unary.cc 54.2 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/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
222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461
    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));
}

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);
}

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]);
462 463 464 465 466 467 468 469 470 471 472 473 474
    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,
475
          phi::errors::InvalidArgument(
476 477 478 479 480 481 482
              "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,
483
              phi::make_ddim(shape),
484 485 486 487 488 489 490 491 492
              capacity));
    } else {
      output_shape[unk_dim_idx] = -1;
    }
  } else {
    if (all_positive) {
      PADDLE_ENFORCE_EQ(
          capacity,
          in_size,
493
          phi::errors::InvalidArgument(
494 495 496 497 498 499 500
              "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,
501
              phi::make_ddim(shape),
502 503 504 505 506 507 508 509 510 511 512
              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,
513
        phi::errors::InvalidArgument(
514 515 516 517 518 519
            "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,
520
            phi::make_ddim(shape),
521 522 523
            capacity));
  }

524
  return phi::make_ddim(output_shape);
525 526
}

527 528 529
void InferMetaFromVecValue(const MetaTensor& x,
                           const std::vector<int64_t>& shape,
                           MetaTensor* out) {
530 531
  PADDLE_ENFORCE_EQ(!shape.empty(),
                    true,
532
                    phi::errors::InvalidArgument(
533 534
                        "The parameter 'shape' in ReshapeOp must be set. "
                        "But received 'shape' is empty."));
535
  auto x_dims = x.dims();
536
  auto out_dims = ValidateShape(shape, x_dims);
537 538 539 540
  out->set_dims(out_dims);
  out->set_dtype(x.dtype());
  out->set_layout(x.layout());
  if (x_dims[0] == out_dims[0]) {
541 542
    // Only pass LoD when the first dimension of output and Input(X)
    // are the same.
543
    out->share_lod(x);
544 545 546
  }
}

W
WJJ1995 已提交
547 548 549 550 551
void IsEmptyInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->set_dims(phi::make_ddim({1}));
  out->set_dtype(DataType::BOOL);
}

Z
zyfncg 已提交
552 553 554 555 556
void IsfiniteInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->set_dims(x.dims());
  out->set_dtype(DataType::BOOL);
}

F
From00 已提交
557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628
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());
}

629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663
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);
}

Z
zyfncg 已提交
664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686
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)));
687
  }
Z
zyfncg 已提交
688 689 690 691
  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;
692
    } else {
Z
zyfncg 已提交
693
      out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
694 695
    }
  }
Z
zyfncg 已提交
696 697 698 699 700
  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);
701
  }
Z
zyfncg 已提交
702
  out->set_dtype(input.dtype());
703 704
}

Z
zyfncg 已提交
705 706 707 708 709 710 711
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,
712
                    phi::errors::InvalidArgument(
Z
zyfncg 已提交
713 714 715
                        "Input should be a 4-D tensor of format [N, C, H, W] "
                        "or [N, H, W, C], but got %u.",
                        input_dims.size()));
716

Z
zyfncg 已提交
717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734
  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]));
735
  }
Z
zyfncg 已提交
736 737 738 739 740 741 742 743 744 745 746 747 748
  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);
749 750
}

F
From00 已提交
751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862
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
863 864 865 866
void RealAndImagInferMeta(const MetaTensor& x, MetaTensor* out) {
  out->set_dims(x.dims());
  out->set_dtype(dtype::ToReal(x.dtype()));
  out->set_layout(x.layout());
867 868
}

869 870 871 872
DDim ReduceInferDim(const MetaTensor& x,
                    const std::vector<int64_t>& axis,
                    bool keep_dim,
                    bool reduce_all) {
873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902
  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());
903
  for (int64_t i = 0; i < x.dims().size(); ++i) {
904
    if (dims_set.find(i) == dims_set.end()) {
905
      full_dim = false;
906 907 908
      break;
    }
  }
909
  reduce_all = reduce_all || full_dim;
910 911 912

  std::vector<int64_t> out_dim_vector;
  if (keep_dim) {
913
    for (int64_t i = 0; i < x.dims().size(); ++i) {
914 915 916
      if (reduce_all || dims_set.find(i) != dims_set.end()) {
        out_dim_vector.push_back(1);
      } else {
917
        out_dim_vector.push_back(x.dims().at(i));
918 919 920
      }
    }
  } else {
921
    for (int64_t i = 0; i < x.dims().size(); ++i) {
922 923 924
      if (reduce_all || dims_set.find(i) != dims_set.end()) {
        continue;
      } else {
925
        out_dim_vector.push_back(x.dims().at(i));
926 927 928 929 930 931 932
      }
    }

    if (out_dim_vector.size() == 0) {
      out_dim_vector.push_back(1);
    }
  }
933
  DDim out_dim = phi::make_ddim(out_dim_vector);
934

935 936 937
  return out_dim;
}

Z
zyfncg 已提交
938
void ReduceInferMeta(const MetaTensor& x,
939 940 941
                     const std::vector<int64_t>& axis,
                     bool keep_dim,
                     MetaTensor* out) {
Z
zyfncg 已提交
942 943
  bool reduce_all = false;
  ReduceInferMetaBase(x, axis, keep_dim, reduce_all, out);
944 945
}

