[OP] Add randperm op (#23292)
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6 years ago
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/* Copyright (c) 2020 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/randperm_op.h"
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#include <string>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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namespace paddle {
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namespace operators {
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class RandpermOp : 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_EQ(ctx->HasOutput("Out"), true,
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platform::errors::NotFound(
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"The output(Out) of randperm op must not be null."));
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int n = ctx->Attrs().Get<int>("n");
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PADDLE_ENFORCE_GT(
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n, 0, platform::errors::InvalidArgument(
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"The input(n) of randperm op must be greater than 0."));
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ctx->SetOutputDim("Out", framework::make_ddim({n}));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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auto data_type =
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static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
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return framework::OpKernelType(data_type, ctx.GetPlace());
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}
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};
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class RandpermOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddOutput("Out", "The output tensor of randperm op.");
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AddAttr<int>(
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"n", "The upper bound (exclusive), and it should be greater than 0.");
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AddAttr<int>("dtype",
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"The data type of output tensor. "
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"Default: 3[int64].")
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.SetDefault(framework::proto::VarType::INT64);
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AddAttr<int>("seed",
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"Random seed used for permute samples. "
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"0 means use a seed generated by the system."
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"Note that if seed is not 0, this operator will always "
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"generate the same random permutation every time. "
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"Default: 0.")
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.SetDefault(0);
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AddComment(R"DOC(
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This operator returns a random permutation of integers from 0 to n-1.
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)DOC");
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}
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};
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class RandpermOpVarTypeInference : public framework::VarTypeInference {
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public:
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void operator()(framework::InferVarTypeContext *ctx) const override {
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auto var_data_type = static_cast<framework::proto::VarType::Type>(
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boost::get<int>(ctx->GetAttr("dtype")));
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auto out_var_name = ctx->Output("Out").front();
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ctx->SetDataType(out_var_name, var_data_type);
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OPERATOR(
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randperm, paddle::operators::RandpermOp, paddle::operators::RandpermOpMaker,
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paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
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paddle::operators::RandpermOpVarTypeInference);
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template <typename T>
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using kernel =
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paddle::operators::RandpermKernel<paddle::platform::CPUDeviceContext, T>;
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REGISTER_OP_CPU_KERNEL(randperm, kernel<int64_t>, kernel<int>);
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@ -0,0 +1,23 @@
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/* Copyright (c) 2020 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/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/operators/randperm_op.h"
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template <typename T>
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using kernel =
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paddle::operators::RandpermKernel<paddle::platform::CUDADeviceContext, T>;
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REGISTER_OP_CUDA_KERNEL(randperm, kernel<int64_t>, kernel<int>);
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@ -0,0 +1,65 @@
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/* Copyright (c) 2020 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 <algorithm>
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#include <cstdlib>
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#include <ctime>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/platform/place.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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static inline void random_permate(T* data_ptr, int num, unsigned int seed) {
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for (int i = 0; i < num; ++i) {
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data_ptr[i] = static_cast<T>(i);
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}
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if (seed == 0) {
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seed = std::random_device()();
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}
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std::srand(seed);
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std::random_shuffle(data_ptr, data_ptr + num);
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}
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template <typename DeviceContext, typename T>
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class RandpermKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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int n = ctx.Attr<int>("n");
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unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
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framework::Variable* out_var = ctx.OutputVar("Out");
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framework::Tensor* out_tensor =
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framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(out_var);
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if (platform::is_cpu_place(ctx.GetPlace())) {
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T* out_data = out_tensor->mutable_data<T>(platform::CPUPlace());
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random_permate<T>(out_data, n, seed);
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} else {
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framework::Tensor tmp_tensor;
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tmp_tensor.Resize(framework::make_ddim({n}));
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T* tmp_data = tmp_tensor.mutable_data<T>(platform::CPUPlace());
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random_permate<T>(tmp_data, n, seed);
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framework::TensorCopy(tmp_tensor, platform::CUDAPlace(), out_tensor);
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}
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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 +1,175 @@
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# Copyright (c) 2020 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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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
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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from paddle.fluid import Program, program_guard
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def check_randperm_out(n, data_np):
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assert isinstance(data_np, np.ndarray), \
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"The input data_np should be np.ndarray."
