53 lines
2.0 KiB
C++
53 lines
2.0 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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#include "tensorflow/compiler/tf2xla/xla_helpers.h"
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#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.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/framework/types.h"
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namespace tensorflow {
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namespace {
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class L2LossOp : public XlaOpKernel {
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public:
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explicit L2LossOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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std::vector<int64> dims(ctx->InputShape(0).dims());
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std::iota(dims.begin(), dims.end(), 0);
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DataType dtype = ctx->input_type(0);
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xla::XlaBuilder* const b = ctx->builder();
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// output = sum(t ** 2) / 2
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const DataType accumulation_type = XlaHelpers::SumAccumulationType(dtype);
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auto t = XlaHelpers::ConvertElementType(ctx->Input(0), accumulation_type);
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auto square = xla::Mul(t, t);
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auto reduce = xla::Reduce(square, XlaHelpers::Zero(b, accumulation_type),
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*ctx->GetOrCreateAdd(accumulation_type), dims);
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auto deconverted = XlaHelpers::ConvertElementType(reduce, dtype);
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auto two = XlaHelpers::IntegerLiteral(b, dtype, 2);
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ctx->SetOutput(0, xla::Div(deconverted, two));
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}
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};
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REGISTER_XLA_OP(Name("L2Loss"), L2LossOp);
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} // namespace
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} // namespace tensorflow
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