supports distributed classification (#18690)
* supports distributed classification training * update API.spec * fix evenly division in python3 * change "index_range" to "index_num" in shard_index operator test=document_preview test=developDDDivano-patch-1
Yi Liu
6 years ago
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GitHub
parent
3429e65aa8
commit
157211c4e1
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// Copyright (c) 2019 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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#include "paddle/fluid/operators/shard_index_op.h"
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namespace paddle {
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namespace operators {
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class ShardIndexOp : 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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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of ShardIndexOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of ShardIndexOp should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_GE(x_dims.size(), 2,
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"Rank of Input(X) should be at least 2.");
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if (ctx->IsRuntime() || x_dims[x_dims.size() - 1] > 0) {
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PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U,
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"Last dimension of Input(X) should be 1.");
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}
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ctx->SetOutputDim("Out", x_dims);
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ctx->ShareLoD("X", /* --> */ "Out");
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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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return framework::OpKernelType(ctx.Input<framework::Tensor>("X")->type(),
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ctx.device_context());
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}
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};
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class ShardIndexOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(LoDTensor, LoDTensor<int|int64>) Input variable. Each value "
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"of X is an index.");
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AddOutput(
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"Out",
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"(Tensor, Tensor<int|int64>) Output tensor with same shape as X. "
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"The tensor consists of sharding representations of values in X.");
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AddAttr<int>("index_num",
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"A positive integer to specify the range of the input X.");
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AddAttr<int>("nshards",
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"A positive integer to specify the number of shards.");
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AddAttr<int>("shard_id", "The current shard id");
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AddAttr<int>("ignore_value", "An ingeter value out of sharded range")
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.SetDefault(-1);
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AddComment(R"DOC(
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This layer creates the sharded index for input. This layers is used in
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model- and data- parallel mixed training generally, in which the index
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data (usually the label) should be recaculated in each trainer according
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to
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.. math::
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assert index_num % nshards == 0
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shard_size = index_num / nshards
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y = x % shard_size if x / shard_size == shard_id else ignore_value
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We take the distributed one-hot representation to show what this layer is
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used for. The distributed one-hot representation is seperated into multiple
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shards, and each shard is filling zeros except the one with the index
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inside. In order to create these sharded representation in each trainer,
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the original index should be recalculated (i.e. sharded) before.
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Examples:
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X is a Tensor of integer values:
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X.shape = [4, 1]
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X.data = [[1], [6], [12], [19]]
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suppose index_num = 20 and nshards = 2, then we get shard_size = 10
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if shard_id == 0, we get the Out:
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Out.shape = [4, 1]
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Out.data = [[1], [6], [-1], [-1]]
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if shard_id == 1, we get the Out:
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Out.shape = [4, 1]
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Out.data = [[-1], [-1], [2], [9]]
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the default `ignore_value` -1 is used in this example.
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)DOC");
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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_OP_WITHOUT_GRADIENT(shard_index, ops::ShardIndexOp,
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ops::ShardIndexOpMaker);
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REGISTER_OP_CPU_KERNEL(shard_index, ops::ShardIndexCPUKernel<int>,
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ops::ShardIndexCPUKernel<int64_t>);
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@ -0,0 +1,77 @@
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// Copyright (c) 2019 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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#include "paddle/fluid/operators/shard_index_op.h"
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#include "paddle/fluid/platform/cuda_primitives.h"
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#include "paddle/fluid/platform/gpu_info.h"
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namespace paddle {
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namespace operators {
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using platform::PADDLE_CUDA_NUM_THREADS;
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template <typename T>
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__global__ void ShardIndexInner(const T* in_data, T* out_data,
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const int64_t numel, const int index_num,
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const int nshards, const int shard_id,
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const int ignore_value) {
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int shard_size = index_num / nshards;
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < numel) {
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assert(in_data[idx] >= 0 && in_data[idx] < index_num);
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if (in_data[idx] / shard_size == shard_id) {
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out_data[idx] = in_data[idx] % shard_size;
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} else {
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out_data[idx] = ignore_value;
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}
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}
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}
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using LoDTensor = framework::LoDTensor;
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template <typename T>
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class ShardIndexCUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<LoDTensor>("X");
