It can be run both CPU/GPU. configure attributes are: * min: the min value of uniform random * max: the max value of uniform random * dims: the dimension of output tensor * seed: the random seed of uniform random. 0 means generate a seed each time.fixstartbug
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/uniform_random_op.h"
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namespace paddle {
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namespace operators {
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class RandomOp : public OperatorWithKernel {
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protected:
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void InferShape(const InferShapeContext &ctx) const override {
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PADDLE_ENFORCE(GetAttr<float>("min") < GetAttr<float>("max"),
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"uniform_random's min must less then max");
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auto tensor = ctx.Output<Tensor>(0);
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auto dims = GetAttr<std::vector<int>>("dims");
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tensor->Resize(framework::make_ddim(dims));
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}
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};
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class RandomOpMaker : public OpProtoAndCheckerMaker {
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public:
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RandomOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddOutput("Out", "The output tensor of uniform random op");
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AddComment(R"DOC(Uniform random operator.
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Used to initialize tensor with uniform random generator.
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)DOC");
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AddAttr<std::vector<int>>("dims", "the dimension of random tensor");
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AddAttr<float>("min", "Minimum value of uniform random").SetDefault(-1.0f);
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AddAttr<float>("max", "Maximun value of uniform random").SetDefault(1.0f);
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AddAttr<int>("seed",
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"Random seed of uniform random. "
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"0 means generate a seed by system")
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.SetDefault(0);
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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_OP(uniform_random, ops::RandomOp, ops::RandomOpMaker);
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REGISTER_OP_CPU_KERNEL(uniform_random,
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ops::UniformRandomKernel<ops::CPUPlace, float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/uniform_random_op.h"
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REGISTER_OP_GPU_KERNEL(uniform_random,
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ops::UniformRandomKernel<ops::GPUPlace, float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 "paddle/operators/type_alias.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class UniformRandomKernel : public OpKernel {
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public:
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void Compute(const ExecutionContext &context) const override {
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auto tensor = context.Output<Tensor>(0);
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tensor->mutable_data<T>(context.GetPlace());
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auto eigenTensor = EigenVector<T>::Flatten(*tensor);
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auto dev = context.GetEigenDevice<Place>();
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auto min = context.op_.GetAttr<float>("min");
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auto max = context.op_.GetAttr<float>("max");
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auto seed = static_cast<uint64_t>(context.op_.GetAttr<int>("seed"));
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auto diff = max - min;
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Eigen::internal::UniformRandomGenerator<T> gen(seed);
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eigenTensor.device(dev) = eigenTensor.random(gen) * diff + min;
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}
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};
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} // namespace operators
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} // namespace paddle
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import unittest
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from paddle.v2.framework.op import Operator
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import paddle.v2.framework.core as core
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import numpy
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class UniformRandomTest(unittest.TestCase):
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def test_uniform_random_cpu(self):
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self.uniform_random_test(place=core.CPUPlace())
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def test_uniform_random_gpu(self):
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if core.is_compile_gpu():
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self.uniform_random_test(place=core.GPUPlace(0))
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def uniform_random_test(self, place):
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scope = core.Scope()
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scope.new_var("X").get_tensor()
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op = Operator(
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"uniform_random",
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Out="X",
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dims=[1000, 784],
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min=-5.0,
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max=10.0,
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seed=10)
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op.infer_shape(scope)
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ctx = core.DeviceContext.create(place)
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op.run(scope, ctx)
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tensor = numpy.array(scope.find_var("X").get_tensor())
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self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1)
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if __name__ == '__main__':
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unittest.main()
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