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244 lines
9.2 KiB
244 lines
9.2 KiB
/* 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/operators/lookup_table_op.h"
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#include <memory>
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#include "paddle/fluid/framework/no_need_buffer_vars_inference.h"
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#include "paddle/fluid/framework/op_version_registry.h"
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#include "paddle/fluid/framework/var_type_inference.h"
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#include "paddle/fluid/platform/bfloat16.h"
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namespace paddle {
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namespace operators {
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class LookupTableOp : 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("W"), "Input", "W", "LookupTable");
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OP_INOUT_CHECK(ctx->HasInput("Ids"), "Input", "Ids", "LookupTable");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "LookupTable");
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auto table_dims = ctx->GetInputDim("W");
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auto ids_dims = ctx->GetInputDim("Ids");
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int ids_rank = ids_dims.size();
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VLOG(5) << "ids rank is " << ids_rank << std::endl;
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PADDLE_ENFORCE_EQ(
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table_dims.size(), 2,
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platform::errors::InvalidArgument(
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"ShapeError: The dimensions of the 'lookup table' must be 2. "
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"But received lookup table's dimensions = %d, "
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"lookup table's shape = [%s].",
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table_dims.size(), table_dims));
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PADDLE_ENFORCE_EQ(
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ids_dims[ids_rank - 1], 1,
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platform::errors::InvalidArgument(
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"ShapeError: The last dimensions of the 'Ids' tensor must be 1. "
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"But received Ids's last dimensions = %d, Ids's shape = [%s].",
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ids_dims[ids_rank - 1], ids_dims));
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auto output_dims =
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framework::vectorize(framework::slice_ddim(ids_dims, 0, ids_rank - 1));
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output_dims.push_back(table_dims[1]);
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ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
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if (ctx->GetOutputsVarType("Out")[0] ==
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framework::proto::VarType::LOD_TENSOR) {
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ctx->ShareLoD("Ids", /*->*/ "Out");
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}
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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 = OperatorWithKernel::IndicateVarDataType(ctx, "W");
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return framework::OpKernelType(data_type, ctx.device_context());
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}
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};
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class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("W",
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"(Tensor) The input represents embedding tensors, "
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"which is a learnable parameter.");
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AddInput("Ids",
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"An input with type int64 "
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"contains the ids to be looked up in W. "
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"The last dimension size must be 1.");
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AddOutput("Out", "The lookup results, which have the same type as W.");
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AddAttr<bool>("is_sparse",
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"(boolean, default false) "
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"Sparse update.")
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.SetDefault(false);
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AddAttr<bool>("is_distributed",
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"(boolean, default false) distributed lookup table.")
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.SetDefault(false);
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AddAttr<int64_t>("padding_idx",
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"(int64, default -1) "
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"If the value is -1, it makes no effect to lookup. "
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"Otherwise the given value indicates padding the output "
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"with zeros whenever lookup encounters it in Ids.")
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.SetDefault(kNoPadding);
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// for parameter training config
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AddAttr<bool>("remote_prefetch",
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"pull sparse params from parameters, this can only be used "
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"in distributed training")
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.SetDefault(false);
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AddAttr<std::string>("entry_config",
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"embedding sparse feature entry config, "
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" probability entry / counting "
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" this can only be used in distributed training"
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"entry")
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.SetDefault("");
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AddAttr<bool>("is_test",
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"(bool, default false) Set to true for inference only, false "
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"for training.")
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.SetDefault(false);
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AddAttr<std::string>("entry",
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"(std::string, default "
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") for entry attribute.")
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.SetDefault("none");
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AddAttr<std::vector<std::string>>(
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"table_names",
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"(string vector, the split table names that will be fetched from "
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"parameter server)"
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"in the order of input variables for mapping")
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.SetDefault({});
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AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
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AddAttr<bool>("grad_inplace",
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"(boolean, default false) "
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"If the grad op reuse the input's variable.")
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.SetDefault(false);
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AddAttr<std::vector<std::string>>(
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"epmap",
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"(string vector, default 127.0.0.1:6164)"
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"Server endpoints in the order of input variables for mapping")
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.SetDefault({});
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AddAttr<std::vector<int64_t>>("height_sections",
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"Height for each output SelectedRows.")
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.SetDefault(std::vector<int64_t>({}));
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AddComment(R"DOC(
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Lookup Table Operator.
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This operator is used to perform lookups on the parameter W,
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then concatenated into a dense tensor.
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The input Ids can carry the LoD (Level of Details) information,
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or not. And the output only shares the LoD information with input Ids.
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)DOC");
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(LookupTableGradOpNoBufferVarsInferer, "W");
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template <typename T>
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class LookupTableGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("lookup_table_grad");
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op->SetInput("W", this->Input("W"));
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op->SetInput("Ids", this->Input("Ids"));
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("W"), this->InputGrad("W"));
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op->SetAttrMap(this->Attrs());
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}
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};
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class LookupTableOpGrad : 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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auto table_dims = ctx->GetInputDim("W");
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ctx->SetOutputDim(framework::GradVarName("W"), table_dims);
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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 = OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Out"));
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return framework::OpKernelType(data_type, ctx.device_context());
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}
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};
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class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
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public:
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void operator()(framework::InferVarTypeContext* ctx) const override {
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auto out_var_name = framework::GradVarName("W");
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auto attr = ctx->GetAttr("is_sparse");
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bool is_sparse = BOOST_GET(bool, attr);
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if (is_sparse) {
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VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
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<< " is set to SelectedRows";
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ctx->SetOutputType(out_var_name,
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framework::proto::VarType::SELECTED_ROWS);
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} else {
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VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
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<< " is set to LoDTensor";
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ctx->SetOutputType(out_var_name, framework::proto::VarType::LOD_TENSOR);
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}
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ctx->SetOutputDataType(out_var_name, ctx->GetInputDataType("W"));
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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(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
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ops::LookupTableGradOpMaker<paddle::framework::OpDesc>,
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ops::LookupTableGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(lookup_table_grad, ops::LookupTableOpGrad,
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ops::LookupTableGradOpNoBufferVarsInferer,
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ops::LookupTableOpGradVarTypeInference);
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REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>,
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ops::LookupTableKernel<double>,
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ops::LookupTableKernel<int8_t>,
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ops::LookupTableKernel<paddle::platform::bfloat16>);
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REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>,
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ops::LookupTableGradKernel<double>,
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ops::LookupTableGradKernel<paddle::platform::bfloat16>);
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/* ========================== register checkpoint ===========================*/
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REGISTER_OP_VERSION(lookup_table)
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.AddCheckpoint(
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R"ROC(
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Upgrade lookup_table add 1 attribute [entry_config].
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)ROC",
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paddle::framework::compatible::OpVersionDesc().NewAttr(
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"entry_config",
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"(std::string) embedding sparse feature entry config.", ""));
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/* ========================================================================== */
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