = num_samples;

  out->set_dims(make_ddim(out_dims));
  out->set_dtype(DataType::INT64);
}

H
hong 已提交
821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838
void NormInferMeta(const MetaTensor& x,
                   int axis,
                   float epsilon,
                   bool is_test,
                   MetaTensor* out,
                   MetaTensor* norm) {
  auto xdim = x.dims();
  out->set_dims(x.dims());
  out->set_dtype(x.dtype());

  if (is_test == false) {
    if (axis < 0) axis = xdim.size() + axis;
    xdim[axis] = 1;
    norm->set_dims(xdim);
    norm->set_dtype(x.dtype());
  }
}

Z
zyfncg 已提交
839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861
void PadInferMeta(const MetaTensor& input,
                  const std::vector<int>& paddings,
                  float pad_value,
                  MetaTensor* out,
                  MetaConfig config) {
  auto x_dim = input.dims();
  PADDLE_ENFORCE_EQ(
      static_cast<int>(paddings.size()),
      x_dim.size() * 2,
      phi::errors::InvalidArgument(
          "Size of 'paddings' dimension should be equal to 2 * size of "
          "Input(X)'s dimension, but received (size of 'paddings' dimension "
          "is) %d vs (2 * size of Input(X)'s dimension is) %d.",
          static_cast<int>(paddings.size()),
          x_dim.size() * 2));
  for (size_t i = 0; i < paddings.size(); ++i) {
    PADDLE_ENFORCE_GE(paddings[i],
                      0,
                      phi::errors::InvalidArgument(
                          "The element of 'paddings' should >= 0, but "
                          "received %d for index %d.",
                          paddings[i],
                          static_cast<int>(i)));
862
  }
Z
zyfncg 已提交
863 864 865 866
  std::vector<int64_t> out_dims(x_dim.size());
  for (int i = 0; i < x_dim.size(); ++i) {
    if ((!config.is_runtime) && (x_dim[i] == -1)) {
      out_dims[i] = -1;
867
    } else {
Z
zyfncg 已提交
868
      out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
869 870
    }
  }
Z
zyfncg 已提交
871 872 873 874 875
  out->set_dims(phi::make_ddim(out_dims));
  if (out_dims[0] == x_dim[0]) {
    // Only pass LoD when the first dimension is equal between
    // output and input.
    out->share_lod(input);
876
  }
Z
zyfncg 已提交
877
  out->set_dtype(input.dtype());
878 879
}

880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950
void Pad3dInferMeta(const MetaTensor& x,
                    const ScalarArray& paddings_scalar_array,
                    const std::string& mode,
                    float value,
                    const std::string& data_format,
                    MetaTensor* out,
                    MetaConfig config) {
  auto x_dim = x.dims();
  PADDLE_ENFORCE_EQ(x_dim.size(),
                    5,
                    errors::InvalidArgument(
                        "The size of Input(X)'s dimension should be equal to "
                        "5, but received %d. ",
                        x_dim.size()));

  std::vector<int64_t> out_dims(x_dim.size());
  out_dims[0] = x_dim[0];
  if (paddings_scalar_array.FromTensor()) {
    if (config.is_runtime) {
      PADDLE_ENFORCE_EQ(
          paddings_scalar_array.GetData().size(),
          6,
          errors::InvalidArgument("Shape of Input(Paddings) should be equal to "
                                  "[6], but received [%d].",
                                  paddings_scalar_array.GetData().size()));
    }
    out_dims[1] = x_dim[1];
    out_dims[2] = x_dim[2];
    out_dims[3] = x_dim[3];
  } else {
    auto paddings = paddings_scalar_array.GetData();

    PADDLE_ENFORCE_EQ(
        paddings.size(),
        6,
        errors::InvalidArgument(
            "Size of paddings should be equal to 6, but received %d.",
            static_cast<int>(paddings.size())));
    if (data_format == "NCDHW") {
      out_dims[1] = x_dim[1];  // channel
      out_dims[2] = ((!config.is_runtime) && (x_dim[2] < 0))
                        ? x_dim[2]
                        : (x_dim[2] + paddings[4] + paddings[5]);  // depth

      out_dims[3] = ((!config.is_runtime) && (x_dim[3] < 0))
                        ? x_dim[3]
                        : (x_dim[3] + paddings[2] + paddings[3]);  // height

      out_dims[4] = ((!config.is_runtime) && (x_dim[4] < 0))
                        ? x_dim[4]
                        : (x_dim[4] + paddings[0] + paddings[1]);  // width
    } else {                                                       // NDHWC
      out_dims[4] = x_dim[4];                                      // channel

      out_dims[1] = ((!config.is_runtime) && (x_dim[1] < 0))
                        ? x_dim[1]
                        : (x_dim[1] + paddings[4] + paddings[5]);  // depth
      out_dims[2] = ((!config.is_runtime) && (x_dim[2] < 0))
                        ? x_dim[2]
                        : (x_dim[2] + paddings[2] + paddings[3]);  // height
      out_dims[3] = ((!config.is_runtime) && (x_dim[3] < 0))
                        ? x_dim[3]
                        : (x_dim[3] + paddings[0] + paddings[1]);  // width
    }
  }

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

Z
zyfncg 已提交
951 952 953 954 955 956 957
void PixelShuffleInferMeta(const MetaTensor& x,
                           int upscale_factor,
                           const std::string& data_format,
                           MetaTensor* out) {
  auto input_dims = x.dims();
  PADDLE_ENFORCE_EQ(input_dims.size(),
                    4,
958
                    phi::errors::InvalidArgument(
Z
zyfncg 已提交
959 960 961
                        "Input should be a 4-D tensor of format [N, C, H, W] "
                        "or [N, H, W, C], but got %u.",
                        input_dims.size()));
962

