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      1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #include "tensorflow/compiler/tf2xla/lib/batch_dot.h"
     17 
     18 #include <memory>
     19 #include <vector>
     20 
     21 #include "tensorflow/compiler/xla/shape_util.h"
     22 #include "tensorflow/compiler/xla/status_macros.h"
     23 #include "tensorflow/compiler/xla/statusor.h"
     24 #include "tensorflow/core/lib/core/errors.h"
     25 
     26 namespace tensorflow {
     27 
     28 xla::StatusOr<xla::ComputationDataHandle> BatchDot(
     29     xla::ComputationBuilder* builder, xla::ComputationDataHandle x,
     30     xla::ComputationDataHandle y, bool transpose_x, bool transpose_y,
     31     bool conjugate_x, bool conjugate_y) {
     32   TF_ASSIGN_OR_RETURN(std::unique_ptr<xla::Shape> x_shape,
     33                       builder->GetShape(x));
     34   TF_ASSIGN_OR_RETURN(std::unique_ptr<xla::Shape> y_shape,
     35                       builder->GetShape(y));
     36 
     37   // Check that both tensors have the same number of dimensions. There must be
     38   // at least two (the batch dimensions can be empty).
     39   if (xla::ShapeUtil::Rank(*x_shape) != xla::ShapeUtil::Rank(*y_shape)) {
     40     return errors::InvalidArgument(
     41         "Arguments to BatchedDot have different ranks: ",
     42         xla::ShapeUtil::HumanString(*x_shape), " vs. ",
     43         xla::ShapeUtil::HumanString(*y_shape));
     44   }
     45   const int ndims = xla::ShapeUtil::Rank(*x_shape);
     46   if (ndims < 2) {
     47     return errors::InvalidArgument(
     48         "Arguments to BatchedDot must have rank >= 2: ", ndims);
     49   }
     50 
     51   // The batch dimensions must be equal and the matrix dimensions must be
     52   // valid.
     53   std::vector<int64> batch_dimension_numbers;
     54   for (int i = 0; i < ndims - 2; ++i) {
     55     if (x_shape->dimensions(i) != y_shape->dimensions(i)) {
     56       return errors::InvalidArgument(
     57           "Dimension ", i, " of inputs to BatchedDot must be equal: ",
     58           xla::ShapeUtil::HumanString(*x_shape), " vs ",
     59           xla::ShapeUtil::HumanString(*y_shape));
     60     }
     61     batch_dimension_numbers.push_back(i);
     62   }
     63 
     64   int x_inner_dim = transpose_x ? (ndims - 2) : (ndims - 1);
     65   int y_inner_dim = transpose_y ? (ndims - 1) : (ndims - 2);
     66   if (x_shape->dimensions(x_inner_dim) != y_shape->dimensions(y_inner_dim)) {
     67     return errors::InvalidArgument(
     68         "Dimensions ", x_inner_dim, " and ", y_inner_dim,
     69         " of arguments to BatchedDot must be equal: ",
     70         xla::ShapeUtil::HumanString(*x_shape), " transpose: ", transpose_x,
     71         " vs. ", xla::ShapeUtil::HumanString(*y_shape),
     72         " transpose: ", transpose_y);
     73   }
     74 
     75   // Check for zero lhs/rhs dim size.
     76   if (xla::ShapeUtil::HasZeroElements(*x_shape) ||
     77       xla::ShapeUtil::HasZeroElements(*y_shape)) {
     78     std::vector<int64> dimensions(batch_dimension_numbers.size());
     79     for (int i = 0; i < batch_dimension_numbers.size(); ++i) {
     80       dimensions[i] = x_shape->dimensions(batch_dimension_numbers[i]);
     81     }
     82     int x_outer_dim = transpose_x ? (ndims - 1) : (ndims - 2);
     83     int y_outer_dim = transpose_y ? (ndims - 2) : (ndims - 1);
     84     dimensions.push_back(x_shape->dimensions(x_outer_dim));
     85     dimensions.push_back(y_shape->dimensions(y_outer_dim));
     86     return builder->Broadcast(
     87         builder->ConstantLiteral(xla::Literal::Zero(x_shape->element_type())),
     88         dimensions);
     89   }
     90 
     91   if (x_shape->element_type() == xla::C64 && conjugate_x) {
     92     x = builder->Conj(x);
     93   }
     94   if (y_shape->element_type() == xla::C64 && conjugate_y) {
     95     y = builder->Conj(y);
     96   }
     97 
     98   // If there are no batch dimensions, use a regular Dot.
     99   // TODO(b/69062148) Remove this code when Dot emitters can be passed
    100   // dimensions to transpose directly (i.e. without requiring a Transpose HLO).
    101   if (batch_dimension_numbers.empty()) {
    102     auto lhs = transpose_x ? builder->Transpose(x, {1, 0}) : x;
    103     auto rhs = transpose_y ? builder->Transpose(y, {1, 0}) : y;
    104     return builder->Dot(lhs, rhs);
    105   }
    106 
    107   xla::DotDimensionNumbers dot_dnums;
    108   dot_dnums.add_lhs_contracting_dimensions(x_inner_dim);
    109   dot_dnums.add_rhs_contracting_dimensions(y_inner_dim);
    110   for (auto batch_dimension_number : batch_dimension_numbers) {
    111     dot_dnums.add_lhs_batch_dimensions(batch_dimension_number);
    112     dot_dnums.add_rhs_batch_dimensions(batch_dimension_number);
    113   }
    114   return builder->DotGeneral(x, y, dot_dnums);
    115 }
    116 
    117 }  // namespace tensorflow
    118