77 lines
3.0 KiB
C++
77 lines
3.0 KiB
C++
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_COMPILER_TF2XLA_KERNELS_CONV_OP_HELPERS_H_
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#define TENSORFLOW_COMPILER_TF2XLA_KERNELS_CONV_OP_HELPERS_H_
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#include <vector>
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/statusor.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/framework/types.h"
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#include "tensorflow/core/util/padding.h"
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#include "tensorflow/core/util/tensor_format.h"
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// This header exposes utilities for translating TensorFlow convolution ops into
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// XLA ops.
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//
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// conv_ops.cc contains lowerings for many of these TF convolution ops (e.g.
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// Conv2D, Conv3DBackpropFilterV2), but you might want to use the utilities in
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// this header to implement a new and exciting convolution op, for example a
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// fused TensorFlow op that contains a convolution and other things.
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namespace tensorflow {
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// We don't support integers for convolutions, so we list the supported types
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// here.
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absl::Span<const DataType> GetXlaConvTypes();
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// ConvOpAttrs contains all of the metadata necessary to specify a TF or XLA
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// convolution.
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struct ConvOpAttrs {
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// Constructs a ConvOpAttrs, reading most of the attributes from `ctx`.
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static xla::StatusOr<ConvOpAttrs> Create(int num_spatial_dims, bool depthwise,
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OpKernelConstruction* ctx);
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bool depthwise;
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int num_spatial_dims;
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std::vector<int32> dilations;
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std::vector<int32> strides;
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Padding padding;
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std::vector<int64> explicit_paddings;
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TensorFormat data_format;
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};
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// Creates a new XLA forward or backward convolution with the given inputs and
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// attributes.
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xla::StatusOr<xla::XlaOp> MakeXlaForwardConvOp(
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StringPiece type_string, xla::XlaOp conv_input, xla::XlaOp filter,
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const ConvOpAttrs& attrs,
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const xla::PrecisionConfig* precision_config = nullptr);
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xla::StatusOr<xla::XlaOp> MakeXlaBackpropInputConvOp(
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StringPiece type_string, const xla::Shape& input_shape, xla::XlaOp filter,
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xla::XlaOp out_backprop, const ConvOpAttrs& attrs,
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const xla::PrecisionConfig* precision_config = nullptr);
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xla::StatusOr<xla::XlaOp> MakeXlaBackpropFilterConvOp(
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StringPiece type_string, xla::XlaOp activations,
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const xla::Shape& filter_shape, xla::XlaOp gradients,
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const ConvOpAttrs& attrs,
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const xla::PrecisionConfig* precision_config = nullptr);
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} // namespace tensorflow
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#endif // TENSORFLOW_COMPILER_TF2XLA_KERNELS_CONV_OP_HELPERS_H_
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