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gt_sorted = np.arange(n)
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out_sorted = np.sort(data_np)
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return list(gt_sorted == out_sorted)
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def error_msg(data_np):
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return "The sorted ground truth and sorted out should " + \
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"be equal, out = " + str(data_np)
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def convert_dtype(dtype_str):
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dtype_str_list = ["int32", "int64"]
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dtype_num_list = [2, 3]
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assert dtype_str in dtype_str_list, dtype_str + \
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" should in " + str(dtype_str_list)
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return dtype_num_list[dtype_str_list.index(dtype_str)]
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class TestRandpermOp(OpTest):
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""" Test randperm op."""
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def setUp(self):
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self.op_type = "randperm"
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self.n = 200
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self.dtype = "int64"
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self.device = None
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self.seed = 0
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self.inputs = {}
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self.outputs = {"Out": np.zeros((self.n)).astype(self.dtype)}
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self.init_attrs()
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self.attrs = {
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"n": self.n,
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"dtype": convert_dtype(self.dtype),
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"device": self.device,
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"seed": self.seed,
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}
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def init_attrs(self):
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pass
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def test_check_output(self):
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self.check_output_customized(self.verify_output)
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def verify_output(self, outs):
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out_np = np.array(outs[0])
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self.assertTrue(
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check_randperm_out(self.n, out_np), msg=error_msg(out_np))
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class TestRandpermOp_attr_n(TestRandpermOp):
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""" Test randperm op for attr n. """
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def init_attrs(self):
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self.n = 10000
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class TestRandpermOp_attr_int32(TestRandpermOp):
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""" Test randperm op for attr int32 dtype. """
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def init_attrs(self):
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self.dtype = "int32"
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class TestRandpermOp_attr_device_cpu(TestRandpermOp):
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""" Test randperm op for cpu device. """
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def init_attrs(self):
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self.device = "cpu"
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class TestRandpermOp_attr_device_gpu(TestRandpermOp):
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""" Test randperm op for gpu device. """
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def init_attrs(self):
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self.device = "gpu"
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class TestRandpermOp_attr_seed(TestRandpermOp):
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""" Test randperm op for attr seed. """
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def init_attrs(self):
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self.seed = 10
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class TestRandpermOpError(unittest.TestCase):
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""" Test randperm op for raise error. """
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def test_errors(self):
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main_prog = Program()
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start_prog = Program()
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with program_guard(main_prog, start_prog):
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def test_Variable():
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out = np.arange(10)
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paddle.randperm(n=10, out=out)
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self.assertRaises(TypeError, test_Variable)
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def test_value():
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paddle.randperm(n=-3)
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self.assertRaises(ValueError, test_value)
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class TestRandpermOp_attr_out(unittest.TestCase):
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""" Test randperm op for attr out. """
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def test_attr_tensor_API(self):
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startup_program = fluid.Program()
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train_program = fluid.Program()
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with fluid.program_guard(train_program, startup_program):
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n = 10
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data_1 = fluid.layers.fill_constant([n], "int64", 3)
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paddle.randperm(n=n, out=data_1)
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data_2 = paddle.randperm(n=n, dtype="int32", device="cpu")
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place = fluid.CPUPlace()
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if fluid.core.is_compiled_with_cuda():
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place = fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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exe.run(startup_program)
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outs = exe.run(train_program, fetch_list=[data_1, data_2])
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out_np = np.array(outs[0])
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self.assertTrue(
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check_randperm_out(n, out_np), msg=error_msg(out_np))
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class TestRandpermDygraphMode(unittest.TestCase):
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def test_check_output(self):
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with fluid.dygraph.guard():
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n = 10
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data_1 = paddle.randperm(n, dtype="int64")
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data_1_np = data_1.numpy()
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self.assertTrue(
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check_randperm_out(n, data_1_np), msg=error_msg(data_1_np))
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data_2 = paddle.randperm(n, dtype="int32", device="cpu")
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data_2_np = data_2.numpy()
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self.assertTrue(
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check_randperm_out(n, data_2_np), msg=error_msg(data_2_np))
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if __name__ == "__main__":
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unittest.main()
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