Z
zyfncg 已提交
963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980
  const bool channel_last = (data_format == "NHWC");

  if (!channel_last) {
    PADDLE_ENFORCE_EQ(input_dims[1] % (upscale_factor * upscale_factor),
                      0,
                      phi::errors::InvalidArgument(
                          "The square of upscale_factor[%u] should divide the "
                          "number of channel[%u]",
                          upscale_factor * upscale_factor,
                          input_dims[1]));
  } else {
    PADDLE_ENFORCE_EQ(input_dims[3] % (upscale_factor * upscale_factor),
                      0,
                      phi::errors::InvalidArgument(
                          "The square of upscale_factor[%u] should divide the "
                          "number of channel[%u]",
                          upscale_factor * upscale_factor,
                          input_dims[3]));
981
  }
Z
zyfncg 已提交
982 983 984 985 986 987 988 989 990 991 992 993 994
  auto output_dims = input_dims;
  output_dims[0] = input_dims[0];
  if (!channel_last) {
    output_dims[1] = input_dims[1] / (upscale_factor * upscale_factor);
    output_dims[2] = input_dims[2] * upscale_factor;
    output_dims[3] = input_dims[3] * upscale_factor;
  } else {
    output_dims[1] = input_dims[1] * upscale_factor;
    output_dims[2] = input_dims[2] * upscale_factor;
    output_dims[3] = input_dims[3] / (upscale_factor * upscale_factor);
  }
  out->set_dtype(x.dtype());
  out->set_dims(output_dims);
995 996
}

F
From00 已提交
997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108
void PoolInferMeta(const MetaTensor& x,
                   const std::vector<int>& kernel_size,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings,
                   bool ceil_mode,
                   bool exclusive,
                   const std::string& data_format,
                   const std::string& pooling_type,
                   bool global_pooling,
                   bool adaptive,
                   const std::string& padding_algorithm,
                   MetaTensor* out,
                   MetaConfig config) {
  std::vector<int> paddings_ = paddings;
  std::vector<int> kernel_size_ = kernel_size;

  auto x_dims = x.dims();
  PADDLE_ENFORCE_EQ(
      x_dims.size() == 4 || x_dims.size() == 5,
      true,
      errors::InvalidArgument(
          "the input of Op(pool) should be 4-D or 5-D Tensor. But "
          "received: %u-D Tensor and it's shape is [%s].",
          x_dims.size(),
          x_dims));

  PADDLE_ENFORCE_EQ(x_dims.size() - kernel_size_.size(),
                    2U,
                    errors::InvalidArgument(
                        "the dimension of input minus the size of "
                        "Attr(kernel_size_) must be euqal to 2 in Op(pool). "
                        "But received: the dimension of input minus the size "
                        "of Attr(kernel_size_) is %d, the "
                        "input's dimension is %d, the shape of input "
                        "is [%s], the Attr(kernel_size_)'s size is %d, the "
                        "Attr(kernel_size_) is [%s].",
                        x_dims.size() - kernel_size_.size(),
                        x_dims.size(),
                        x_dims,
                        kernel_size_.size(),
                        make_ddim(kernel_size_)));

  PADDLE_ENFORCE_EQ(
      kernel_size_.size(),
      strides.size(),
      errors::InvalidArgument(
          "the size of Attr(kernel_size_) and Attr(strides) in "
          "Op(pool) must be equal. "
          "But received: Attr(kernel_size_)'s size is %d, Attr(strides)'s "
          "size is %d, Attr(kernel_size_) is [%s], Attr(strides)is [%s].",
          kernel_size_.size(),
          strides.size(),
          make_ddim(kernel_size_),
          make_ddim(strides)));

  // MKL-DNN Kernels are using NCHW order of dims description
  // so we ignore data_format consideration for MKL-DNN kernel
  const bool channel_last = (config.is_run_mkldnn_kernel == false) &&
                            (data_format == "NHWC" || data_format == "NDHWC");

  // update paddings if "SAME" or global_pooling
  DDim data_dims;
  if (channel_last) {
    data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
  } else {
    data_dims = slice_ddim(x_dims, 2, x_dims.size());
  }
  funcs::UpdatePadding(&paddings_,
                       global_pooling,
                       adaptive,
                       padding_algorithm,
                       data_dims,
                       strides,
                       kernel_size_);

  if (global_pooling) {
    funcs::UpdateKernelSize(&kernel_size_, data_dims);
  }

  std::vector<int64_t> output_shape;
  if (adaptive) {
    output_shape.insert(
        output_shape.end(), kernel_size_.begin(), kernel_size_.end());
  } else {
    for (int i = 0; i < data_dims.size(); ++i) {
      if ((!config.is_runtime) && (data_dims[i] < 0)) {
        output_shape.push_back(data_dims[i]);
      } else {
        output_shape.push_back(funcs::PoolOutputSize(data_dims[i],
                                                     kernel_size_[i],
                                                     paddings_[2 * i],
                                                     paddings_[2 * i + 1],
                                                     strides[i],
                                                     ceil_mode));
      }
    }
  }

  // output_N = input_N
  output_shape.insert(output_shape.begin(), x_dims[0]);
  // output_C = input_C
  if (channel_last) {
    output_shape.push_back(x_dims[x_dims.size() - 1]);
  } else {
    output_shape.insert(output_shape.begin() + 1, x_dims[1]);
  }

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

Z
zyfncg 已提交
1109 1110 1111 1112
void RealAndImagInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->set_dims(x.dims());
  out->set_dtype(dtype::ToReal(x.dtype()));
  out->set_layout(x.layout());
1113 1114
}

