69 lines
2.6 KiB
C++
69 lines
2.6 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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#include <algorithm>
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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/lib/arithmetic.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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namespace tensorflow {
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namespace {
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class BucketizeOp : public XlaOpKernel {
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public:
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explicit BucketizeOp(OpKernelConstruction* context) : XlaOpKernel(context) {
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OP_REQUIRES_OK(context, context->GetAttr("boundaries", &boundaries_));
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OP_REQUIRES(context, std::is_sorted(boundaries_.begin(), boundaries_.end()),
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errors::InvalidArgument("Expected sorted boundaries"));
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}
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void Compile(XlaOpKernelContext* context) override {
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xla::XlaBuilder* builder = context->builder();
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const DataType dtype = context->input_type(0);
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xla::XlaOp input = context->Input(0);
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xla::XlaOp boundaries = xla::ConstantR1<float>(builder, boundaries_);
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// TODO(phawkins): the following behavior matches the behavior of the core
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// Bucketize kernel. However, comparing an int32 or int64 against float may
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// lead to inaccurate bucketing due to rounding.
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if (dtype == DT_DOUBLE) {
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input = xla::ConvertElementType(input, xla::F64);
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boundaries = xla::ConvertElementType(boundaries, xla::F64);
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} else {
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input = xla::ConvertElementType(input, xla::F32);
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}
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xla::XlaOp comparison =
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xla::ConvertElementType(xla::Ge(xla::Broadcast(input, {1}), boundaries,
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/*broadcast_dimensions=*/{0}),
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xla::S32);
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xla::XlaOp buckets = xla::Reduce(
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comparison, /*init_value=*/xla::ConstantR0<int32>(builder, 0),
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/*computation=*/xla::CreateScalarAddComputation(xla::S32, builder),
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/*dimensions_to_reduce=*/{0});
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context->SetOutput(0, buckets);
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}
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private:
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std::vector<float> boundaries_;
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};
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REGISTER_XLA_OP(Name("Bucketize"), BucketizeOp);
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} // namespace
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} // namespace tensorflow
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