946 947 948 949 950 951 952 953 954
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());
955 956
}

Z
zyfncg 已提交
957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974
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);
975 976
}

Z
zyfncg 已提交
977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994
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);
995 996
}

Z
zyfncg 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
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
1060 1061 1062 1063 1064 1065 1066 1067 1068 1069
          "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();
1070 1071
  // step1: get formated sections
  std::vector<int64_t> sections;
C
chentianyu03 已提交
1072 1073
  // num_or_sections is a number
  if (num_or_sections_data.size() == 1) {
1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
    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
1089 1090 1091 1092 1093 1094 1095 1096 1097
    }
  } 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) {
1098 1099
      sections.push_back(num_or_sections_data[i]);

C
chentianyu03 已提交
1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110
      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,
1111
                        phi::errors::InvalidArgument(
C
chentianyu03 已提交
1112 1113 1114
                            "Only one dimension value of Attr(num_or_sections) "
                            "in SplitOp can be -1. "
                            "But received Attr(num_or_sections) = [%s].",
1115
                            phi::make_ddim(num_or_sections_data)));
C
chentianyu03 已提交
1116 1117 1118 1119 1120 1121 1122 1123 1124
    }

    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,
1125
          phi::errors::InvalidArgument(
C
chentianyu03 已提交
1126 1127 1128 1129 1130
              "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.",
1131
              phi::make_ddim(num_or_sections_data),
C
chentianyu03 已提交
1132 1133 1134 1135 1136 1137 1138 1139 1140 1141
              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,
1142
          phi::errors::InvalidArgument(
C
chentianyu03 已提交
1143 1144 1145 1146
              "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.",
1147
              phi::make_ddim(num_or_sections_data),
C
chentianyu03 已提交
1148 1149 1150
              x.dims(),
              axis_value));
    }
1151 1152 1153 1154 1155 1156
  }

  // 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
1157 1158
      out_dims[i][axis_value] = sections[i];
    }
1159 1160 1161 1162
  } else {
    for (size_t i = 0; i < sections.size(); ++i) {
      out_dims[i][axis_value] = -1;
    }
C
chentianyu03 已提交
1163 1164
  }

1165
  for (size_t i = 0; i < sections.size(); ++i) {
C
chentianyu03 已提交
1166 1167
    if (axis_value != 0) {
      // Only pass LoD when not spliting along the first dim.
1168 1169 1170
      out[i]->set_dtype(x.dtype());
      out[i]->set_dims(out_dims[i]);
      out[i]->set_layout(x.layout());
C
chentianyu03 已提交
1171
    } else {
1172 1173 1174 1175
      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
1176 1177
    }
  }
C
Chen Weihang 已提交
1178 1179
}

Z
zyfncg 已提交
1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209
/*  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
1210 1211
  }

Z
zyfncg 已提交
1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 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 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281
  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
1282 1283 1284
  }
}

C
Chen Weihang 已提交
1285 1286 1287 1288 1289 1290
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
1291

C
Chen Weihang 已提交
1292 1293 1294 1295 1296 1297
  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,
1298
      phi::errors::OutOfRange(
C
Chen Weihang 已提交
1299 1300 1301 1302 1303
          "Input's dim is out of range (expected at least 2, but got %ld).",
          x_dims.size()));
  PADDLE_ENFORCE_LT(
      dim1_,
      x_dims.size(),
1304
      phi::errors::OutOfRange(
C
Chen Weihang 已提交
1305 1306 1307 1308 1309 1310 1311 1312
          "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(),
1313
      phi::errors::OutOfRange(
C
Chen Weihang 已提交
1314 1315 1316 1317 1318 1319 1320 1321
          "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_,
1322 1323 1324 1325
      phi::errors::InvalidArgument("The dimensions should not be identical "
                                   "%ld vs %ld.",
                                   dim1,
                                   dim2));
C
Chen Weihang 已提交
1326 1327 1328 1329 1330 1331 1332 1333 1334

  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_));
  }
1335
  out->set_dims(phi::make_ddim(sizes));
C
Chen Weihang 已提交
1336
  out->set_dtype(x.dtype());
C
chentianyu03 已提交
1337 1338
}

Z
zyfncg 已提交
1339 1340 1341 1342 1343 1344 1345
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
1346

Z
zyfncg 已提交
1347 1348 1349 1350 1351 1352
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
1353

Z
zyfncg 已提交
1354 1355 1356 1357 1358 1359 1360 1361 1362
  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
1363

Z
zyfncg 已提交
1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388
  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
1389
  }
Z
zyfncg 已提交
1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444

  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());
}

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,
      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));
  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
1445 1446
}

1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 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 1530 1531 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 1602 1603 1604
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));
}

1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615
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);
}

1616
}  // namespace phi
1617

1618 1619
PD_REGISTER_INFER_META_FN(copy_to, phi::CopyToInferMeta);
PD_REGISTER_INFER_META_FN(split, phi::SplitInferMeta);