1115 1116 1117 1118
DDim ReduceInferDim(const MetaTensor& x,
                    const std::vector<int64_t>& axis,
                    bool keep_dim,
                    bool reduce_all) {
1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148
  auto x_rank = x.dims().size();

  std::vector<int64_t> formated_axis = axis;
  for (size_t i = 0; i < axis.size(); ++i) {
    PADDLE_ENFORCE_LT(axis[i],
                      x_rank,
                      errors::InvalidArgument(
                          "The reduce dim index %d should be in the "
                          "range [-dimension(X), dimension(X)] "
                          "which dimesion = %d. But received dim index = %d.",
                          i,
                          x_rank,
                          axis[i]));
    PADDLE_ENFORCE_GE(axis[i],
                      -x_rank,
                      errors::InvalidArgument(
                          "The reduce dim index %d should be in the "
                          "range [-dimension(X), dimension(X)] "
                          "which dimesion = %d. But received dim index = %d.",
                          i,
                          x_rank,
                          axis[i]));

    if (axis[i] < 0) {
      formated_axis[i] = axis[i] + x_rank;
    }
  }

  bool full_dim = true;
  std::set<int64_t> dims_set(formated_axis.begin(), formated_axis.end());
1149
  for (int64_t i = 0; i < x.dims().size(); ++i) {
1150
    if (dims_set.find(i) == dims_set.end()) {
1151
      full_dim = false;
1152 1153 1154
      break;
    }
  }
1155
  reduce_all = reduce_all || full_dim;
1156 1157 1158

  std::vector<int64_t> out_dim_vector;
  if (keep_dim) {
1159
    for (int64_t i = 0; i < x.dims().size(); ++i) {
1160 1161 1162
      if (reduce_all || dims_set.find(i) != dims_set.end()) {
        out_dim_vector.push_back(1);
      } else {
1163
        out_dim_vector.push_back(x.dims().at(i));
1164 1165 1166
      }
    }
  } else {
1167
    for (int64_t i = 0; i < x.dims().size(); ++i) {
1168 1169 1170
      if (reduce_all || dims_set.find(i) != dims_set.end()) {
        continue;
      } else {
1171
        out_dim_vector.push_back(x.dims().at(i));
1172 1173 1174 1175 1176 1177 1178
      }
    }

    if (out_dim_vector.size() == 0) {
      out_dim_vector.push_back(1);
    }
  }
1179
  DDim out_dim = phi::make_ddim(out_dim_vector);
1180

1181 1182 1183
  return out_dim;
}

Z
zyfncg 已提交
1184
void ReduceInferMeta(const MetaTensor& x,
1185 1186 1187
                     const std::vector<int64_t>& axis,
                     bool keep_dim,
                     MetaTensor* out) {
Z
zyfncg 已提交
1188 1189
  bool reduce_all = false;
  ReduceInferMetaBase(x, axis, keep_dim, reduce_all, out);
1190 1191
}

1192 1193 1194 1195 1196 1197 1198 1199 1200
void ReduceInferMetaBase(const MetaTensor& x,
                         const std::vector<int64_t>& axis,
                         bool keep_dim,
                         bool reduce_all,
                         MetaTensor* out) {
  DDim out_dim = ReduceInferDim(x, axis, keep_dim, reduce_all);
  out->set_dims(out_dim);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
1201 1202
}

Z
zyfncg 已提交
1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220
void ReshapeInferMeta(const MetaTensor& x,
                      const ScalarArray& shape,
                      MetaTensor* out,
                      MetaConfig config) {
  auto& shape_data = shape.GetData();
  PADDLE_ENFORCE_NOT_NULL(out,
                          phi::errors::InvalidArgument(
                              "Output(Out) of ReshapeOp should not be null."));
  if (!config.is_runtime && shape.FromTensor()) {
    out->set_dims(phi::make_ddim(shape_data));
    out->share_lod(x);
    return;
  }
  PADDLE_ENFORCE_GT(shape_data.size(),
                    0,
                    phi::errors::InvalidArgument(
                        "The shape's size in ReshapeOp can't be zero."));
  InferMetaFromVecValue(x, shape_data, out);
1221 1222
}

Z
zyfncg 已提交
1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240
void ReshapeWithXShapeInferMeta(const MetaTensor& x,
                                const ScalarArray& shape,
                                MetaTensor* xshape,
                                MetaTensor* out,
                                MetaConfig config) {
  PADDLE_ENFORCE_NOT_NULL(
      xshape,
      phi::errors::InvalidArgument(
          "Output(XShape) of ReshapeOp should not be null."));
  const auto& x_dims = x.dims();
  std::vector<int64_t> xshape_dims(x_dims.size() + 1);
  xshape_dims[0] = 0;
  for (int i = 0; i < x_dims.size(); ++i) {
    xshape_dims[i + 1] = x_dims[i];
  }
  xshape->set_dims(phi::make_ddim(xshape_dims));
  xshape->share_lod(x);
  ReshapeInferMeta(x, shape, out, config);
1241 1242
}

1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269
void ReverseInferMeta(const MetaTensor& x,
                      const std::vector<int>& axis,
                      MetaTensor* out) {
  PADDLE_ENFORCE_NE(axis.empty(),
                    true,
                    phi::errors::InvalidArgument("'axis' can not be empty."));
  const auto& x_dims = x.dims();
  for (int a : axis) {
    PADDLE_ENFORCE_LT(a,
                      x_dims.size(),
                      phi::errors::OutOfRange(
                          "The axis must be less than input tensor's rank. "
                          "but got %d >= %d",
                          a,
                          x_dims.size()));
    PADDLE_ENFORCE_GE(
        a,
        -x_dims.size(),
        phi::errors::OutOfRange(
            "The axis must be greater than the negative number of "
            "input tensor's rank, but got %d < %d",
            a,
            -x_dims.size()));
  }
  out->share_meta(x);
}

