/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/collective/global_gather_op.h" namespace paddle { namespace operators { class GlobalGatherOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "GlobalGather"); OP_INOUT_CHECK( ctx->HasInput("local_count"), "Input", "local_count", "GlobalGather"); OP_INOUT_CHECK( ctx->HasInput("global_count"), "Input", "global_count", "GlobalGather"); OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "GlobalGather"); int ring_id = ctx->Attrs().Get("ring_id"); PADDLE_ENFORCE_GE( ring_id, 0, platform::errors::InvalidArgument( "The ring_id (%d) for global gather op must be non-negative.", ring_id)); auto input_dims = ctx->GetInputDim("X"); auto ndim_input = input_dims.size(); // dim check PADDLE_ENFORCE_EQ(ndim_input, 2, platform::errors::InvalidArgument( "The input tensor's dimension must be 2. " "But received input's dimension = %d.", ndim_input)); framework::DDim out_dims = phi::make_ddim({-1, -1}); ctx->SetOutputDim("Out", out_dims); } protected: phi::KernelKey GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace()); } }; class GlobalGatherOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() { AddInput("X", "(Tensor) tensor send."); AddInput("local_count", "(Tensor) Tensor which has n_expert * world_size elements that " "indicates" "how many data needed to be received from each expert."); AddInput("global_count", "(Tensor) Tensor which has n_expert * world_size elements that " "indicates" "how many data needed to be sent to each expert."); AddOutput("Out", "(Tensor) the result of global_gather."); AddAttr("ring_id", "(int default 0) nccl communication ring id.") .SetDefault(0); AddAttr( "use_calc_stream", "(bool default false) eject CUDA operations to calculation stream.") .SetDefault(false); AddComment(R"DOC( Global Gather Operator Gather data in X to n_expert * world_size exeperts according to local_count and receive tensors from n_expert * world_size experts according to global_count. )DOC"); } }; template class GlobalGatherOpGradMaker : public framework::SingleGradOpMaker { public: using framework::SingleGradOpMaker::SingleGradOpMaker; protected: void Apply(GradOpPtr retv) const override { retv->SetType("global_scatter"); retv->SetInput("X", this->OutputGrad("Out")); retv->SetInput("local_count", this->Input("local_count")); retv->SetInput("global_count", this->Input("global_count")); retv->SetOutput("Out", this->InputGrad("X")); retv->SetAttrMap(this->Attrs()); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; namespace plat = paddle::platform; REGISTER_OPERATOR(global_gather, ops::GlobalGatherOp, ops::GlobalGatherOpMaker, ops::GlobalGatherOpGradMaker, ops::GlobalGatherOpGradMaker) REGISTER_OP_CPU_KERNEL(global_gather, ops::GlobalGatherOpCPUKernel, ops::GlobalGatherOpCPUKernel, ops::GlobalGatherOpCPUKernel, ops::GlobalGatherOpCPUKernel, ops::GlobalGatherOpCPUKernel);