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/* Copyright 2017 The TensorFlow 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.
==============================================================================*/
#include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h"
#include "tensorflow/compiler/xla/legacy_flags/cpu_runtime_flags.h"
#include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h"
#include "tensorflow/compiler/xla/service/cpu/ir_emission_utils.h"
#include "tensorflow/compiler/xla/service/hlo_computation.h"
#include "tensorflow/compiler/xla/service/hlo_instruction.h"
#include "tensorflow/compiler/xla/service/hlo_opcode.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/compiler/xla/util.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
namespace xla {
namespace cpu {
StatusOr<bool> ConvCanonicalization::Run(HloModule* module) {
legacy_flags::CpuRuntimeFlags* flags = legacy_flags::GetCpuRuntimeFlags();
if (!flags->xla_cpu_use_eigen) {
return false;
}
bool changed = false;
for (HloInstruction* hlo :
module->entry_computation()->MakeInstructionPostOrder()) {
if (hlo->opcode() == HloOpcode::kConvolution &&
!PotentiallyImplementedAsEigenConvolution(*hlo)) {
const ConvolutionDimensionNumbers& dnums =
hlo->convolution_dimension_numbers();
auto batch_dim = dnums.batch_dimension();
auto feature_dim = dnums.feature_dimension();
auto kernel_input_feature_dim = dnums.kernel_input_feature_dimension();
auto kernel_output_feature_dim = dnums.kernel_output_feature_dimension();
int num_spatial_dims = dnums.spatial_dimensions_size();
int num_dims = num_spatial_dims + 2;
// A canonical convolution's dimension numbers need to satisfy the
// following conditions (see cs/PotentiallyImplementedAsEigenConvolution).
//
// - the input is in NHWC or NWHC order.
// - the kernel is in HWIO or WHIO order.
// - the spatial dimensions are in the same relative order in the input,
// kernel and output.
//
// For simplicity, as a first step, we reshape the input and filter to
// NHWC and HWIO order, respectively. This may lose precision but not
// break the soundness.
HloInstruction* input = hlo->mutable_operand(0);
std::vector<int64> new_input_dim_order(num_dims);
std::vector<int64> new_input_dims(num_dims);
new_input_dim_order[0] = batch_dim;
new_input_dims[0] = input->shape().dimensions(batch_dim);
for (int i = 0; i < num_spatial_dims; ++i) {
new_input_dim_order[i + 1] = dnums.spatial_dimensions(i);
new_input_dims[i + 1] =
input->shape().dimensions(dnums.spatial_dimensions(i));
}
new_input_dim_order[num_dims - 1] = feature_dim;
new_input_dims[num_dims - 1] = input->shape().dimensions(feature_dim);
Shape new_input_shape =
ShapeUtil::MakeShape(input->shape().element_type(), new_input_dims);
HloInstruction* new_input = module->entry_computation()->AddInstruction(
HloInstruction::CreateTranspose(new_input_shape, input,
new_input_dim_order));
HloInstruction* kernel = hlo->mutable_operand(1);
std::vector<int64> new_kernel_dim_order(num_dims);
std::vector<int64> new_kernel_dims(num_dims);
for (int i = 0; i < num_spatial_dims; ++i) {
new_kernel_dim_order[i] = dnums.kernel_spatial_dimensions(i);
new_kernel_dims[i] =
kernel->shape().dimensions(dnums.kernel_spatial_dimensions(i));
}
new_kernel_dim_order[num_dims - 2] = kernel_input_feature_dim;
new_kernel_dims[num_dims - 2] =
kernel->shape().dimensions(kernel_input_feature_dim);
new_kernel_dim_order[num_dims - 1] = kernel_output_feature_dim;
new_kernel_dims[num_dims - 1] =
kernel->shape().dimensions(kernel_output_feature_dim);
Shape new_kernel_shape =
ShapeUtil::MakeShape(kernel->shape().element_type(), new_kernel_dims);
HloInstruction* new_kernel = module->entry_computation()->AddInstruction(
HloInstruction::CreateTranspose(new_kernel_shape, kernel,
new_kernel_dim_order));
std::vector<int64> new_conv_dims(num_dims);
new_conv_dims[0] = hlo->shape().dimensions(batch_dim);
for (int i = 0; i < num_spatial_dims; ++i) {
new_conv_dims[i + 1] =
hlo->shape().dimensions(dnums.spatial_dimensions(i));
}
new_conv_dims[num_dims - 1] = hlo->shape().dimensions(feature_dim);
Shape new_conv_shape =
ShapeUtil::MakeShape(hlo->shape().element_type(), new_conv_dims);
ConvolutionDimensionNumbers new_dnums;
new_dnums.set_batch_dimension(0);
for (int i = 0; i < num_spatial_dims; ++i) {
new_dnums.add_spatial_dimensions(i + 1);
new_dnums.add_kernel_spatial_dimensions(i);
}
new_dnums.set_feature_dimension(num_dims - 1);
new_dnums.set_kernel_input_feature_dimension(num_dims - 2);
new_dnums.set_kernel_output_feature_dimension(num_dims - 1);
// The window of the old convolution is reused, because reshapes only
// change the dimension mapping but not the dimension sizes. For
// example, input height and width are the same as before the reshapes.
HloInstruction* new_conv = module->entry_computation()->AddInstruction(
HloInstruction::CreateConvolve(new_conv_shape, new_input, new_kernel,
hlo->window(), new_dnums));
// kConvolution inherits the dimension mapping of its input, so we need to
// reshape the output back to the shape of the original convolution. This
// is done by apply the inverse permutation of the collapsing order of the
// input reshape.
module->entry_computation()->ReplaceWithNewInstruction(
hlo,
HloInstruction::CreateTranspose(
hlo->shape(), new_conv, InversePermutation(new_input_dim_order)));
changed = true;
}
}
return changed;
}
} // namespace cpu
} // namespace xla
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