C
chenenquan 已提交
1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300
void RollInferMeta(const MetaTensor& x,
                   const ScalarArray& shifts,
                   const std::vector<int64_t>& axis,
                   MetaTensor* out) {
  auto shifts_data = shifts.GetData();

  if (axis.size() != 0) {
    PADDLE_ENFORCE_EQ(
        axis.size(),
        shifts_data.size(),
        phi::errors::InvalidArgument("When dims.size() != 0, dims.size() "
                                     "should be equal to "
                                     "shifts.size(). But received "
                                     "dims.size() = %d, shifts.size() = %d",
                                     axis.size(),
                                     shifts_data.size()));
  } else {
    PADDLE_ENFORCE_EQ(
        shifts_data.size(),
        1,
        phi::errors::InvalidArgument("When dims.size() == 0, shifts.size() "
                                     "should be equal to 1, But received "
                                     "shifts.size() = %d",
                                     shifts_data.size()));
  }

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

1301 1302 1303 1304 1305 1306 1307 1308 1309 1310
void SetValueInferMeta(const MetaTensor& x, MetaTensor* out) {
  auto in_dims = x.dims();
  PADDLE_ENFORCE_LT(
      in_dims.size(),
      7,
      phi::errors::InvalidArgument(
          "The rank of input should be less than 7, but received %d.",
          in_dims.size()));
}

1311 1312 1313 1314 1315 1316
void ShapeInferMeta(const MetaTensor& input, MetaTensor* out) {
  auto in_dim = input.dims();
  out->set_dims(phi::make_ddim({in_dim.size()}));
  out->set_dtype(DataType::INT32);
}

Z
zyfncg 已提交
1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379
void ShardIndexInferMeta(const MetaTensor& in,
                         int index_num,
                         int nshards,
                         int shard_id,
                         int ignore_value,
                         MetaTensor* out,
                         MetaConfig config) {
  auto x_dims = in.dims();
  PADDLE_ENFORCE_GE(
      x_dims.size(),
      2,
      phi::errors::InvalidArgument("Rank of Input(X) should be at least 2, "
                                   "but the value given is %d.",
                                   x_dims.size()));
  if (config.is_runtime || x_dims[x_dims.size() - 1] > 0) {
    PADDLE_ENFORCE_EQ(x_dims[x_dims.size() - 1],
                      1U,
                      phi::errors::InvalidArgument(
                          "The last dimension of Input(X) should be 1, "
                          "but the value given is %d.",
                          x_dims[x_dims.size() - 1]));
  }

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

void SizeInferMeta(const MetaTensor& input, MetaTensor* out) {
  out->set_dtype(DataType::INT64);
  out->set_dims({1});
}

void SoftmaxInferMeta(const MetaTensor& x, int axis, MetaTensor* out) {
  auto dim_x = x.dims();
  auto rank_x = dim_x.size();
  PADDLE_ENFORCE_GE(axis,
                    -rank_x,
                    phi::errors::InvalidArgument(
                        "Attr(axis) value should be in range [-R, R-1], "
                        "R is the rank of Input(X)."));
  PADDLE_ENFORCE_LT(axis,
                    rank_x,
                    phi::errors::InvalidArgument(
                        "Attr(axis) value should be in range [-R, R-1], "
                        "R is the rank of Input(X)."));

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

void SplitInferMeta(const MetaTensor& x,
                    const ScalarArray& num_or_sections,
                    const Scalar& axis,
                    std::vector<MetaTensor*> out,
                    MetaConfig config) {
  int axis_value = axis.to<int>();
  int rank = x.dims().size();
  PADDLE_ENFORCE_EQ(
      axis_value >= -rank && axis_value < rank,
      true,
      phi::errors::InvalidArgument(
C
chentianyu03 已提交
1380 1381 1382 1383 1384 1385 1386 1387 1388 1389
          "The axis is expected to be in range of [%d, %d), but got %d",
          -rank,
          rank,
          axis_value));
  if (axis_value < 0) {
    axis_value = axis_value + rank;
  }

  auto input_axis_dim = x.dims().at(axis_value);
  auto num_or_sections_data = num_or_sections.GetData();
1390 1391
  // step1: get formated sections
  std::vector<int64_t> sections;
C
chentianyu03 已提交
1392 1393
  // num_or_sections is a number
  if (num_or_sections_data.size() == 1) {
1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
    int num = num_or_sections_data.at(0);

    PADDLE_ENFORCE_EQ(input_axis_dim % num,
                      0,
                      phi::errors::InvalidArgument(
                          "The input's size along the split dimension "
                          "must be evenly divisible by Attr(num_or_sections). "
                          "But received Attr(num_or_sections) "
                          "= %d, input(X)'s shape = [%s], Attr(dim) = %d.",
                          num,
                          x.dims(),
                          axis_value));

    for (int i = 0; i < num; ++i) {
      sections.push_back(input_axis_dim / num);
C
chentianyu03 已提交
1409 1410 1411 1412 1413 1414 1415 1416 1417
    }
  } else {
    // num_or_sections is a sections
    const int unknow_dim_val = -1;
    int unknow_dim_idx = -1;
    int num_of_unknow = 0;
    int sum_of_section = 0;

    for (size_t i = 0; i < num_or_sections_data.size(); ++i) {
1418 1419
      sections.push_back(num_or_sections_data[i]);

