Yancey1989
7 years ago
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31464f3425
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/* Copyright (c) 2016 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/data_type.h"
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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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#include "paddle/fluid/platform/device_context.h"
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namespace paddle {
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namespace operators {
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class UniformRandomTableInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *ctx) const override {
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VLOG(3) << "Infershape...";
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of UniformRandomTableOp should not be null.");
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PADDLE_ENFORCE(
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ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
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"uniform_random's min must less then max");
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auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
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std::vector<int64_t> temp;
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temp.reserve(shape.size());
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for (auto dim : shape) {
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temp.push_back(static_cast<int64_t>(dim));
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}
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ctx->SetOutputDim("Out", framework::make_ddim(temp));
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}
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};
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class UniformRandomTableOp : public framework::OperatorBase {
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public:
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using framework::OperatorBase::OperatorBase;
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &dev_place) const override {
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VLOG(3) << "RunImpl...";
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auto out =
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scope.FindVar(Output("Out"))->GetMutable<framework::SelectedRows>();
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auto shard_cnt = Attr<int>("shard_cnt");
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auto shard_id = Attr<int>("shard_id");
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auto max_id = Attr<int>("max_id");
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auto shape = Attr<std::vector<int>>("shape");
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auto tensor = out->mutable_value();
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tensor->Resize(framework::make_ddim(shape));
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// Only allocate the memory of large table on CPU
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auto cpu = platform::CPUPlace();
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float *data = tensor->mutable_data<float>(cpu);
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VLOG(3) << "generate seed";
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unsigned int seed = static_cast<unsigned int>(Attr<int>("seed"));
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std::minstd_rand engine;
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if (seed == 0) {
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seed = std::random_device()();
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}
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engine.seed(seed);
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std::uniform_real_distribution<float> dist(Attr<float>("min"),
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Attr<float>("max"));
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int64_t size = tensor->numel();
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for (int64_t i = 0; i < size; ++i) {
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data[i] = dist(engine);
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}
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// initialize rows by round-robin
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// TODO(Yancey1989): need to support other way to distribute Ids
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VLOG(3) << "calculate rows_size...";
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int64_t rows_size = 0;
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if (max_id % shard_cnt == 0) {
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rows_size = max_id / shard_cnt;
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} else {
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rows_size = max_id / shard_cnt + 1;
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}
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auto *rows = out->mutable_rows();
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rows->resize(rows_size);
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(*rows)[0] = shard_id;
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for (int64_t idx = 1; idx < rows_size; ++idx) {
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(*rows)[idx] = (*rows)[idx - 1] + shard_cnt;
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}
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out->set_height(max_id);
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}
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};
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class UniformRandomTableOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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UniformRandomTableOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: framework::OpProtoAndCheckerMaker(proto, op_checker) {
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AddOutput("Out",
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"(SelectedRows)"
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"The output table of uniform random table op.");
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AddComment(R"DOC(
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Uniform random operator for initializing a table.
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This operator initializes a SelectedRows with random values sampled from a
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uniform distribution.
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)DOC");
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AddAttr<int>("max_id",
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"(int, required)"
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"The maximal Id for the table.");
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AddAttr<int>("shard_cnt",
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"(int, required)"
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"The count of shards for distributing the table.");
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AddAttr<int>("shard_id", "(int, required) The current shard ID.");
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AddAttr<std::vector<int>>("shape",
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"(vector<int>) The shape of the output tensor");
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AddAttr<float>("min",
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"(float, default -1.0) "
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"Minimum value of uniform random")
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.SetDefault(-1.0f);
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AddAttr<float>("max",
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"(float, default 1.0) "
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"Maximun value of uniform random")
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.SetDefault(1.0f);
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AddAttr<int>("seed",
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"(int, default 0) "
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"Random seed used for generating 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 numbers every time.")
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.SetDefault(0);
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AddAttr<int>("dtype", "(int, default 5(FP32)) Output tensor data type")
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.SetDefault(framework::proto::VarType::FP32);
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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_OPERATOR(uniform_random_table, ops::UniformRandomTableOp,
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ops::UniformRandomTableInferShape,
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ops::UniformRandomTableOpMaker,
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paddle::framework::EmptyGradOpMaker);
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@ -0,0 +1,66 @@
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# Copyright (c) 2018 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.fluid.core as core
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from paddle.fluid.op import Operator
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def output_hist(out):
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hist, _ = np.histogram(out, range=(-5, 10))
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hist = hist.astype("float32")
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hist /= float(out.size)
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prob = 0.1 * np.ones((10))
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return hist, prob
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class TestUniformRandomTableOp(unittest.TestCase):
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def get_places(self):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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return places
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def test_check_output(self):
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for place in self.get_places():
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self.check_with_place(place)
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def check_with_place(self, place):
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scope = core.Scope()
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out = scope.var("X").get_selected_rows()
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op = Operator(
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"uniform_random_table",
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Out="X",
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shape=[4, 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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shard_cnt=3,
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shard_id=1,
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max_id=10)
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op.run(scope, place)
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self.assertEqual(out.rows(), [1, 4, 7, 10])
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self.assertEqual(out.height(), 10)
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self.assertEqual(out.get_tensor().shape(), [4, 784])
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hist, prob = output_hist(np.array(out.get_tensor()))
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self.assertTrue(
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np.allclose(
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hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
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if __name__ == "__main__":
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unittest.main()
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