76 lines
3.3 KiB
C++
76 lines
3.3 KiB
C++
/* Copyright 2017 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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// XLA-specific base classes for Unary and Binary Ops.
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#ifndef TENSORFLOW_COMPILER_TF2XLA_KERNELS_CWISE_OPS_H_
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#define TENSORFLOW_COMPILER_TF2XLA_KERNELS_CWISE_OPS_H_
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#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/xla/client/client_library.h"
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/util/bcast.h"
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namespace tensorflow {
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// Coefficient-wise binary operations. Each binary Op expects two
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// inputs that can be broadcast to the same shape. The base class
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// contains pure virtual methods to override: description is a textual
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// description of the operation; and Computation adds the
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// implementation of the operation to a xla::XlaBuilder. For most
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// arithmetic Ops XLA handles the broadcasting automatically given the input
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// tensors.
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class XlaBinaryOp : public XlaOpKernel {
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public:
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explicit XlaBinaryOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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const DataType lhs = BaseType(input_type(0));
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const DataType rhs = BaseType(input_type(1));
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OP_REQUIRES(ctx, lhs == rhs,
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errors::InvalidArgument("Input types of binary op must match"));
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}
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~XlaBinaryOp() override {}
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// Implement the (tensor,tensor)->tensor lambda that should be
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// applied to the inputs. The desired computation should be added to
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// 'tc->builder()' and '(lhs,rhs)' are the function's inputs and
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// (lhs_shape,rhs_shape) are their respective
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// shapes. 'broadcast_helper' contains metadata about the shapes of
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// the inputs and the dimensions that need to be broadcast, which
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// may be useful for Ops that can't use standard XLA automatic
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// broadcasting. 'extend_dimension' is non-empty if lhs and rhs have
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// different ranks, and indicates which dimensions of the
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// higher-rank input should be matched when broadcasting the
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// lower-rank input. See comment below and the documentation on broadcasting
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// in the XLA documentation.
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virtual xla::XlaOp Computation(
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XlaOpKernelContext* ctx, const xla::XlaOp& lhs,
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const absl::Span<const int64>& lhs_shape, const xla::XlaOp& rhs,
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const absl::Span<const int64>& rhs_shape, const BCast& broadcast_helper,
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const std::vector<int64>& extend_dimensions) = 0;
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void Compile(XlaOpKernelContext* ctx) override;
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// Helper function that performs the broadcasting described by
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// 'broadcast_helper', yielding arguments 'lhs' and 'rhs' that have the same
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// shape.
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static std::pair<xla::XlaOp, xla::XlaOp> Broadcast(
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xla::XlaOp lhs, xla::XlaOp rhs, const BCast& broadcast_helper);
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};
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} // namespace tensorflow
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#endif // TENSORFLOW_COMPILER_TF2XLA_KERNELS_CWISE_OPS_H_
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