C
chentianyu03 已提交
1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430
      if (num_or_sections_data[i] == unknow_dim_val) {
        num_of_unknow++;
        unknow_dim_idx = i;
      } else {
        sum_of_section += num_or_sections_data[i];
      }
    }

    if (config.is_runtime) {
      PADDLE_ENFORCE_LE(num_of_unknow,
                        1,
1431
                        phi::errors::InvalidArgument(
C
chentianyu03 已提交
1432 1433 1434
                            "Only one dimension value of Attr(num_or_sections) "
                            "in SplitOp can be -1. "
                            "But received Attr(num_or_sections) = [%s].",
1435
                            phi::make_ddim(num_or_sections_data)));
C
chentianyu03 已提交
1436 1437 1438 1439 1440 1441 1442 1443 1444
    }

    if (unknow_dim_idx != -1) {
      // for example, input shape = [4 ,5], axis = 1, sections = [2, 3, -1].
      // input_axis_dim = 5, sum_of_sections = 5.
      // the following check will fail.
      PADDLE_ENFORCE_LT(
          sum_of_section,
          input_axis_dim,
1445
          phi::errors::InvalidArgument(
C
chentianyu03 已提交
1446 1447 1448 1449 1450
              "Sum of Attr(num_or_sections) other than unknown section "
              "must be less than the input's "
              "size "
              "along the split dimension. But received Attr(num_or_sections) "
              "= [%s], input(X)'s shape = [%s], Attr(dim) = %d.",
1451
              phi::make_ddim(num_or_sections_data),
C
chentianyu03 已提交
1452 1453 1454 1455 1456 1457 1458 1459 1460 1461
              x.dims(),
              axis_value));

      if (config.is_runtime) {
        sections[unknow_dim_idx] = input_axis_dim - sum_of_section;
      }
    } else {
      PADDLE_ENFORCE_EQ(
          sum_of_section,
          input_axis_dim,
1462
          phi::errors::InvalidArgument(
C
chentianyu03 已提交
1463 1464 1465 1466
              "Sum of Attr(num_or_sections) must be equal to the input's "
              "size "
              "along the split dimension. But received Attr(num_or_sections)"
              " = [%s], input(X)'s shape = [%s], Attr(dim) = %d.",
1467
              phi::make_ddim(num_or_sections_data),
C
chentianyu03 已提交
1468 1469 1470
              x.dims(),
              axis_value));
    }
1471 1472 1473 1474 1475 1476
  }

  // setp2: fill out dims
  std::vector<phi::DDim> out_dims(sections.size(), x.dims());
  if (config.is_runtime || input_axis_dim > 0) {
    for (size_t i = 0; i < sections.size(); ++i) {
C
chentianyu03 已提交
1477 1478
      out_dims[i][axis_value] = sections[i];
    }
1479 1480 1481 1482
  } else {
    for (size_t i = 0; i < sections.size(); ++i) {
      out_dims[i][axis_value] = -1;
    }
C
chentianyu03 已提交
1483 1484
  }

1485
  for (size_t i = 0; i < sections.size(); ++i) {
C
chentianyu03 已提交
1486 1487
    if (axis_value != 0) {
      // Only pass LoD when not spliting along the first dim.
1488 1489 1490
      out[i]->set_dtype(x.dtype());
      out[i]->set_dims(out_dims[i]);
      out[i]->set_layout(x.layout());
C
chentianyu03 已提交
1491
    } else {
1492 1493 1494 1495
      out[i]->set_dtype(x.dtype());
      out[i]->set_dims(out_dims[i]);
      out[i]->set_layout(x.layout());
      out[i]->share_lod(x);
C
chentianyu03 已提交
1496 1497
    }
  }
C
Chen Weihang 已提交
1498 1499
}

Z
zyfncg 已提交
1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529
/*  Why not use SumRawInferMeta directly?
    Because we need make InferMetaFunction's args follow the design of api.yaml
*/
void SumInferMeta(const MetaTensor& x,
                  const std::vector<int64_t>& axis,
                  DataType dtype,
                  bool keep_dim,
                  MetaTensor* out) {
  bool reduce_all = false;
  SumRawInferMeta(x, axis, keep_dim, reduce_all, dtype, out);
}

void SumRawInferMeta(const MetaTensor& x,
                     const std::vector<int64_t>& axis,
                     bool keep_dim,
                     bool reduce_all,
                     DataType dtype,
                     MetaTensor* out) {
  DDim out_dim = ReduceInferDim(x, axis, keep_dim, reduce_all);

  DataType out_dtype;
  if (dtype != DataType::UNDEFINED) {
    out_dtype = dtype;
  } else {
    if (x.dtype() == DataType::BOOL || x.dtype() == DataType::INT32 ||
        x.dtype() == DataType::INT64) {
      out_dtype = DataType::INT64;
    } else {
      out_dtype = x.dtype();
    }
L
Leo Chen 已提交
1530 1531
  }

Z
zyfncg 已提交
1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601
  out->set_dims(out_dim);
  out->set_dtype(out_dtype);
  out->set_layout(x.layout());
}

void TileInferMeta(const MetaTensor& x,
                   const ScalarArray& repeat_times,
                   MetaTensor* out,
                   MetaConfig config) {
#define MAX_RANK_SUPPORTED 6

  auto repeat_times_data = repeat_times.GetData();
  auto x_dims = x.dims();
  if (repeat_times_data.size() == 0) {
    repeat_times_data = std::vector<int64_t>(x_dims.size(), -1);
  }

  PADDLE_ENFORCE_LE(
      x_dims.size(),
      MAX_RANK_SUPPORTED,
      errors::InvalidArgument(
          "The rank of the input 'x' for tile op "
          "must not be greater than %d, but the value received is %d.",
          MAX_RANK_SUPPORTED,
          x_dims.size()));
  PADDLE_ENFORCE_LE(
      repeat_times_data.size(),
      MAX_RANK_SUPPORTED,
      errors::InvalidArgument(
          "The size of the shape of input 'repeat_times' for tile op "
          "must not be greater than %d, but the value received is %d.",
          MAX_RANK_SUPPORTED,
          repeat_times_data.size()));
  PADDLE_ENFORCE_GE(
      repeat_times_data.size(),
      1,
      errors::InvalidArgument(
          "The size of the shape of input 'repeat_times' for tile op "
          "must be positive integers, but the value received is %d.",
          repeat_times_data.size()));