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auto* out = context.Output<LoDTensor>("Out");
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int index_num = context.Attr<int>("index_num");
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int nshards = context.Attr<int>("nshards");
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int shard_id = context.Attr<int>("shard_id");
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int ignore_value = context.Attr<int>("ignore_value");
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PADDLE_ENFORCE_GT(index_num, 0);
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PADDLE_ENFORCE_GT(nshards, 0);
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PADDLE_ENFORCE(shard_id >= 0 && shard_id < nshards,
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"shard_id(%d) is not in range [0, %d)", shard_id, nshards);
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out->Resize(in->dims());
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out->set_lod(in->lod());
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auto* in_data = in->data<T>();
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auto* out_data = out->mutable_data<T>(context.GetPlace());
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int64_t numel = in->numel();
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auto stream =
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context.template device_context<platform::CUDADeviceContext>().stream();
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ShardIndexInner<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) /
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PADDLE_CUDA_NUM_THREADS,
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PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
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in_data, out_data, numel, index_num, nshards, shard_id, ignore_value);
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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_OP_CUDA_KERNEL(shard_index, ops::ShardIndexCUDAKernel<int>,
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ops::ShardIndexCUDAKernel<int64_t>);
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@ -0,0 +1,58 @@
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// Copyright (c) 2019 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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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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template <typename T>
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class ShardIndexCPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<LoDTensor>("X");
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auto* out = context.Output<LoDTensor>("Out");
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int index_num = context.Attr<int>("index_num");
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int nshards = context.Attr<int>("nshards");
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int shard_id = context.Attr<int>("shard_id");
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int ignore_value = context.Attr<int>("ignore_value");
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PADDLE_ENFORCE_GT(index_num, 0);
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PADDLE_ENFORCE_GT(nshards, 0);
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PADDLE_ENFORCE(shard_id >= 0 && shard_id < nshards,
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"shard_id(%d) is not in range [0, %d)", shard_id, nshards);
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int shard_size = index_num / nshards;
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out->Resize(in->dims());
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out->set_lod(in->lod());
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auto* in_data = in->data<T>();
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auto* out_data = out->mutable_data<T>(context.GetPlace());
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int64_t numel = in->numel();
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for (int64_t i = 0; i < numel; ++i) {
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PADDLE_ENFORCE(in_data[i] >= 0 && in_data[i] < index_num,
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"Input index(%d) is out of range [0,%d)", in_data[i],
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index_num);
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if (in_data[i] / shard_size == shard_id) {
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out_data[i] = in_data[i] % shard_size;
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} else {
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out_data[i] = ignore_value;
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,85 @@
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# Copyright (c) 2019 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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from __future__ import print_function
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import unittest
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import numpy as np
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import math
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from op_test import OpTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.fluid.framework as framework
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from paddle.fluid.framework import Program, program_guard
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def common_setup(self, index_num, nshards, shard_id, ignore_value):
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self.op_type = 'shard_index'
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x_lod = [[i for i in range(10)]]
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N = sum(x_lod[0])
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x = [np.random.randint(0, index_num - 1) for i in range(N)]
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x = np.array(x).astype('int32').reshape([N, 1])
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shard_size = index_num // nshards
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out = np.zeros(shape=x.shape).astype('int32')
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for i in range(N):
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if x[i] // shard_size == shard_id:
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out[i] = x[i] % shard_size
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else:
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out[i] = ignore_value
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {
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'index_num': index_num,
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'nshards': nshards,
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'shard_id': shard_id,
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'ignore_value': ignore_value
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}
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self.outputs = {'Out': (out, x_lod)}
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class TestShardIndexShardId0Op(OpTest):
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def setUp(self):
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common_setup(self, 20, 2, 0, -1)
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def test_check_output(self):
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self.check_output()
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class TestShardIndexShardId1Op(OpTest):
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def setUp(self):
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common_setup(self, 20, 2, 1, -1)
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def test_check_output(self):
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self.check_output()
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class TestShardIndexIgnoreValueOp(OpTest):
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def setUp(self):
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common_setup(self, 20, 2, 0, -2)
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def test_check_output(self):
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self.check_output()
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class TestShardIndexNotEvenlyDividedOp(OpTest):
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def setUp(self):
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common_setup(self, 15, 2, 1, -1)
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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unittest.main()
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