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119 lines
4.3 KiB
119 lines
4.3 KiB
/* 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/multinomial_op.h"
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/generator.h"
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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/common_infer_shape_functions.h"
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namespace paddle {
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namespace operators {
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class MultinomialOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "A tensor contains probabilities of categories");
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AddOutput("Out", "The output tensor of multinomial op");
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AddAttr<int>("num_samples", "number of the generated samples")
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.SetDefault(1);
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AddAttr<bool>("replacement", "can a category be sampled more than once")
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.SetDefault(false);
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AddComment(R"DOC(
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This OP returns a Tensor filled with the sampled categoris according to Multinomial probabilities.
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Out ~ Multinomial(X)
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)DOC");
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}
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};
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class MultinomialOp : 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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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Multinomial");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Multinomial");
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auto x_dim = ctx->GetInputDim("X");
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int64_t x_rank = x_dim.size();
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PADDLE_ENFORCE_GT(x_rank, 0,
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platform::errors::InvalidArgument(
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"The number of dimensions of the input probability "
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"distribution should be > 0, but got %d.",
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x_rank));
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PADDLE_ENFORCE_LE(x_rank, 2,
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platform::errors::InvalidArgument(
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"The number of dimensions of the input probability "
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"distribution should be <= 2, but got %d.",
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x_rank));
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std::vector<int64_t> out_dims(x_rank);
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for (int64_t i = 0; i < x_rank - 1; i++) {
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out_dims[i] = x_dim[i];
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}
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int64_t num_samples = ctx->Attrs().Get<int>("num_samples");
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PADDLE_ENFORCE_GT(
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num_samples, 0,
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platform::errors::InvalidArgument(
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"The number of samples should be > 0, but got %d.", num_samples));
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out_dims[x_rank - 1] = num_samples;
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ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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}
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};
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template <typename T>
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class MultinomialOpKernel<platform::CPUDeviceContext, T>
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: 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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const auto x = ctx.Input<framework::Tensor>("X");
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auto out = ctx.Output<framework::Tensor>("Out");
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const int64_t num_samples = ctx.Attr<int>("num_samples");
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const bool replacement = ctx.Attr<bool>("replacement");
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auto *in_data = x->data<T>();
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int64_t *out_data = out->mutable_data<int64_t>(ctx.GetPlace());
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auto in_dims = x->dims();
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int64_t in_rank = in_dims.size();
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const int64_t num_categories = in_dims[in_rank - 1];
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const int64_t num_distributions = in_rank > 1 ? in_dims[in_rank - 2] : 1;
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MultinomialFunctor<T>(out_data, in_data, num_samples, replacement,
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num_categories, num_distributions);
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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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namespace plat = paddle::platform;
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REGISTER_OPERATOR(
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multinomial, ops::MultinomialOp, ops::MultinomialOpMaker,
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paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OP_CPU_KERNEL(
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multinomial, ops::MultinomialOpKernel<plat::CPUDeviceContext, float>,
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ops::MultinomialOpKernel<plat::CPUDeviceContext, double>);
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