  auto out_rank =
      std::max(static_cast<size_t>(x_dims.size()), repeat_times_data.size());
  std::vector<int64_t> out_shape(out_rank);
  auto x_dim_vec = phi::vectorize<int>(x_dims);
  if (x_dim_vec.size() > repeat_times_data.size()) {
    auto diff = x_dim_vec.size() - repeat_times_data.size();
    repeat_times_data.insert(repeat_times_data.begin(), diff, -1);
  } else {
    auto diff = repeat_times_data.size() - x_dim_vec.size();
    x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
  }
  for (size_t i = 0; i < repeat_times_data.size(); ++i) {
    if (x_dim_vec[i] == -1 || repeat_times_data[i] == -1) {
      out_shape[i] = -1;
    } else {
      PADDLE_ENFORCE_GT(
          repeat_times_data[i],
          0,
          errors::InvalidArgument(
              "Every element of the input 'repeat_times' for tile op must be "
              "greater than 0, but the value given is %d.",
              repeat_times_data[i]));
      out_shape[i] = x_dim_vec[i] * repeat_times_data[i];
    }
  }

  out->set_dims(phi::make_ddim(out_shape));
  if (out_shape[0] == x_dims[0]) {
    out->share_lod(x);
L
Leo Chen 已提交
1602 1603 1604
  }
}

1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653
void TopKInferMeta(const MetaTensor& x,
                   const Scalar& k_scalar,
                   int axis,
                   bool largest,
                   bool sorted,
                   MetaTensor* out,
                   MetaTensor* indices,
                   MetaConfig config) {
  auto input_dims = x.dims();
  const int& dim_size = input_dims.size();
  PADDLE_ENFORCE_EQ(
      (axis < dim_size) && (axis >= (-1 * dim_size)),
      true,
      phi::errors::InvalidArgument(
          "the axis of topk must be [-%d, %d), but you set axis is %d",
          dim_size,
          dim_size,
          axis));

  if (axis < 0) axis += dim_size;

  int k = k_scalar.to<int>();
  if (k_scalar.FromTensor()) {
    k = -1;
  } else {
    PADDLE_ENFORCE_EQ(k >= 1,
                      true,
                      phi::errors::InvalidArgument(
                          "the attribute of k in the topk must >= 1 or be a "
                          "Tensor, but received %d .",
                          k));
  }

  PADDLE_ENFORCE_GE(
      input_dims.size(),
      1,
      phi::errors::InvalidArgument("input of topk must have >= 1d shape"));

  phi::DDim dims = input_dims;

  dims[axis] = k;
  out->set_dims(dims);
  out->share_lod(x);
  out->set_dtype(x.dtype());
  indices->set_dims(dims);
  indices->share_lod(x);
  indices->set_dtype(DataType::INT64);
}

C
Chen Weihang 已提交
1654 1655 1656 1657 1658 1659
void TraceInferMeta(
    const MetaTensor& x, int offset, int axis1, int axis2, MetaTensor* out) {
  int dim1 = axis1;
  int dim2 = axis2;

  auto x_dims = x.dims();
C
chentianyu03 已提交
1660

C
Chen Weihang 已提交
1661 1662 1663 1664 1665 1666
  int dim1_ = dim1 < 0 ? x_dims.size() + dim1 : dim1;
  int dim2_ = dim2 < 0 ? x_dims.size() + dim2 : dim2;

  PADDLE_ENFORCE_GE(
      x_dims.size(),
      2,
1667
      phi::errors::OutOfRange(
C
Chen Weihang 已提交
1668 1669 1670 1671 1672
          "Input's dim is out of range (expected at least 2, but got %ld).",
          x_dims.size()));
  PADDLE_ENFORCE_LT(
      dim1_,
      x_dims.size(),
1673
      phi::errors::OutOfRange(
C
Chen Weihang 已提交
1674 1675 1676 1677 1678 1679 1680 1681
          "Attr(dim1) is out of range (expected to be in range of [%ld, "
          "%ld], but got %ld).",
          -(x_dims.size()),
          (x_dims.size() - 1),
          dim1));
  PADDLE_ENFORCE_LT(
      dim2_,
      x_dims.size(),
1682
      phi::errors::OutOfRange(
C
Chen Weihang 已提交
1683 1684 1685 1686 1687 1688 1689 1690
          "Attr(dim2) is out of range (expected to be in range of [%ld, "
          "%ld], but got %ld).",
          -(x_dims.size()),
          (x_dims.size() - 1),
          dim2));
  PADDLE_ENFORCE_NE(
      dim1_,
      dim2_,
1691 1692 1693 1694
      phi::errors::InvalidArgument("The dimensions should not be identical "
                                   "%ld vs %ld.",
                                   dim1,
                                   dim2));
C
Chen Weihang 已提交
1695 1696 1697 1698 1699 1700 1701 1702 1703

  auto sizes = vectorize(x_dims);
  if (x_dims.size() == 2) {
    sizes.clear();
    sizes.push_back(1);
  } else {
    sizes.erase(sizes.begin() + std::max(dim1_, dim2_));
    sizes.erase(sizes.begin() + std::min(dim1_, dim2_));
  }
1704
  out->set_dims(phi::make_ddim(sizes));
C
Chen Weihang 已提交
1705
  out->set_dtype(x.dtype());
C
chentianyu03 已提交
1706 1707
}

Z
zyfncg 已提交
1708 1709 1710 1711 1712 1713 1714
void TransferLayoutInferMeta(const MetaTensor& x,
                             DataLayout layout,
                             MetaTensor* out) {
  out->set_dims(x.dims());
  out->set_dtype(x.dtype());
  out->set_layout(layout);
}
H
hong 已提交
1715

