add unique kernel and op (#17557)
zhoukunsheng
6 years ago
committed by
Tao Luo
parent
71af72b1c2
commit
206c44e2a8
@ -0,0 +1,61 @@
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/* Copyright (c) 2019 PaddlePaddle 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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#include "paddle/fluid/operators/unique_op.h"
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namespace paddle {
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namespace operators {
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class UniqueOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of UniqueOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of UniqueOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Index"),
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"Output(Index) of UniqueOp should not be null.");
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auto in_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE(in_dims.size() == 1, "Input(X) should be a vector.");
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ctx->SetOutputDim("Out", {-1});
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ctx->SetOutputDim("Index", in_dims);
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}
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};
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class UniqueOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "Input tensor. It should be a 1-D tensor.");
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AddAttr<int>("dtype", "data type for output index");
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AddOutput("Out", "A unique subsequence for input tensor.");
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AddOutput("Index",
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"An index tensor pointing to unique subsequence, which has "
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"identical shape with input tensor and int64 dtype.");
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AddComment(R"DOC(
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Return a unique subsequence for 1-D input tensor, and an index tensor pointing to this unique subsequence
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(unique, ops::UniqueOp, ops::UniqueOpMaker);
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REGISTER_OP_CPU_KERNEL(unique, ops::UniqueKernel<float>,
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ops::UniqueKernel<double>, ops::UniqueKernel<int32_t>,
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ops::UniqueKernel<int64_t>);
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@ -0,0 +1,83 @@
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/* Copyright (c) 2019 PaddlePaddle 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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#pragma once
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#include <cmath>
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#include <unordered_map>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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template <typename InT>
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struct UniqueOpFunctor {
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framework::Tensor* out_;
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framework::Tensor* index_;
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const framework::Tensor* in_;
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UniqueOpFunctor(framework::Tensor* out, framework::Tensor* index,
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const framework::Tensor* in)
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: out_(out), index_(index), in_(in) {}
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template <typename IndexT>
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void apply() const {
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auto* in_data = in_->data<InT>();
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auto* index_data = index_->mutable_data<IndexT>(platform::CPUPlace());
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int64_t j = 0;
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// TODO(fangzeyang): Should optimize performance here.
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std::unordered_map<InT, int64_t> dict;
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std::vector<InT> uniq;
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PADDLE_ENFORCE(in_->numel() < pow(2, 31),
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"numel of Unique op input should less than INT_MAX");
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for (auto i = 0; i < in_->numel(); i++) {
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auto it = dict.find(in_data[i]);
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if (it == dict.end()) {
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dict.insert(std::make_pair(in_data[i], j));
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uniq.push_back(in_data[i]);
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index_data[i] = static_cast<IndexT>(j);
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j++;
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} else {
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index_data[i] = static_cast<IndexT>(it->second);
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}
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}
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out_->Resize(framework::make_ddim({static_cast<int64_t>(uniq.size())}));
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auto out_data = out_->mutable_data<InT>(platform::CPUPlace());
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std::memcpy(out_data, uniq.data(), uniq.size() * sizeof(InT));
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}
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};
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template <typename T>
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class UniqueKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto data_type = static_cast<framework::proto::VarType::Type>(
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context.Attr<int>("dtype"));
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auto* x = context.Input<framework::Tensor>("X");
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auto* out = context.Output<framework::Tensor>("Out");
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auto* index = context.Output<framework::Tensor>("Index");
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framework::VisitDataType(data_type, UniqueOpFunctor<T>(out, index, x));
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,72 @@
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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class TestUniqueOp(OpTest):
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def setUp(self):
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self.op_type = "unique"
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self.init_config()
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def test_check_output(self):
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self.check_output()
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def init_config(self):
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self.inputs = {'X': np.array([2, 3, 3, 1, 5, 3], dtype='int64'), }
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
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self.outputs = {
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'Out': np.array(
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[2, 3, 1, 5], dtype='int64'),
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'Index': np.array(
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[0, 1, 1, 2, 3, 1], dtype='int32')
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}
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class TestOne(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.array([2], dtype='int64'), }
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
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self.outputs = {
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'Out': np.array(
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[2], dtype='int64'),
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'Index': np.array(
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[0], dtype='int32')
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}
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class TestRandom(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.random.randint(0, 100, (150, ), dtype='int64')}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
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np_unique, np_index, reverse_index = np.unique(self.inputs['X'], True,
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True)
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np_tuple = [(np_unique[i], np_index[i]) for i in range(len(np_unique))]
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np_tuple.sort(key=lambda x: x[1])
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target_out = np.array([i[0] for i in np_tuple], dtype='int64')
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target_index = np.array(
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[list(target_out).index(i) for i in self.inputs['X']],
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dtype='int64')
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self.outputs = {'Out': target_out, 'Index': target_index}
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if __name__ == "__main__":
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unittest.main()
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