// Copyright (c) 2021 PaddlePaddle 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 "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/platform/enforce.h" namespace paddle { namespace operators { class SearchSortedOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; static bool SearchsortedDimsMatchedBeforeLastDim( const framework::DDim& sequences_dims, const framework::DDim& values_dims) { if (sequences_dims.size() != values_dims.size()) { return false; } const auto& sequences_dims_size = sequences_dims.size(); for (int64_t dim = 0; dim < sequences_dims_size - 1; ++dim) { if (sequences_dims[dim] != values_dims[dim]) { return false; } } return true; } void InferShape(framework::InferShapeContext* ctx) const override { OP_INOUT_CHECK(ctx->HasInput("SortedSequence"), "Input", "SortedSequence", "searchsorted"); OP_INOUT_CHECK(ctx->HasInput("Values"), "Input", "Values", "searchsorted"); OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "searchsorted"); auto sequences_dims = ctx->GetInputDim("SortedSequence"); auto values_dims = ctx->GetInputDim("Values"); auto out_int32 = ctx->Attrs().Get("out_int32"); if (sequences_dims.size() != 1) { PADDLE_ENFORCE_EQ( SearchsortedDimsMatchedBeforeLastDim(sequences_dims, values_dims), true, platform::errors::Unavailable( "The dimensions of sorted_sequence tensor ( %s ) and values " "tensor ( %s ) can not match. Because the input sorted_sequence " "tensor must be 1 dimension or the first N-1 dimensions of " "sorted_sequence tensor and input values tensor must match. " "Please input appropriate sorted_sequence and values again! ", sequences_dims, values_dims)); } if (out_int32) { PADDLE_ENFORCE_LT( sequences_dims[sequences_dims.size() - 1], std::numeric_limits::max(), platform::errors::Unavailable( "The size of sorted_sequence %d exceed the maximum limit d%. " "Because the size of sorted_sequence should be less than the " "output maximum value for int32 bit. Please set appropriate " "sorted_sequence to meet this requirement! ", sequences_dims[sequences_dims.size() - 1], std::numeric_limits::max())); } ctx->SetOutputDim("Out", values_dims); } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "SortedSequence"); return framework::OpKernelType(data_type, ctx.device_context()); } }; class SearchSortedOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("SortedSequence", "(Tensor), N-D or 1-D tensor, The value of the tensor" "monotonically increases in the innermost dimension."); AddInput("Values", "(Tensor), N-D tensor given values."); AddOutput("Out", "(Tensor), The output tensor of searchsorted op."); AddAttr("out_int32", "the output tensor is int64 type if False and on the" "contrary for int32") .SetDefault(false); AddAttr( "right", "corresponding to lower bound if False and upper bound if True") .SetDefault(false); AddComment(R"DOC( Searchsorted Operator. This OP is used to find the index of the corresponding sorted_sequence in the innermost dimension based on the given values. )DOC"); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(searchsorted, ops::SearchSortedOp, ops::SearchSortedOpMaker);