Z
zyfncg 已提交
1716 1717 1718 1719 1720 1721
void TransposeInferMeta(const MetaTensor& x,
                        const std::vector<int>& axis,
                        MetaTensor* out) {
  auto x_dims = x.dims();
  size_t x_rank = x_dims.size();
  size_t axis_size = axis.size();
H
hong 已提交
1722

Z
zyfncg 已提交
1723 1724 1725 1726 1727 1728 1729 1730 1731
  PADDLE_ENFORCE_EQ(
      x_rank,
      axis_size,
      errors::InvalidArgument("The input tensor's dimension "
                              "should be equal to the axis's size. "
                              "But received input tensor's dimension is %d, "
                              "axis's size is %d",
                              x_rank,
                              axis_size));
H
hong 已提交
1732

Z
zyfncg 已提交
1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757
  std::vector<int> count(axis_size, 0);
  for (size_t i = 0; i < axis_size; i++) {
    PADDLE_ENFORCE_GE(
        axis[i],
        0,
        errors::InvalidArgument("The axis should be greater than or equal to 0."
                                "But received %d of axis[%d]",
                                axis[i],
                                i));

    PADDLE_ENFORCE_EQ(
        axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1,
        true,
        errors::InvalidArgument(
            "Each element of Attribute axis should "
            "be a unique value range from 0 to (dims - 1), "
            "where the dims is the axis's size, "
            "unique value means this axis value can appear only once. "
            "But received axis[%d] is %d, axis_size is %d, "
            "count[axis[%d]] is %d",
            i,
            axis[i],
            axis_size,
            i,
            count[axis[i]]));
H
hong 已提交
1758
  }
Z
zyfncg 已提交
1759 1760 1761 1762 1763 1764 1765 1766 1767 1768

  phi::DDim out_dims(x_dims);
  for (size_t i = 0; i < axis_size; ++i) {
    out_dims[i] = x_dims[axis[i]];
  }

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

H
hong 已提交
1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779
void TransposeGradInferMeta(const MetaTensor& x,
                            const std::vector<int>& axis,
                            MetaTensor* out) {
  std::vector<int> reversed_axis(axis);
  for (size_t i = 0; i < axis.size(); i++) {
    reversed_axis[axis[i]] = i;
  }

  TransposeInferMeta(x, reversed_axis, out);
}

Z
zyfncg 已提交
1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810
void UnbindInferMeta(const MetaTensor& x,
                     int axis,
                     std::vector<MetaTensor>* outs) {
  auto in_dims = x.dims();
  std::vector<int> out_dim;
  axis = axis < 0 ? in_dims.size() + axis : axis;
  for (int i = 0; i < in_dims.size(); ++i) {
    if (i != axis) out_dim.push_back(in_dims[i]);
  }
  auto out_dims = phi::make_ddim(out_dim);

  for (size_t i = 0; i < outs->size(); ++i) {
    (*outs)[i].set_dtype(x.dtype());
    (*outs)[i].set_dims(out_dims);
    (*outs)[i].set_layout(x.layout());
    (*outs)[i].share_lod(x);
  }
}

void UnchangedInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->share_meta(x);
}

// meta x -> out without change, check if axis in range [-Rank(x), Rank(x)-1]
void UnchangedInferMetaCheckAxis(const MetaTensor& x,
                                 int axis,
                                 MetaTensor* out) {
  auto rank = x.dims().size();
  PADDLE_ENFORCE_GE(
      axis,
      -rank,
1811
      phi::errors::InvalidArgument(
Z
zyfncg 已提交
1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
          "Attr(axis) value should be in range [-R, R-1], "
          "R is the rank of Input(X). But received axis: %d, R: %d.",
          axis,
          rank));
  PADDLE_ENFORCE_LT(
      axis,
      rank,
      phi::errors::InvalidArgument(
          "Attr(axis) value should be in range [-R, R-1], "
          "R is the rank of Input(X). But received axis: %d, R: %d.",
          axis,
          rank));
  out->share_meta(x);
H
hong 已提交
1825 1826
}

1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
void UnfoldInferMeta(const MetaTensor& x,
                     const std::vector<int>& kernel_sizes,
                     const std::vector<int>& strides,
                     const std::vector<int>& paddings,
                     const std::vector<int>& dilations,
                     MetaTensor* out,
                     MetaConfig config) {
  auto in_dims = x.dims();
  // Only [N, C, H, W] input supported now
  PADDLE_ENFORCE_EQ(
      in_dims.size(),
      4,
      phi::errors::InvalidArgument(
          "Input should be 4-D tensor of format [N, C, H, W], but get %u",
          in_dims.size()));
  PADDLE_ENFORCE_EQ(
      in_dims.size() - kernel_sizes.size(),
      2U,
      phi::errors::InvalidArgument(
          "The dims of X should be larger than that of kernel_sizes "
          "by a number of 2, due to the batch size and input channel dim. "
          "But recieved dims(X:%u) - dims(kernel_sizes:%u) != 2",
          in_dims.size(),
          kernel_sizes.size()));
  PADDLE_ENFORCE_EQ(
      strides.size(),
      kernel_sizes.size(),
      phi::errors::InvalidArgument(
          "The dims of strides should be the same with that of kernel_sizes. "
          "But recieved dims(strides: %u) != dims(kernel_sizes: %u).",
          strides.size(),
          kernel_sizes.size()));
  PADDLE_ENFORCE_EQ(
      paddings.size(),
      2 * strides.size(),
      phi::errors::InvalidArgument(
          "The dims of paddings should be 2 times of that of strides. "
          "But recieved dims(paddings: %u) != 2*dims(strides: %u).",
          paddings.size(),
          strides.size()));
  PADDLE_ENFORCE_EQ(
      strides.size(),
      dilations.size(),
      phi::errors::InvalidArgument(
          "The dims of strides should be the same with that of dilations. "
          "But recieved dims(strides: %u) != dims(dilations: %u).",
          strides.size(),
          dilations.size()));

