common_shape.h

/* 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. */

#pragma once

#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"

namespace phi {
namespace funcs {

inline void SetXShape(const DenseTensor &x, DenseTensor *xshape) {
  const auto &in_dims = x.meta().dims;
  std::vector<int64_t> xshape_dims(in_dims.size() + 1);
  xshape_dims[0] = 0;
  for (int i = 0; i < in_dims.size(); ++i) {
    xshape_dims[i + 1] = in_dims[i];
  }
  xshape->ResizeAndAllocate(make_ddim(xshape_dims));
  xshape->ResetLoD(x.meta().legacy_lod);
}

template <typename T>
inline void GetBroadcastDimsArrays(const DDim &x_dims,
                                   const DDim &y_dims,
                                   T *x_dims_array,
                                   T *y_dims_array,
                                   T *out_dims_array,
                                   const int max_dim,
                                   const int axis) {
  PADDLE_ENFORCE_GE(
      axis,
      0,
      common::errors::InvalidArgument(
          "Axis should be great than or equal to 0, but received axis is %d.",
          axis));
  PADDLE_ENFORCE_LE(
      axis,
      max_dim,
      common::errors::InvalidArgument(
          "Axis should be less than or equal to %d, but received axis is %d.",
          max_dim,
          axis));
  if (x_dims.size() > y_dims.size()) {
    std::fill(y_dims_array, y_dims_array + axis, 1);
    if (axis + y_dims.size() < max_dim) {
      std::fill(y_dims_array + axis + y_dims.size(), y_dims_array + max_dim, 1);
    }
    std::copy(x_dims.Get(), x_dims.Get() + x_dims.size(), x_dims_array);
    std::copy(y_dims.Get(), y_dims.Get() + y_dims.size(), y_dims_array + axis);
  } else {
    std::fill(x_dims_array, x_dims_array + axis, 1);
    if (axis + x_dims.size() < max_dim) {
      std::fill(x_dims_array + axis + x_dims.size(), x_dims_array + max_dim, 1);
    }
    std::copy(x_dims.Get(), x_dims.Get() + x_dims.size(), x_dims_array + axis);
    std::copy(y_dims.Get(), y_dims.Get() + y_dims.size(), y_dims_array);
  }
  for (int i = 0; i < max_dim; ++i) {
    PADDLE_ENFORCE_EQ(
        x_dims_array[i] == y_dims_array[i] ||
            (x_dims_array[i] <= 1 && x_dims_array[i] != 0) ||
            (y_dims_array[i] <= 1 && y_dims_array[i] != 0),
        true,
        common::errors::InvalidArgument(
            "Broadcast dimension mismatch. Operands could "
            "not be broadcast together with the shape of X = [%s] and "
            "the shape of Y = [%s]. Received [%d] in X is not equal to "
            "[%d] in Y at i:%d.",
            x_dims,
            y_dims,
            x_dims_array[i],
            y_dims_array[i],
            i));
    if ((x_dims_array[i] > 1 || y_dims_array[i] > 1) ||
        (x_dims_array[i] == 1 && y_dims_array[i] == 1)) {
      out_dims_array[i] = (std::max)(x_dims_array[i], y_dims_array[i]);
    } else {
      out_dims_array[i] = -1;
      if (y_dims_array[i] == 0 || x_dims_array[i] == 0) {
        out_dims_array[i] = 0;
      }
    }
  }
}

inline void GetPrePostNumel(
    const DDim &dim, int axis, int64_t *pre, int64_t *n, int64_t *post) {
  *pre = 1;
  *post = 1;
  *n = dim[axis];
  for (int i = 0; i < axis; ++i) {
    (*pre) *= dim[i];
  }
  for (int i = axis + 1; i < dim.size(); ++i) {
    (*post) *= dim[i];
  }
}

static DDim ExtendDims2Rank(const DDim &in_dims, int rank) {
  if (in_dims.size() == rank) {
    return in_dims;
  }
  std::vector<int64_t> shapes(rank, 1);
  for (int i = in_dims.size() - 1, j = rank - 1; i >= 0; --i, --j) {
    shapes[j] = in_dims[i];
  }
  return make_ddim(shapes);
}

template <size_t D>
static void GetBroadcastDims(const DDim &in_dims,
                             const DDim &out_dims,
                             Eigen::DSizes<int, D> *bcast_dims) {
  for (size_t i = 0; i < D; ++i) {
    if (in_dims[i] == out_dims[i]) {
      (*bcast_dims)[i] = 1;
    } else {
      // Calculate broadcast factor: out_dims[i] / in_dims[i]
      // in_dims[i] should be 1 for valid broadcasting
      PADDLE_ENFORCE_EQ(
          in_dims[i],
          1,
          common::errors::InvalidArgument(
              "Tensor dimension mismatch for broadcasting. "
              "Input dimension %d is %d, output dimension is %d. "
              "For broadcasting, input dimension must be 1 or equal to output.",
              i,
              in_dims[i],
              out_dims[i]));
      (*bcast_dims)[i] = out_dims[i];
    }
  }
}