  // check kernel_sizes
  PADDLE_ENFORCE_GT(kernel_sizes[0],
                    0,
                    phi::errors::InvalidArgument(
                        "The `kernel_sizes` should be greater than zero, "
                        "but recieved kernel_height: %d kernel_width: %d.",
                        kernel_sizes[0],
                        kernel_sizes[1]));
  PADDLE_ENFORCE_GT(kernel_sizes[1],
                    0,
                    phi::errors::InvalidArgument(
                        "The `kernel_sizes` should be greater than zero, "
                        "but recieved kernel_height: %d kernel_width: %d.",
                        kernel_sizes[0],
                        kernel_sizes[1]));
  // check strides
  PADDLE_ENFORCE_GT(strides[0],
                    0,
                    phi::errors::InvalidArgument(
                        "The `strides` should be greater than zero, "
                        "but recieved strides_height: %d strides_width: %d.",
                        strides[0],
                        strides[1]));
  PADDLE_ENFORCE_GT(strides[1],
                    0,
                    phi::errors::InvalidArgument(
                        "The `strides` should be greater than zero, "
                        "but recieved strides_height: %d strides_width: %d.",
                        strides[0],
                        strides[1]));
  // check dilations
  PADDLE_ENFORCE_GT(
      dilations[0],
      0,
      phi::errors::InvalidArgument(
          "The `dilations` should be greater than zero, "
          "but recieved dilations_height: %d dilations_width: %d.",
          dilations[0],
          dilations[1]));
  PADDLE_ENFORCE_GT(
      dilations[1],
      0,
      phi::errors::InvalidArgument(
          "The `dilations` should be greater than zero, "
          "but recieved dilations_height: %d dilations_width: %d.",
          dilations[0],
          dilations[1]));

  std::vector<int> out_dims;
  out_dims.push_back(in_dims[0]);
  int output_channels = in_dims[1] * kernel_sizes[0] * kernel_sizes[1];
  out_dims.push_back(output_channels);

  int output_height = phi::funcs::CalcOutputSize(in_dims[2],
                                                 kernel_sizes[0],
                                                 dilations[0],
                                                 paddings[0],
                                                 paddings[2],
                                                 strides[0]);
  int output_width = phi::funcs::CalcOutputSize(in_dims[3],
                                                kernel_sizes[1],
                                                dilations[1],
                                                paddings[1],
                                                paddings[3],
                                                strides[1]);
  if (config.is_runtime) {
    // only check output height and width in runtime
    PADDLE_ENFORCE_GT(
        output_height,
        0,
        phi::errors::InvalidArgument(
            "The sliding blocks calculated from input spatial size "
            "(%d, %d), kernel_sizes (%d, %d), strides (%d, %d), "
            "dilations (%d, %d), is (%d, %d), which should be a "
            "positive integer.",
            in_dims[2],
            in_dims[3],
            kernel_sizes[0],
            kernel_sizes[1],
            strides[0],
            strides[1],
            dilations[0],
            dilations[1],
            output_height,
            output_width));
    PADDLE_ENFORCE_GT(
        output_width,
        0,
        phi::errors::InvalidArgument(
            "The sliding blocks calculated from input spatial size "
            "(%d, %d), kernel_sizes (%d, %d), strides (%d, %d), "
            "dilations (%d, %d), is (%d, %d), which should be a "
            "positive integer.",
            in_dims[2],
            in_dims[3],
            kernel_sizes[0],
            kernel_sizes[1],
            strides[0],
            strides[1],
            dilations[0],
            dilations[1],
            output_height,
            output_width));
  }
  int output_col_length = output_height * output_width;
  out_dims.push_back(output_col_length);
  out->set_dims(phi::make_ddim(out_dims));
}

H
hong 已提交
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
void OneHotRawInferMeta(const MetaTensor& x,
                        int32_t depth,
                        DataType dtype,
                        bool allow_out_of_range,
                        MetaTensor* out) {
  auto x_dims = x.dims();
  PADDLE_ENFORCE_GE(
      x_dims.size(),
      1,
      phi::errors::InvalidArgument("Rank of Input(X) should be at least 1."));

  auto out_dims_vec = phi::vectorize(x_dims);
  out_dims_vec.push_back(depth);
  auto out_dims = phi::make_ddim(out_dims_vec);
  out->set_dims(out_dims);
  out->share_lod(x);
  out->set_dtype(dtype);
}

void OneHotInferMeta(const MetaTensor& x,
                     const Scalar& depth_t,
                     MetaTensor* out) {
  auto x_dims = x.dims();
  PADDLE_ENFORCE_GE(
      x_dims.size(),
      1,
      phi::errors::InvalidArgument("Rank of Input(X) should be at least 1."));

  int depth = depth_t.to<int>();
  auto out_dims_vec = phi::vectorize(x_dims);
  out_dims_vec.push_back(depth);
  auto out_dims = phi::make_ddim(out_dims_vec);
  out->set_dims(out_dims);
  out->share_lod(x);
H
hong 已提交
2019

H
hong 已提交
2020 2021 2022
  out->set_dtype(phi::DataType::FLOAT32);
}

2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033
void WhereIndexInferMeta(const MetaTensor& condition, MetaTensor* out) {
  auto rank = condition.dims().size();
  PADDLE_ENFORCE_GE(
      rank,
      1UL,
      phi::errors::InvalidArgument(
          "Input(Condition) should have number of dimension at least 1"));
  out->set_dims(phi::make_ddim({-1, rank}));
  out->set_dtype(DataType::INT64);
}

2034
}  // namespace phi
2035

2036 2037
PD_REGISTER_INFER_META_FN(copy_to, phi::CopyToInferMeta);
PD_REGISTER_INFER_META_FN(split, phi::SplitInferMeta);