inline bool CheckDims(const DDim &dims_x, const DDim &dims_y) {
  if (dims_x.size() != dims_y.size()) {
    return false;
  }
  for (int i = 0; i < dims_x.size(); i++) {
    if (dims_x[i] != dims_y[i]) {
      return false;
    }
  }
  return true;
}

// Just For Matrix OP, for example:
// x's dim = [5, 3, 2, M, M] ; y's dim = [3, 1, M, N]
// out [5, 3, 2], which is batch_size of matrix
static inline std::vector<int64_t> MatrixGetBroadcastBatchPortion(
    std::vector<int64_t> x, std::vector<int64_t> y) {
  size_t size_x = x.size();
  size_t size_y = y.size();
  size_t size = std::max(size_x, size_y);
  std::vector<int64_t> batchPortion(size);

  ptrdiff_t i = (ptrdiff_t)size - 1;
  for (; i >= 0; --i) {
    ptrdiff_t offset = size - i - 1;
    ptrdiff_t dim_x = size_x - offset - 1;
    ptrdiff_t dim_y = size_y - offset - 1;
    int64_t x_size = (dim_x >= 0) ? x[dim_x] : 1;
    int64_t y_size = (dim_y >= 0) ? y[dim_y] : 1;

    PADDLE_ENFORCE_EQ(
        (x_size == y_size || x_size == 1 || y_size == 1),
        true,
        common::errors::PreconditionNotMet(
            "The size of tensor x (%d) must match the size of tensor y "
            "(%d) at non-singleton dimension %d.",
            x_size,
            y_size,
            i));

    batchPortion[i] = x_size != 1 ? x_size : y_size;
  }
  return batchPortion;
}

// Just For Matrix OP, for example:
// x's dim = [5, 3, 2, M, M] ; y's dim = [3, 1, M, N]
// out should be [5, 3, 2, M, M] + [5, 3, 2, M, N], and [5, 3, 2] is
// batch_size of matrix
static inline std::tuple<std::vector<int64_t>, std::vector<int64_t>>
MatrixGetBroadcastDims(const DenseTensor &x, const DenseTensor &y) {
  std::vector<int64_t> x_dims_vec = vectorize(x.dims());
  std::vector<int64_t> y_dims_vec = vectorize(y.dims());

  std::vector<int64_t>::const_iterator f1 = x_dims_vec.begin();
  std::vector<int64_t>::const_iterator l1 = x_dims_vec.end() - 2;
  std::vector<int64_t> x_dims_vec_cut(f1, l1);

  std::vector<int64_t>::const_iterator f2 = y_dims_vec.begin();
  std::vector<int64_t>::const_iterator l2 = y_dims_vec.end() - 2;
  std::vector<int64_t> y_dims_vec_cut(f2, l2);

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

  std::vector<int64_t> x_expand_size({expand_batch_portion});
  x_expand_size.insert(x_expand_size.end(),
                       {x_dims_vec[static_cast<int>(x_dims_vec.size()) - 2],
                        x_dims_vec[static_cast<int>(x_dims_vec.size()) - 1]});

  std::vector<int64_t> y_expand_size({expand_batch_portion});
  y_expand_size.insert(y_expand_size.end(),
                       {y_dims_vec[static_cast<int>(y_dims_vec.size()) - 2],
                        y_dims_vec[static_cast<int>(y_dims_vec.size()) - 1]});

  return std::make_tuple(x_expand_size, y_expand_size);
}

inline DDim GetOutputDims(const DDim &s_dims, const DDim &l_dims) {
  if (s_dims.size() > l_dims.size()) {
    return GetOutputDims(l_dims, s_dims);
  }
  std::vector<int64_t> shapes = vectorize<int64_t>(l_dims);
  for (int i = s_dims.size() - 1, j = l_dims.size() - 1; i >= 0; --i, --j) {
    int64_t s = s_dims[i];
    int64_t l = l_dims[j];
    if (s != l) {
      if (l == 1) {
        shapes[j] = s;
      } else if (s != 1) {
        PADDLE_THROW(errors::InvalidArgument(
            "The shape of tensor a %s:%d must match shape of tensor b "
            "%s:%d.",
            s_dims.to_str(),
            i,
            l_dims.to_str(),
            j));
      }
    }
  }
  return make_ddim(shapes);
}

inline DDim GetOutputDimsForDynamicShape(const DDim &s_dims,
                                         const DDim &l_dims) {
  if (s_dims.size() > l_dims.size()) {
    return GetOutputDimsForDynamicShape(l_dims, s_dims);
  }
  std::vector<int64_t> shapes = vectorize<int64_t>(l_dims);

  for (int i = s_dims.size() - 1, j = l_dims.size() - 1; i >= 0; --i, --j) {
    int64_t s = s_dims[i];
    int64_t l = l_dims[j];
    if (s != l) {
      if (l == 1) {
        shapes[j] = s;
      } else if (s == 1 || s == -1) {
        shapes[j] = l;
      } else if (l == -1) {
        shapes[j] = s;
      } else {
        PADDLE_THROW(errors::InvalidArgument(
            "The shape of tensor a %s:%d must match shape of tensor b "
            "%s:%d.",
            s_dims.to_str(),
            i,
            l_dims.to_str(),
            j));
      }
    }
  }
  return make_ddim(shapes);
}

inline int64_t CalStride(DDim dim) {
  int rank = dim.size();
  int64_t dimsum = 1;
  int64_t strides = 0;
  for (int i = rank - 1; i >= 0; i--) {
    strides += dimsum;
    dimsum *= dim[i];
  }
  return strides;
}

inline std::vector<int32_t> GetPermuteShape(const std::vector<int> &axis,
                                            const DDim &in_dims) {
  std::vector<int32_t> out_dims(in_dims.size());
  for (size_t i = 0; i < axis.size(); i++) {
    out_dims[i] = in_dims[axis[i]];
  }
  return out_dims;
}

inline std::vector<int32_t> GetFlattenShape(const int axis,
                                            const std::vector<int> &in_dims) {
  int64_t outer = 1, inner = 1;
  for (int i = 0; i < static_cast<int>(in_dims.size()); ++i) {
    if (i < axis) {
      outer *= in_dims[i];
    } else {
      inner *= in_dims[i];
    }
  }
  std::vector<int32_t> out_shape(2);
  out_shape[0] = outer;
  out_shape[1] = inner;
  return out_shape;
}

inline void FCOutputSize(const DDim &in_dims,
                         const DDim &w_dims,
                         std::vector<int64_t> &out_dims,  // NOLINT
                         int in_num_col_dims,
                         bool padding_weights) {
  auto in_mat_dims = common::flatten_to_2d(in_dims, in_num_col_dims);
  auto w_dims0 = padding_weights ? w_dims[0] - 4 : w_dims[0];
  auto w_dims1 = padding_weights ? w_dims[1] - 4 : w_dims[1];
  PADDLE_ENFORCE_EQ(
      in_mat_dims[1],
      w_dims0,
      common::errors::InvalidArgument(
          "The input's second dimension and weight's first dimension is "
          "expected to be the same. But received input's second dimension is "
          "%d, input's shape is %s; weight's first dimension is %d, weight's "
          "shape is %s.",
          in_mat_dims[1],
          in_mat_dims,
          w_dims0,
          make_ddim({w_dims0, w_dims1})));

  out_dims.reserve(static_cast<size_t>(in_num_col_dims + 1));
  for (int i = 0; i < in_num_col_dims; ++i) {
    out_dims.push_back(in_dims[i]);
  }
  out_dims.push_back(w_dims1);
}

inline std::vector<int64_t> GetReduceDims(const DenseTensor &in,
                                          const DenseTensor &out) {
  std::vector<int64_t> reduce_dims;
  auto in_dims = in.dims();
  auto out_dims = out.dims();
  int diff = in_dims.size() - out_dims.size();
  for (int i = 0; i < diff; ++i) {
    reduce_dims.push_back(i);
  }
  for (int i = 0; i < out_dims.size(); ++i) {
    if (out_dims[i] == 1 && in_dims[i + diff] != 1) {
      reduce_dims.push_back(i + diff);
    } else {
      PADDLE_ENFORCE_EQ(
          in_dims[i + diff],
          out_dims[i],
          common::errors::InvalidArgument(
              "ReduceDims dimension mismatch. Operands could "
              "not be broadcast together with the shape of in_dims = [%s] and "
              "the shape of out_dims = [%s]. Received [%d] in X is not equal "
              "to "
              "[%d] in Y at i:%d.",
              in_dims,
              out_dims,
              in_dims[i + diff],
              out_dims[i],
              i));
    }
  }
  return reduce_dims;
}

}  // namespace funcs
}  // namespace phi